The Impact of Asymmetric Cost Behavior and Its Reflection on Decision Quality

The aim of this study is to investigate the presence of asymmetric costing behavior (anti-sticky costing) in the Iraqi General Cement Company and examine its impact on strategic quality decisions. Data for the period from 2007 to 2021 were collected from the company and analyzed using a descriptive analysis method. The findings indicated a statistically significant link between associated costs and several financial performance metrics, such as return on assets (ROA), return on equity (ROE), and Tobin’s Q. However, no statistical evidence was identified to establish a relationship between debt coverage ratios and other examined variables. Among the recommendations of the study is that they suggest increasing operational efficiency to improve the profitability of the company also advise to pay close attention to the associated costs and continuing to work on reducing cost overruns and to take advantage of improved profitability. Furthermore, it is recommended to periodically perform the impact of investment decisions on the company’s finances to enhance overall performance.

Introduction

Cost conduct is an essential pillar in building and describing data derived from the control accounting gadget. It serves as an essential device for distinguishing relevant fees from irrelevant ones when making managerial choices. However, the traditional assumption of cost conduct linearity has faced complaints following the emergence of new research offering proof that expenses respond differently to changes in interest stages. This research has shown that certain fee items might also increase or lower at various prices with adjustments in interest stages, a phenomenon called uneven cost behavior (sticky prices).

Indeed, some costs show adjustment to changes in activity levels; They may not adjust immediately or adjust to increases or decreases in performance. This dynamic, known as asymmetric cost behavior or “sticky costs,” means that a cost rises or falls differently as activities change e.g., during periods of booming sales, companies may spend money more on things like labor and materials Because this behavior manifests itself in some “associated” costs—costs that continue to increase even as activity decreases—especially because of things like employee retention, contracts internal responsibilities, or because of employee reluctance to reduce capacity.

The concept of associated costs has important implications for short-term decision-making, budgeting, and business analysis. Recognizing the nonlinearity of cost behavior enables managers to accurately forecast costs, allocate resources, and analyze the impact of strategic decisions Nonlinear cost behavior suggests that traditional linear models do not fully capture the complexity of costs within an organization. As a result, improved models such as those introduced by Anderson and Lanen (2009), have been developed to provide a more nuanced understanding of how costs behave across applications. This model uses regression analyzes that consider the natural logarithm of the price coefficient and sales revenue, providing a more realistic proxy for analyzing price volatility relative to performance variables.

Study Methodology

Study Problem

Traditional cost behavior analysis models classify costs into fixed and variable categories, where variable costs are assumed to change in a linear fashion with activity level fluctuations within a defined capacity range, irrespective of whether the activity level increases or decreases. However, this classification conflicts with recent accounting studies that indicate costs respond asymmetrically to changes in activity levels, making information derived from traditional cost behavior models less accurate and potentially leading to flawed decisions. Therefore, the study problem can be articulated through the following questions:

1. Are there asymmetric (sticky-sliding) costs in the Iraqi Cement Company?

2. Do asymmetric costs impact the quality of strategic decisions?

Two sub-questions can be derived from these:

• Does sticky cost behavior affect the quality of strategic decisions in the Iraqi Cement Company?

• Does sliding cost behavior affect the quality of strategic decisions in the Iraqi Cement Company?

Importance of the Study

Scientific Importance

The scientific importance of this study lies in the novelty of the asymmetric cost behavior concept and the scarcity of Arabic research on the subject. This study aims to address and analyze this concept, examining its impact on strategic decision-making, which is essential to an entity’s future.

Practical Importance

The practical significance of this study stems from the importance of cost analysis and classification, as well as cost behavior examination, which affects financial statements and strategic decisions such as pricing and asset replacement. Understanding asymmetric cost behavior, especially sticky and sliding costs, could provide real value to decision-makers by enhancing the accuracy of strategic decisions.

Study Objectives

This study aims to achieve the following objectives:

1. To introduce the concept of asymmetric cost behavior, including sticky and sliding costs.

2. To analyze and classify costs as sticky and sliding in the Iraqi Cement Company.

3. To examine the impact of sticky costs on the quality of strategic decisions.

4. To examine the impact of sliding costs on the quality of strategic decisions.

Study Hypotheses

• Main Hypothesis 1: Asymmetric (sticky-sliding) costs exist in the Iraqi Cement Company.

• Main Hypothesis 2: There is a statistically significant relationship between asymmetric costs and the quality of strategic decisions, measured by financial performance indicators.

Theoretical Framework

Asymmetric Cost Behavior

Numerous studies have attempted to explain the asymmetry in cost behavior when activity levels increase versus when they decrease. The initial study in this area was conducted by Noreen and Soderstrom, who analyzed data from a sample of 100 hospitals in Washington State. Their findings indicated that indirect costs do not change proportionally with increases or decreases in activity levels; instead, they vary at different rates for certain types of indirect costs (Mghizet al., 2017, p. 10). Later, Anderson and Lanen (2009), expanded on this concept, providing a clearer definition of asymmetric cost behavior by examining the disparity between SG&A (Selling, General, and Administrative) costs and sales, coining the concept “ABJ,” which represents the initials of the three researchers. This study triggered further research across various countries, exploring modern interpretations of asymmetric cost behavior.

Subsequent studies revealed additional drivers behind asymmetric cost behavior, such as managerial empire-building, free cash flow (FCF) management, upper management aspirations, and executive rewards and incentives. The divergence between managerial and investor interests can also lead to asymmetric cost behavior, resulting in sticky or anti-sticky (sliding) costs (Ciftci & Salama, 2018).

Concept and Definition of Asymmetric Cost Behavior

Recent research in cost behavior has shown that costs do not change consistently with shifts in sales volume. It was found that costs are more responsive to sales increases than they are to decreases, indicating that, for the same level of change, costs do not decrease as sharply as they increase. In other words, costs respond asymmetrically to declines and increases in sales, challenging the conventional economic and accounting assumption of symmetric cost behavior. This finