The Relationship Between Earnings Management and Inventory Management in Emerging Markets: The Case of Moroccan Companies Listed on the Casablanca Stock Exchange

Abstract

This study examines how inventory management influences accrual-based earnings management in emerging markets. Specifically, it analyzes the effect of three inventory performance indicators—Inventory Turnover Ratio (ITR), Inventory Service Level (ISL), and Inventory Coverage Rate (ICR)—on discretionary accruals (AVDA), measured as the absolute value of discretionary accruals estimated using the Kothari model. The Moroccan context offers a relevant setting due to the scarcity of research linking operational supply-chain metrics to financial reporting practices in emerging economies. The empirical analysis relies on 321 firm-year observations from 41 non-financial companies listed on the Casablanca Stock Exchange between 2016 and 2023. A panel fixed-effects regression model is employed to assess the association between inventory indicators and AVDA. Results show a significant negative relationship between ISL and discretionary accruals, while ITR and ICR exhibit no significant effects. These findings indicate that higher inventory service reliability is associated with reduced earnings management, highlighting the governance role of inventory-related SCM practices in Morocco.

1. Introduction

Over the last few decades, firms have increasingly recognized SCM as a strategic driver of competitiveness and corporate performance rather than a purely operational function. SCM has evolved into a strategic discipline that shapes corporate value creation by coordinating physical, financial, and informational flows across the production and distribution network (Mentzer, 2004; Shi & Yu, 2013). For leading global firms such as Amazon and Apple, SCM excellence has become a pillar of sustainable competitive advantage.

At its core, SCM aims to optimize the triptych of physical, financial, and information flows—from procurement and production management to inventory control and final product delivery. Among these dimensions, inventory management plays a pivotal role in balancing cost efficiency, customer satisfaction, and production continuity (Demeter & Matyusz, 2011; Moser et al., 2017). Effective inventory management contributes not only to operational performance but also to the reliability of financial statements, as inventory valuation directly affects reported earnings (Cook et al., 2012; Galdi & Johnson, 2021).

Recent research emphasizes that inventory decisions can serve dual objectives: optimizing operations and managing reported financial outcomes (Cohen & Zarowin, 2010; Comiran & Siriviriyakul, 2022). Firms under pressure to meet earnings targets may engage in real or accrual-based earnings management by manipulating production volumes, altering valuation methods, or deferring write-downs (Dechow et al., 2012; Healy & Wahlen, 1999). These practices influence both cost of goods sold and ending inventory, thereby affecting profitability metrics.

From a theoretical standpoint, agency theory suggests that managers, driven by personal incentives, may exploit discretion in financial reporting to meet short-term goals (Meckling & Jensen, 1976, as expanded by Crocker & Slemrod, 2007). Within this framework, inventory management offers a convenient tool for such manipulation due to its accounting flexibility and sensitivity to estimation assumptions (Baldenius & Reichelstein, 2000). Institutional theory, by contrast, posits that firms in emerging markets adopt modern SCM and transparency practices to gain legitimacy and align with international expectations (Breuer, 2021; Chen et al., 2010).

In Morocco, structural features of the corporate environment—such as high ownership concentration, weaker investor protection frameworks, and heterogeneous corporate governance practices—create conditions under which inventory-related information plays a disproportionate role in shaping financial reporting quality (Bellari & El Amraoui, 2025b). In highly concentrated ownership structures, majority shareholders often exert significant influence over managerial decisions, reducing the effectiveness of external monitoring mechanisms and increasing the potential for earnings manipulation (Bellari, 2024a). Similarly, weaker legal and regulatory protections for minority investors limit shareholders’ ability to challenge opportunistic reporting behaviors or demand more transparent disclosure (Bellari & El Amraoui, 2025a). Under these circumstances, managers operate with greater discretion, and accounting estimates linked to inventory—such as provisions for obsolescence, valuation methods, and timing of write-downs—become key channels through which earnings can be adjusted with minimal external scrutiny. Because inventory represents a substantial asset for many Moroccan listed firms, especially in manufacturing, distribution, and retail, the quality of inventory data directly influences the reliability of accrual estimates and thus the credibility of reported earnings (Bellari, 2024b).

Furthermore, the limited digitalization of inventory systems in Moroccan firms amplifies the information asymmetry between managers, auditors, and external stakeholders (Bellari & El Amraoui, 2025b). Many companies still rely on fragmented or semi-manual tracking systems, which increase the likelihood of discrepancies in physical counts, valuation errors, and delays in updating stock movements. These technological limitations reduce the accuracy and traceability of inventory data, creating opportunities for managerial discretion in estimating cost allocations, adjusting provisions, or manipulating the timing of inventory recognition (Lanier et al., 2019; Suhanda & Firmansyah, 2020). In emerging-market settings where internal controls evolve gradually and digital adoption progresses unevenly, the reliability of operational data becomes a central determinant of accrual accuracy. Consequently, inventory management quality—measured through turnover, service level, or cost recovery indicators—acts as an informal governance mechanism: firms with disciplined and transparent inventory processes tend to produce more reliable accounting numbers and engage less in accrual-based earnings management (Autore et al., 2024; Bafghi & Aldin, 2024). This dynamic is distinct from developed markets, where stronger regulatory institutions, advanced digital infrastructures, and more standardized reporting procedures mitigate the role of inventory transparency in constraining managerial opportunism (Li, 2019; Cook et al., 2012).

Despite the growing literature on SCM performance and earnings management, prior studies have rarely examined how specific inventory performance indicators—especially Inventory Service Level (ISL)—affect the magnitude of discretionary accruals. Most research focuses either on operational efficiency or on accounting manipulation, but not on their intersection. In particular, the role of ISL remains unexplored, even though it captures inventory reliability and process discipline more directly than traditional measures such as turnover or coverage. This study contributes to filling this gap by providing the first empirical evidence from Morocco, an emerging market characterized by high ownership concentration, heterogeneous digitalization, and varying internal control quality. By integrating SCM indicators into the earnings management literature, this study introduces an operational determinant of financial reporting quality and highlights ISL as a potential governance mechanism in environments with weaker monitoring structures.

This study therefore aims to investigate the relationship between SCM—specifically inventory performance indicators—and earnings management among Moroccan listed companies. By analyzing the extent to which inventory discipline influences discretionary accruals, this research provides empirical evidence on whether efficient SCM practices mitigate managerial incentives or opportunities for earnings manipulation. The findings contribute to bridging operational and accounting perspectives, demonstrating that effective supply chain and inventory control mechanisms enhance not only operational efficiency but also financial transparency. From a practical standpoint, this study offers implications for managers, auditors, and policymakers, emphasizing that strengthening SCM capabilities—particularly through digitalization and structured inventory processes—can serve as a lever for governance, reliability, and sustainable performance in Morocco’s emerging market context.

2. Literature Review

2.1. Inventory Management and Financial Reporting Quality

In the last two decades, supply chain management has evolved from an operational support function into a core strategic discipline driving competitive advantage and financial performance (Mentzer, 2004; Shi & Yu, 2013). SCM encompasses the integrated coordination of physical, informational, and financial flows across organizations. At the heart of this system lies inventory management, a critical mechanism for aligning supply with demand and ensuring operational continuity.

Beyond its operational importance, inventory performance is increasingly recognized for its financial implications. Efficient management of inventory not only optimizes costs and reduces working capital but may also limit managerial incentives or opportunities for earnings manipulation. This opens the door to a growing stream of interdisciplinary research investigating the connection between physical flow optimization and earnings management (EM).

A new stream of empirical literature has emerged examining how operational efficiency affects managerial discretion in earnings reporting. Lanier et al. (2019) demonstrate that firms with greater supply chain power tend to engage more in real earnings management (e.g., overproduction) to meet financial targets. Comiran and Siriviriyakul (2022) show that excessive inventory accumulation may signal future write-downs and earnings smoothing behaviors. Some authors suggest that supply chain digitalization enhances transparency and discourages EM (Autore et al., 2024; Bafghi & Aldin, 2024). Suhanda and Firmansyah (2020) explore SCM as a mediating factor reducing the volatility and opacity associated with EM. These studies converge on the idea that firms with robust SCM, particularly in inventory practices, exhibit less need or ability to engage in earnings manipulation.

From a theoretical standpoint, inventory management interacts with earnings management through both agency theory and information asymmetry perspectives. Baldenius and Reichelstein (2000) show that historical cost-based residual income measures can yield optimal incentives for managers to manage inventory efficiently, especially under the LIFO valuation method. However, when compensation is tied to short-term accounting results, managers may intentionally manipulate production or inventory levels to influence reported earnings (Wu & Lai, 2022). This behavior is consistent with the agency problem, wherein information asymmetry allows managers to act in their own interest rather than that of shareholders.

Moreover, as Friebel and Guriev (2005) highlight, such opportunistic manipulation within internal hierarchies can distort incentives, encourage rent-seeking behavior, and reduce overall efficiency. Crocker and Slemrod (2007) further note that because earnings-based contracts are imperfectly informative, some level of earnings management might emerge as an equilibrium response to balance contractual efficiency and truthful reporting. In this context, inventory decisions play a dual role: they are both operational levers for cost control and potential instruments for accounting discretion.

While much of the literature highlights the connection between inventory practices and real earnings management, inventory management also has a direct and theoretically grounded influence on accrual-based earnings management. Accruals related to inventory—such as allowances for obsolescence, adjustments to net realizable value, and cost allocation between ending inventory and cost of goods sold—require managerial judgment and rely heavily on the quality of underlying inventory data. When inventory systems are inefficient, fragmented, or poorly monitored, managers face greater discretion in estimating these accruals, creating opportunities to manipulate reported earnings through valuation adjustments rather than through real operating actions.

Efficient inventory management constrains this discretion in several ways. First, firms with high-quality inventory processes (e.g., accurate records, timely updates, strong service levels, low discrepancies) generate more reliable operational data, which reduces estimation uncertainty for accruals such as write-downs, provisions, and overhead allocations. Second, transparent inventory flows strengthen the audit trail available to internal and external auditors, making it more difficult for managers to justify aggressive or opportunistic accrual adjustments. Third, robust inventory discipline limits the buildup of obsolete or slow-moving items, reducing situations in which managers might strategically time impairments to smooth earnings. In this sense, inventory performance acts as a governance mechanism that improves accrual accuracy and reduces the potential for discretionary financial reporting.

Overall, the literature suggests a complex yet significant relationship between inventory efficiency and earnings quality. Efficient inventory management improves operational transparency and reduces the scope for accrual-based manipulation by strengthening data accuracy, reducing estimation uncertainty, and limiting opportunities for discretionary valuation. Conversely, poor or opportunistic inventory practices can conceal inefficiencies and distort financial outcomes. This dual nature justifies examining inventory metrics—such as turnover ratio, service level, and coverage rate—not only as operational indicators but also as potential determinants of accrual-based earnings management, particularly in emerging market contexts where monitoring mechanisms and digital infrastructures remain uneven.

2.2. Research Hypotheses

This study aims to investigate whether firms’ inventory management practices—viewed as key indicators of supply chain efficiency—are associated with lower levels of earnings management. Efficient inventory control is central to financial transparency be-cause inventory valuation directly affects cost of goods sold and, consequently, reported earnings. Poor or manipulative inventory practices may distort accruals and misrepresent financial performance. Drawing on prior literature in accounting, operations, and supply chain management, the following hypotheses are proposed.

H1: 

A higher inventory turnover ratio (ITR) is negatively associated with discretionary accruals.

The Inventory Turnover Ratio (ITR) captures how quickly a firm converts inventory into sales. A high ITR indicates efficient procurement planning, rapid stock rotation, and minimal obsolete or slow-moving items. These operational conditions directly reduce the areas of accounting that depend heavily on managerial judgment—such as provisions for inventory obsolescence, net realizable value (NRV) assessments, and discretionary allocations of production overheads.

Prior research demonstrates that inventory accounts are among the most sensitive to accrual manipulation. Thomas and Zhang (2002) show that inventory variations are strongly associated with discretionary accruals because managers can exploit valuation flexibility to influence reported earnings. When turnover is high, however, inventory remains close to physical flows and actual demand, limiting managers’ ability to justify subjective adjustments (Demeter & Matyusz, 2011).

From an agency theory perspective (Crocker & Slemrod, 2007), managers have incentives to manipulate earnings when operations are inefficient or costs rise. However, firms with lean and transparent inventory systems face fewer opportunities for such manipulation because inventory-related estimates—such as obsolescence reserves or production cost allocations—become more closely tied to real transactions.

Similarly, Dichev et al. (2013) have previously argued that rapid inventory rotation improves the objectivity of valuation estimates and reduces the informational gaps that allow discretionary manipulation. More recent evidence supports this view: Baboukardos and Rimmel (2016) find that transparent and efficient inventory practices improve the reliability of accounting estimates by reducing uncertainty around obsolescence and NRV testing. From a supply chain perspective, Lanier et al. (2019) and Anderson and Dekker (2009) show that operational efficiency and integrated SCM systems enhance the accuracy of cost allocations and reduce overall accounting discretion.

Taken together, this literature suggests that firms with high inventory turnover face fewer estimation-based choices—precisely the mechanisms through which accrual-based earnings management typically occurs. Efficient turnover reduces slow-moving stock and valuation uncertainty, thereby limiting the opportunities for managers to manipulate accruals.

H2: 

A higher inventory service level (ISL) is negatively associated with discretionary accruals.

The Inventory Service Level (ISL) reflects a firm’s ability to satisfy customer demand without delays or stockouts. High service levels typically arise from accurate forecasting, synchronized purchasing and production activities, robust internal information systems, and real-time tracking of inventory movements (Cho et al., 2012). These operational features improve the precision of inventory records and reduce discrepancies between physical flows and accounting balances.

Because inventory valuation is one of the most estimate-dependent areas of financial reporting, better operational accuracy directly limits the scope for managerial discretion. High ISL reduces the need for subjective adjustments related to net realizable value testing, shrinkage estimates, overhead capitalization, and work-in-progress valuation. In other words, when inventory operations function reliably, managers have fewer opportunities to manipulate accruals through judgment-based estimates (Lanier et al., 2019; Suhanda & Firmansyah, 2020).

From a transparency and legitimacy perspective (Breuer, 2021; Chen et al., 2010), firms with efficient service levels tend to maintain better data integration and performance monitoring systems, reducing the informational asymmetry between management and stakeholders.

Prior research supports this mechanism. Hribar et al. (2014) show that real-time operational information improves the accuracy of financial reporting and reduces discretionary accruals by narrowing the gap between operational data and accounting estimates. Similarly, Bushman et al. (2004) highlight that strong internal information systems constrain managerial discretion by reducing informational asymmetry and increasing the verifiability of reported figures. Field-evidence from Dichev et al. (2013) reinforce that firms with disciplined operational controls exhibit lower levels of accrual manipulation because managers cannot easily justify subjective accounting adjustments. In the supply chain domain, Gao et al. (2022) find that supply chain integration and visibility enhance transparency and reduce earnings management, demonstrating that operational coordination has direct implications for accounting quality.

Thus, these insights indicate that high ISL serves as a non-financial indicator of internal control strength and reporting reliability. By enhancing data accuracy and reducing estimation uncertainty, superior service levels limit managers’ ability to rely on discretionary accruals to alter financial results.

H3: 

A higher inventory coverage rate (ICR) is positively associated with discretionary accruals.

The inventory coverage rate (ICR) represents the average number of days or months that inventory remains in stock before being sold. It reflects how long capital is tied up in inventory and how efficiently a company balances supply and demand (Cho et al., 2012).

A high ICR generally indicates slow-moving or excess inventory, which increases managerial discretion in valuation because these items require more subjective assessments. This longer holding periods heighten uncertainty related to potential obsolescence, NRV measurement, the timing of write-downs, and the estimation of impairment provisions—precisely the areas where accrual-based earnings management typically occurs.

Because inventory valuation relies extensively on judgment, high ICR creates conditions that facilitate opportunistic financial reporting. Managers can delay or accelerate write-downs, overstate the recoverable value of outdated goods, or adjust provisions to meet short-term earnings benchmarks. These accounting choices do not require changes in real operations; they are purely accrual-based and allow managers to influence earnings without altering underlying economic performance.

Prior studies support this mechanism. Thomas and Zhang (2002) identify inventory as one of the most frequent channels for accrual manipulation, given the flexibility embedded in valuation rules. Galdi and Pereira (2007) further show that firms with slow-moving inventories face greater opportunities to manipulate accruals because the valuation of aged stock inherently requires discretionary judgments. Empirical evidence from Cook et al. (2012) demonstrates that managers actively use inventory valuation discretion—including NRV assessments and impairment timing—to manage reported earnings.

Similarly, Galdi and Johnson (2021) highlight that inventory accounting choices and valuation methods (e.g., FIFO vs. weighted average) can significantly influence reported profits and accruals, particularly in manufacturing-intensive industries. Longer inventory holding periods amplify estimation uncertainty related to obsolescence and net realizable value, thereby increasing opportunities for discretionary accrual manipulation.

From an agency and cost-of-capital perspective, such behavior may provide short-term earnings benefits but deteriorates long-term performance, as excess inventory leads to higher holding costs and potential impairments (Friebel & Guriev, 2005; Wu & Lai, 2022).

More broadly, Srinidhi et al. (2011) highlight that, assets requiring high levels of estimation provide the greatest scope for accrual-based manipulation, reinforcing the idea that slow-moving inventory invites subjective accounting adjustments.

Taken together, these studies propound that a high ICR increases managerial latitude in valuation decisions, thereby expanding the opportunities for accrual-based earnings management. In contrast to firms with efficient inventory turnover or high service levels, firms with prolonged inventory holding periods face greater uncertainty and heavier reliance on accounting judgment, making accrual manipulation more likely.

To isolate the effect of inventory metrics on earnings management, the study includes a set of parsimonious control variables that are consistently used in the earnings management literature and theoretically justified in the Moroccan context. Given the relatively small sample size of Moroccan listed firms, the inclusion of too many controls could reduce statistical power or introduce multicollinearity. Therefore, we restrict our controls to a well-established core set capturing firm size, industry characteristics, and financial performance—three dimensions repeatedly shown to influence discretionary accruals.

Firm size is one of the most widely used controls in earnings management research because it captures several economic and governance attributes that directly affect managerial discretion (Siregar & Utama, 2008; Swastika, 2013; Bellari, 2024a). Larger firms typically benefit from more sophisticated internal controls, higher analyst coverage, and stronger audit quality, all of which reduce opportunities for aggressive accrual manipulation. At the same time, larger firms often have more complex operations and diversified activities, which may increase estimation uncertainty and create room for discretionary accounting choices. This dual effect makes firm size a fundamental determinant of earnings quality. Controlling for size ensures that the effect attributed to inventory performance is not confounded with differences in monitoring, internal control structures, or information environments that naturally vary across small and large firms.

Industry sector is included because inventory cycles, valuation practices, and cost structures differ substantially across industries, particularly between manufacturing, retail, and service-oriented firms (Breuer, 2021; Moser et al., 2017; Bellari, 2024a). These differences affect both operational drivers of inventory metrics and the accounting rules governing cost allocations or write-downs. For instance, industries with long production cycles (e.g., chemicals, metallurgy) face higher uncertainty in estimating inventory obsolescence, while fast-moving retail industries tend to exhibit frequent stock rotations and more standardized inventory valuation practices. These heterogeneities directly influence the baseline level of accruals associated with inventory. Including industry effects helps ensure that the relationship between inventory performance and discretionary accruals is not driven by structural sectoral differences. This is especially important in the Moroccan setting, where industrial composition varies significantly across listed firms.

ROA is incorporated following Kothari et al. (2005), who demonstrate that discretionary accrual models can be biased if performance differences across firms are not controlled for. Earnings management is often correlated with profitability: high-performing firms may have fewer incentives to inflate earnings, whereas poorly performing firms may resort to accrual manipulation to meet benchmarks or avoid reporting losses. Controlling for ROA helps mitigate performance-related bias in the estimation of discretionary accruals and ensures that variations in accruals are not simply reflecting differences in underlying economic performance. This adjustment is essential in emerging markets, where profitability volatility is higher and managerial incentives tied to performance are more pronounced.

Taken together, these three variables—firm size, industry sector, and profitability—represent the most theoretically relevant and empirically validated determinants of earnings management across the accounting literature. Given the limited number of Moroccan listed firms and the risk of overfitting, the model is intentionally restricted to this concise set of controls to maintain statistical robustness while capturing the principal sources of variation in discretionary accruals.

2.3. Conceptual Model

To empirically examine the impact of supply chain efficiency on earnings management, the following conceptual model (Figure 1) illustrates the hypothesized relationships between key inventory performance indicators and discretionary accruals, with firm size, industry sector, and return on assets included as control variables.

3. Methodology

3.1. Research Sample

To examine the relationship between inventory management and earnings management, this study employs a panel data approach, using an unbalanced panel of Moroccan non-financial listed firms from 2016 to 2023. Panel models allow us to control for unobservable firm-specific characteristics that may influence both inventory performance and earnings management.

All financial and accounting data used in this study were obtained from the official Casablanca Stock Exchange database, supplemented with firm annual reports, audited financial statements, and publicly available corporate publications. Because the objective of this research is to analyze the relationship between inventory performance and discretionary accruals, we employ a multi-stage filtering process to ensure sample consistency across sectors and years.

First, we removed all financial institutions (banks, insurance companies, and investment firms), as their balance sheet structures, regulatory environments, and financial reporting frameworks differ substantially from non-financial sectors and are not comparable in terms of inventory practices.

Second, to avoid distortion of inventory ratios, we excluded service firms or sectors with negligible or zero inventories, such as, electronic payment services, software & IT services and consulting. Only firms with a material proportion of inventories in their operating cycle were retained. This ensures that inventory turnover, coverage rate, and service level remain meaningful and comparable performance indicators.

Third, we addressed concerns related to data availability and consistency. Firms with fewer than eight consecutive years of observations, as well as newly listed or delisted firms during the period 2016–2023, were removed. This criterion guarantees the construction of a balanced econometric panel and reduces bias due to missing data.

Applying these criteria resulted in a final fixed sample of 41 companies over the study period.

Table 1. Summary of the Selected Sample.

Selection Criteria	Number
Companies listed on the Casablanca Stock Exchange	77
- Companies operating in the financial sector	−20
- Companies excluded due to insufficient data	−11
- Companies excluded due to absence of inventories	−5
= Final Sample	41

Source: Compiled by the authors based on Casablanca Stock Exchange data, 2023.

summarizes the selection process used to construct the study sample.

To ensure transparency regarding the sectoral composition of the firms included in the analysis,

Table 2. Industry Breakdown of the Final Sample (41 Firms).

Sector Code (Sect)	Industry Sector	Number of Firms	%
1	Food and Beverage industry	7	17.07%
2	Construction Materials (Cement, etc.)	6	14.63%
3	Real Estate & Hospitality Services	5	12.2%
4	Retail & Distribution	4	9.76%
5	Energy & Petrol-related Industries	4	9.76%
6	Mining & Extraction	3	7.32%
7	IT Hardware & Distribution	2	4.88%
8	Metals & Metallurgy industry	2	4.88%
9	Pharmaceuticals & Healthcare Products	2	4.88%
10	Transport & Logistics	2	4.88%
11	Automotive	2	4.88%
12	Paper, Packaging & Printing	1	2.44%
13	Telecommunications	1	2.44%

Source: Authors’ compilation based on Casablanca Stock Exchange data, 2023.

provides a detailed breakdown of the 41 companies across the 13 industry sectors represented in the Casablanca Stock Exchange.

As shown in Table 2, the sample covers 13 distinct inventory-intensive sectors, including chemicals, food processing, retail, manufacturing, and logistics. This categorization aligns with the sectoral classification used in the Casablanca Stock Exchange database (SECT variable).

3.2. Measurement of Discretionary Accruals

To identify the indicator for earnings management, we follow a three-step approach. First, we estimate total accruals. Then, we calculate normal (or non-discretionary) accruals using the model proposed by Kothari et al. (2005). Finally, we derive abnormal (or discretionary) accruals, which are the central focus of the study.

3.2.1. First Step: Determination of Total Accruals

Several researchers (Cohen & Zarowin, 2010; Kim et al., 2012; Bellari, 2024a) highlight that the subtractive method is the most appropriate approach for estimating total accruals. This method consists of taking the difference between net income and operating cash flows. While net income is easily identifiable in a company’s financial statements, operating cash flows are more difficult to determine. In Morocco, companies are not required to disclose a cash flow statement, as this report is not standardized under the General Accounting Standardization Code (CGNC). The financing statement only indicates the net cash position without explicitly presenting or detailing operating cash flows. Consequently, only companies reporting under IFRS publish a cash flow statement, in line with IAS 7.

To estimate operating cash flows, we calculate the difference between the firm’s self-financing capacity and the change in its working capital requirement (WCR). Ideally, the WCR should exclude cash flows related to investment and financing activities to isolate only the operational component. However, distinguishing between operating and non-operating elements within the WCR is difficult, as such details can only be extracted from the general ledger, which is not publicly accessible.

Given these constraints, we adopt the approach proposed by Francis et al. (2005), which defines operating cash flows as the net difference between self-financing capacity and the overall working capital requirement.

3.2.2. Second Step: Estimation of Normal Accruals

The model proposed by Kothari et al. (2005) estimates non-discretionary accruals using the following regression equation:

NA_it/ASSET_it−1 = α (1/ASSETi_t−1) + β₁ (ΔREV_it − ΔAR_it/ASSET_it−1) + β₂ (FIXASSET_it/ASSET_it−1) + β₃ ROA_it + ε_it

For firm i at period t:

NA	Normal accruals
ASSETit−1	Total assets at the beginning of the period
ΔREVit − ΔARit	Difference between the change in revenue and the change in accounts receivable
FIXASSETit	Total gross fixed assets for the period
ROAit	Return on assets
α; β1; β2; β3	Coefficients to be estimated
εit	Error term

In addition to fixed assets and sales growth adjusted for accounts receivable, the model by Kothari and al. stands out by incorporating a variable that captures the firm’s economic performance (Kothari et al., 2005). Indeed, this model integrates the adjustments suggested by previous models (Dechow et al., 2012; Jones, 1991), while making a key contribution by introducing a variable that reflects the firm’s economic performance, the profitability of its economic assets.

3.2.3. Third Step: Evaluation of Abnormal Accruals

In this final step, abnormal accruals are calculated as the difference between total accruals, obtained by subtracting operating cash flows from net income, and the values estimated using the Kothari et al. (2005) model. This approach can be expressed as follows:

AVDA_it/ASSET_it−1 = TA/ASSET_it−1 − [α (1/ASSET_it−1) + β₁ (ΔREV_it − ΔAR_it/ASSET_it−1) + β₂ (FIXASSETit/ASSET_it−1) + β₃ (ROA_it) + ε_it]

For firm i at period t:

AVDAit	Absolute value of discretionary accruals
α, β1, β2, and β3	Coefficients to be estimated

The variable AVDA is measured in absolute terms to account for both upward and downward earnings management (i.e., negative and positive values). A high absolute value thus indicates a strong reliance on earnings management practices, whether to inflate or reduce earnings. According to DeFond & Zhang, the choice between absolute values and signed accruals depends on the study’s objective (DeFond & Zhang, 2014). If the direction of earnings management is not the primary focus, the absolute value is the most appropriate approach.

In this study, our goal is not to determine whether earnings management is upward or downward but rather to assess whether companies that effectively manage their SCM’s physical flows (inventories) are less likely to practice earnings, regardless of its direction. For this reason, we use the absolute value of abnormal accruals. This methodological choice aligns with numerous previous studies (Barth et al., 2001; Francis et al., 2005; Tehranian et al., 2006; Hribar & Nichols, 2007; Cohen & Zarowin, 2010; Chen et al., 2010; DeFond & Zhang, 2014; Li, 2019; Galdi & Johnson, 2021; Bafghi & Aldin, 2024; Bellari, 2024a; Bellari & El Amraoui, 2025b).

However, to ensure that our findings do not depend on this choice, the analysis is complemented by an alternative specification using signed discretionary accruals (SDA), confirming that the results do not hinge on the directionality of accruals.

3.3. Presentation of the Different Variables

Table 3. Operationalization of study variables.

Variables	Attribute	Indicator	Measure
Dependent	Discretionary Accruals	AVDA	Absolute value of discretionary accruals normalized by total assets.
Explanatory	Inventory Turnover Ratio	ITR	Cost of Goods Sold/Average Inventory
	Inventory Service Level	ISL	Number of Orders Fulfilled on Time/Total Orders
	Inventory Coverage Rate	ICR	(Average inventory × 360 days)/Average demand
Control	Firm Size	SIZE	Natural logarithm of total assets.
	Industry Sector	SECT	Polychotomous variable taking values from 1 to 13, representing the various industry sectors of the companies in the sample.
	Return on Assets	ROA	Ratio of operating income to total assets.

Source: Compiled by the authors.

summarizes all the variables making up our regression model for examining the relationship between CSR and earnings management:

We have to point out that ISL variable is constructed from operational performance indicators disclosed by firms in their annual reports, CSR reports, investor presentations, or logistics performance dashboards. For firms that do not publish the exact count of on-time orders, ISL is proxied using available customer service indicators such as delivery punctuality rate, order-fulfillment rate, or on-time delivery percentage. These proxies are widely used in SCM research as operational equivalents of service level. All ISL data used in this study were retrieved manually from the public disclosures of listed firms.

Furthermore, following prior earnings management studies (Kothari et al., 2005; Hribar & Nichols, 2007), all continuous variables were winsorized at the 1st and 99th percentiles to reduce the influence of extreme values, including the initially observed AVDA maximum of 1.55.

In summary, we examine the relationship between the physical flows of Supply Chain Management (SCM), specifically inventory management, and earnings management in Moroccan companies listed on the Casablanca Stock Exchange. Our analysis is based on a linear fixed-effects panel regression model, formulated as follows:

AVDAit = α + β₁ SIZE_it + β₂ SECT_it + β₃ ROA_it + β₄ ITR_it + β₅ ISL_it + β₆ ICR_it + μ_i + λ_t + ε_it

where μ_i captures firm-specific fixed effects and λ_t captures time fixed effects, allowing us to control for unobserved heterogeneity and common macroeconomic shocks.

4. Results

First, we present the descriptive statistics and the correlation coefficients for the research variables, followed by the outcomes of various specification tests.

4.1. Descriptive Statistics

Table 4. Descriptive Statistics.

Variable	Min	Median	Mean	Max	SD	Skewness	Kurtosis
AVDA	0.00	0.10	0.181	0.96	0.2036	1.864	3.463
SIZE	17.77	21.08	21.047	24.45	1.5570	−0.020	−0.693
SECT	1.00	5.00	5.602	13.00	3.330	0.390	−1.149
ROA	−0.19	0.06	0.055	0.25	0.0729	−1.823	4.956
ITR	0.24	4.44	8.983	56.98	12.23	3.911	16.158
ISL	0.91	0.97	0.969	0.99	0.0186	−1.017	0.032
ICR	13.27	59.00	72.762	303.73	56.192	2.398	6.264

Source: Results of authors’ estimations using panel data from Casablanca Stock Exchange, 2023.

presents the descriptive statistics for all variables included in the study. The dependent variable, discretionary accruals (AVDA), has a mean of 0.181 and a median of 0.10, indicating that earnings management remains moderate among Moroccan listed firms. The distribution is right-skewed (skewness = 1.864), suggesting that while most firms exhibit relatively low earnings manipulation, a subset displays higher discretionary adjustments. The kurtosis value (3.463) reflects a moderately peaked distribution.

Regarding firm size (SIZE), the descriptive indicators show limited dispersion (SD = 1.557) and a nearly symmetric distribution (skewness = −0.020), suggesting homogeneity in firm scale within the sample.

For the industry sector (SECT) variable, the mean (5.602) and median (5.00) reveal a fairly balanced distribution across the 13 sectors represented in the Casablanca Stock Exchange classification. Low skewness (0.390) confirms the absence of sectoral concentration.

The return on assets (ROA) shows a mean profitability of 5.5%, with a distribution that is slightly negatively skewed (−1.823), reflecting a number of firms with lower profitability relative to the median (0.06). The kurtosis value (4.956) indicates greater tail heaviness, consistent with the presence of firms experiencing unusually weak performance.

For the supply chain indicators, the Inventory Turnover Ratio (ITR) presents substantial dispersion (SD = 12.23), with a right-skewed distribution (3.911). This pattern signals notable differences in operational efficiency across firms, particularly between fast-moving and slow-moving industries.

The Inventory Service Level (ISL) exhibits a highly concentrated distribution, with values clustered near the upper end of the scale (mean = 0.969, median = 0.97). The slight negative skewness (−1.017) reflects a small number of firms with service levels marginally below the sample average.

Finally, the Inventory Coverage Rate (ICR) shows wide variability (SD = 56.192) and positive skewness (2.398), indicating that some firms maintain inventory levels significantly above typical operational needs.

Overall, the descriptive statistics highlight the heterogeneity of operational and financial characteristics across Moroccan listed firms, particularly in inventory dynamics and profitability. This variability reinforces the relevance of analyzing the link between inventory performance and earnings management within a fixed-effects panel framework.

4.2. Correlation

Table 5. Correlations.

	AVDA	SIZE	SECT	ROA	ITR	ISL	ICR
AVDA	1.0000
SIZE	−0.4325 ***	1.0000
SECT	0.1169 *	−0.1828 ***	1.0000
ROA	0.0999	−0.0425	0.1400 *	1.0000
ITR	−0.1280 *	0.2127 ***	0.0102	0.1519 **	1.0000
ISL	−0.4513 ***	0.5332 ***	−0.2647 ***	−0.0766	−0.0179	1.0000
ICR	−0.0582	0.1844 ***	−0.2952 ***	−0.2734 ***	−0.2829 ***	0.1198 *	1.000

(***, ** and * denote significance levels at p < 0.001, p < 0.01, and p < 0.05, respectively). Source: Results of authors’ estimations using panel data from Casablanca Stock Exchange, 2023.

presents the correlation matrix, which reveals a significant negative correlation (r = −0.4513, p < 0.001) between one explanatory variable (ISL) and the dependent variable (discretionary accruals).

The firm size, as control variable, is significantly and inversely correlated with earnings management (r = −0.4325, p < 0.001), indicating that larger firms tend to engage less in earnings management. Concerning the industry sector, we can observe a very weak significant correlation with accruals (r = −0.1169, p < 0.05).

Moreover, the matrix indicates that larger firms are more actively involved in Inventory Service Level, as evidenced by a significant positive correlation (r = 0.5332, p < 0.001) between firm size (SIZE) and ISL.

It is important to note that neither the asset profitability of the firms nor the two others explanatory variables exert a significant influence on the practice of earnings management.

4.3. Specification Tests

To examine the validity and robustness of our regression model, we conducted a series of specification tests. First, to ensure that the model is statistically sound, we performed a Fisher test. The F-statistic, which measures the strength of the test for individual effects, is equal to 9.1747. A higher F-value indicates greater variation. With a p-value of 2.2 × 10⁻¹⁶—well below the conventional 0.05 threshold—we reject the null hypothesis that no significant individual effects exist.

Thus, the Fisher test for our regression model indicates that individual effects are significant (p-value < 0.05), implying significant heterogeneity in our data and suggesting that a fixed effects model is more appropriate than a random effects model for our study. It should be noted that a more in-depth analysis using a Hausman test is recommended to draw definitive conclusions.

In our model, the chi-squared statistic—which measures the strength of the test comparing fixed effects and random effects models—stands at approximately 154,578 according to the Hausman test. A high value indicates a significant difference between the two models. Moreover, the p-value is extremely low (2.2 × 10⁻¹⁶), far below the 0.05 threshold. These results confirm that the random effects model is inconsistent compared to the fixed effects model (p-value < 0.05). Therefore, the fixed effects model is preferred for our analysis, as it provides more reliable and consistent estimates of the independent variables’ coefficients.

It is worth noting that the fixed effects model explains approximately 54.7% of the variance in the dependent variable (R-Squared = 0.547) with significant coefficients for the explanatory variables and one control variable. In fact, the results obtained via RStudio 4.4.3 suggest that inventory turnover ratio (ITR), inventory management level (ISL), inventory coverage rate (ICR) and firm size (SIZE) have significant negative influence on earnings management.

Furthermore, to assess multicollinearity, we calculated the Variance Inflation Factor (VIF) and Tolerance. Since the VIF values for all six variables are below 10 and the tolerance values are well above 0.1, multicollinearity does not pose a major problem for our model. Consequently, we can be reasonably confident that the coefficient estimates are not unduly affected by collinearity among the independent variables.

However, our model exhibits two main issues: heteroscedasticity and autocorrelation. The Breusch-Pagan test yields a p-value of 4.942 × 10⁻¹⁰, which is far below the 0.05 threshold, leading us to reject the null hypothesis of homoscedasticity. In other words, there is sufficient evidence to conclude that the model’s residuals exhibit significant heteroscedasticity (i.e., the residuals from the regression of earnings management determinants do not have constant variance).

Additionally, the Breusch-Godfrey test for autocorrelation reveals a high LM statistic (61.41) with 1 degree of freedom and a p-value of 4.619 × 10⁻¹⁵. These results suggest that the residuals in our model exhibit first-order autocorrelation, meaning that the errors are not independent, which could indicate a misspecification of the model or the omission of relevant explanatory variables.

To address these issues, we applied a correction using Driscoll and Kraay’s (1998) Standard Errors to the fixed effects model. This approach adjusts the standard errors and test statistics under conditions of heteroscedasticity and autocorrelation. Consequently, the results from Driscoll–Kraay’s test (see

Table 6. Summary of Driscoll-Kraay’s Test for AVDA (Correction for Standard Errors).

Variable	Estimate	Standard Error	T Value	Pr(>|t|)
SIZE	−0.0674198	0.0327140	−2.0609	0.04023 *
SECT	0.0571601	0.0085266	6.7038	1.111 × 10−10 **
ROA	0.0638263	0.0385018	1.6578	0.09848
ITR	−0.0326354	0.0359702	−0.9073	0.36503
ISL	−24.3295005	2.9508824	−8.2448	6.406 × 10−15 **
ICR	−0.0026062	0.0032081	−0.8124	0.41726

(**, and * denote significance levels at p < 0.001, and p < 0.05, respectively). Source: Results of authors’ estimations using panel data from Casablanca Stock Exchange, 2023.

) indicate that inventory management level (ISL), firm size (SIZE), and industry sector (SECT) have significant negative influences on earnings management, with respective p-values of 6.406 × 10⁻¹⁵, 0.04023 and 1.111 × 10⁻¹⁰ (all below 0.05).

Thus, the results adjusted for heteroscedasticity and autocorrelation confirm the robustness of the initial estimations. Although the significance of certain variables diminished, the findings indicate that inventory management level (ISL), firm size (SIZE), and industry sector (SECT) remain significantly and negatively associated with earnings management. The subsequent section provides a detailed discussion of these results in relation to the existing body of literature.

As an additional robustness check, we re-estimated the fixed-effects model using signed discretionary accruals (SDA) instead of the absolute measure. The SDA regression results (see

Table 7. Summary of Driscoll-Kraay’s Test for SDA (Correction for Standard Errors).

Variable	Estimate	Standard Error	t Value	Pr(>|t|)
SIZE	−0.0709812	0.0341124	−2.0794	0.0387 *
SECT	0.0558935	0.0087641	6.3741	2.21 × 10−10 **
ROA	0.0583447	0.0379183	1.5390	0.1248
ITR	−0.0304881	0.0361058	−0.8444	0.3993
ISL	−23.9126034	2.9814477	−8.0203	1.12 × 10−14 **
ICR	−0.0024119	0.0032552	−0.7408	0.4594

(**, and * denote significance levels at p < 0.001, and p < 0.05, respectively). Source: Results of authors’ estimations using panel data from Casablanca Stock Exchange, 2023.

) exhibit the same sign and significance pattern as the ABSDA model. In particular, ISL remains strongly negative and statistically significant (p < 0.001), while SIZE and SECT also retain their significance levels. The coefficients of ITR, ICR, and ROA behave consistently across both specifications. This confirms that our findings do not depend on the direction of accruals but reflect the magnitude of discretionary reporting behavior.

5. Discussion

The empirical results provide valuable insights into the dynamics linking inventory management practices to earnings management in Moroccan listed firms. Overall, the evidence supports the proposition that operational transparency and discipline in managing physical flows can help reduce earnings manipulation.

However, the contrasting results across the three inventory indicators—ITR, ISL, and ICR—show that these mechanisms do not operate uniformly. Each indicator captures a different aspect of inventory performance, which explains why only H2 is supported while H1 and H3 are not.

First, the rejection of H1 (A higher inventory turnover ratio is negatively associated with discretionary accruals) is theoretically understandable. In the literature review, prior studies argue that slow-moving inventory increases managers’ discretion in estimating write-downs, obsolescence provisions, and NRV adjustments (Thomas & Zhang, 2002; Galdi & Pereira, 2007; Cook et al., 2012). Under this reasoning, higher turnover should reduce valuation uncertainty and therefore limit accrual manipulation. This logic is theoretically sound.

However, ITR measures the speed of inventory flow, not the valuation subjectivity embedded in inventory accounting. The distinction is crucial. A high ITR may indicate strong sales performance, demand intensity, or industry-specific production cycles, but it does not necessarily improve the transparency, accuracy, or auditability of the valuation processes through which accrual-based earnings management occurs.

Thus, even firms with high turnover may retain substantial discretion in estimating obsolescence, timing impairment losses, allocating production overheads, and adjusting NRV assumptions.

Several SCM studies confirm that turnover ratios reflect operational activity rather than informational governance (Lanier et al., 2019). Recent evidence also shows that ITR is a weak predictor of discretionary accruals because it does not regulate how managers apply valuation rules (Suhanda & Firmansyah, 2020).

Consequently, the theoretical mechanism derived from the literature focuses on valuation discretion, whereas the empirical variable (ITR) used in this study captures operational velocity. These two constructs—though related—are not equivalent. This conceptual gap explains why the theoretical expectation behind H1 is valid in principle but does not translate empirically in the Moroccan context, leading to the rejection of the hypothesis.

Second, the strong support for H2 (A higher inventory service level is negatively associated with discretionary accruals) is fully aligned with theory. The Inventory Service Level (ISL) directly reflects the firm’s planning accuracy, demand forecasting quality, supply chain coordination, and process discipline. High ISL indicates that inventory movements are predictable, well-monitored, and supported by reliable data. Under information asymmetry theory, stronger operational transparency reduces the margins of discretion available to managers when determining inventory-related accruals. Moreover, institutional theory suggests that firms that achieve high service levels adopt more formalized internal processes, making opportunistic manipulation more detectable and less likely. Thus, ISL acts as an internal governance mechanism, explaining why it has the strongest negative and significant association with discretionary accruals and remains robust across ABSDA and SDA specifications. This matches findings from digital SCM studies showing that traceability, accuracy, and data integrity reduce earnings manipulation (Autore et al., 2024; Bafghi & Aldin, 2024).

Third, the rejection of H3 (A higher inventory coverage rate is positively associated with discretionary accruals) results from the fact that ICR captures duration, not valuation transparency. Although prior studies emphasize that slow-moving or obsolete inventories increase managerial discretion (Thomas & Zhang, 2002; Galdi & Pereira, 2007; Cook et al., 2012; Galdi & Johnson, 2021), these studies focus on valuation discretion, such as NRV estimation, impairment timing, or obsolescence provisioning. These mechanisms rely on subjective accounting judgments, not on the physical duration of inventory. In contrast, the ICR used in this study is an operational intensity indicator—it measures the number of days inventory can cover expected demand, but it does not capture valuation uncertainty, deterioration, or obsolescence risks. As such, ICR does not directly map onto the accrual-based discretion channels documented in the literature. This distinction explains why, despite prior evidence that slow-moving inventory often facilitates accrual manipulation, the ICR variable in our model does not exhibit a significant association with discretionary accruals: it reflects stock duration, not the valuation subjectivity that drives accrual-based earnings management.

Furthermore, while longer holding periods may theoretically amplify valuation uncertainty (Galdi & Johnson, 2021), Moroccan listed firms operate in heterogeneous sectors—such as construction materials, mining, and heavy industry—where extended inventory cycles are operationally justified rather than indicative of obsolescence risk. In such contexts, the mere length of inventory coverage does not create additional flexibility for manipulating accruals. This aligns with the idea that accrual manipulation arises when assets require subjective estimation (Srinidhi et al., 2011), but this subjectivity is not captured by ICR. Therefore, the rejection of H3 is fully consistent with theory: ICR measures quantity and duration, not valuation discretion, and thus cannot constrain or enable accrual-based manipulation.

Taken together, these results confirm that only inventory indicators that improve informational quality and operational transparency (such as ISL) can reduce discretionary accruals, whereas indicators reflecting volume (ITR) or duration (ICR) do not exert similar governance pressure. This theoretical coherence strengthens the confidence in our findings and clarifies why only H2 is validated

Furthermore, regarding the control variables, we find that both firm size and industry sector are significant. First, firm size (SIZE) shows a significant negative association with discretionary accruals, reflecting the stronger governance infrastructure of larger Moroccan companies. These firms face heightened regulatory scrutiny from the Moroccan Capital Market Authority (AMMC), higher institutional ownership, and more sophisticated internal controls, often supported by IFRS reporting and Big Four auditors. Their organizational complexity and reputational exposure reduce opportunities for opportunistic accounting. This aligns with the broader literature linking firm size to reduced informational asymmetry and decreased earnings manipulation in emerging markets (Swastika, 2013; Ali et al., 2015; Bellari, 2024a).

Second, Industry sector (SECT) is positively and significantly related to accrual-based earnings management, highlighting sectoral disparities in incentives and opportunities for discretion. This result is also strongly context-dependent.

To begin with, Morocco’s sectoral composition is highly heterogeneous, ranging from capital-intensive industries (cement, metals, mining) to cyclical sectors (construction, real estate, tourism) and margin-sensitive sectors (distribution, food-processing). In cyclical or volatile industries, firms face greater pressure to smooth earnings, especially when profitability fluctuates with commodity prices, tourism flows, or public infrastructure investment cycles.

Moreover, several Moroccan sectors operate under weaker operational digitalization, particularly in inventory-heavy industries such as construction materials, metals, and traditional manufacturing. Limited digitization of SCM processes increases opacity in inventory valuation and reduces traceability, giving managers greater freedom to apply discretionary accruals.

In addition, competitive pressure varies substantially across Moroccan sectors. For example, real estate and hospitality firms face volatile revenues and strong incentives to report stable results to lenders; manufacturing and mining firms deal with fluctuating commodity prices, encouraging earnings smoothing; and distribution and retail sectors face tighter competition, pushing managers to maintain stable margins during demand shocks.

Finally, institutional theory suggests that industries with more mature governance norms (e.g., telecommunications, major industrial exporters) experience stronger pressure to comply with transparency expectations. Conversely, emerging or fragmented industries may apply accounting discretion more frequently due to weaker monitoring.

In sum, the findings highlight the unique role of Inventory Service Level as an operational-financial bridge, capturing not only logistical performance but also the behavioral discipline associated with reduced earnings manipulation. The partial validation of the hypotheses also suggests that not all inventory metrics carry equal informational value.

6. Conclusions

The primary motivation for this study stems from the scarcity of academic literature examining the relationship between and earnings management in the specific context of emerging markets, particularly in Morocco. In response, this research empirically investigates how inventory performance indicators can influence financial reporting and earnings management behavior in Moroccan companies listed on the Casablanca Stock Ex-change.

The findings reveal that among the three inventory indicators, only the inventory service level (ISL)—a proxy for supply chain reliability, demand-planning accuracy, and internal coordination—is significantly and negatively associated with discretionary accruals. This suggests that it is not the speed of inventory rotation (ITR) nor the duration of stock holding (ICR) that constrains accrual-based manipulation, but rather the reliability and transparency generated by effective service-level performance. This nuance has important implications. It indicates that operational tools improving forecasting accuracy, on-time delivery, and digital traceability may naturally reduce managers’ room to engage in subjective valuation choices. Additionally, the significance of firm size and industry sector confirms that contextual conditions—such as governance maturity, market visibility, and sectoral volatility—remain major determinants of earnings management behavior in Morocco.

This study enriches the literature on earnings management by integrating supply chain management (SCM) variables into the analysis—an approach rarely explored in the context of emerging economies. The results suggest that improving operational excellence not only enhances competitiveness but also contributes to better financial transparency and corporate governance through a reduction in earnings manipulation.

For practitioners, this means that financial transparency cannot be improved through inventory volume metrics alone; rather, integration between SCM and accounting functions is essential, particularly around service-level monitoring and digital traceability. For auditors, the results highlight that firms with low ISL or operating in volatile or weakly digitized sectors require heightened attention to inventory valuation and accrual estimates. For regulators and policymakers, these findings reinforce the need to promote supply chain digitalization and sector-specific governance improvements, since operational reliability—not merely operational speed—emerges as a critical antecedent of high-quality financial reporting in the Moroccan context.

Looking forward, this study opens several promising avenues for future research. First, the use of real earnings management proxies could provide a more comprehensive understanding of how firms balance accrual-based and operational manipulation strategies, especially given the difficulty of capturing such data in emerging markets. Second, sector-specific analyses could illuminate how industry characteristics—such as demand volatility, capital intensity, or regulatory pressure—influence the SCM–earnings management nexus. Third, the potential nonlinearities and interaction effects between inventory indicators and firm-specific attributes (such as governance structures, ownership con-centration, or digitalization level) deserve closer examination to capture the nuanced dynamics of earnings management behavior.

Finally, adopting mixed-method research designs that combine econometric models with qualitative approaches (e.g., interviews with managers, case studies, or process tracing) could help uncover the underlying mechanisms linking SCM performance to financial reporting practices. Such approaches would allow researchers to validate quantitative findings and to explore the decision-making rationales that drive managers in emerging market firms to engage—or refrain from engaging—in earnings manipulation. Furthermore, while the present study relies on a parsimonious set of three firm-level control variables, future research could integrate additional governance or macroeconomic indicators to enrich the analysis and capture broader contextual dynamics.

In sum, this study not only broadens the scope of earnings management research by integrating supply chain perspectives but also provides a foundation for developing a more interdisciplinary understanding of corporate behavior in emerging markets, where the interplay between operational realities and financial strategies is both complex and underexplored.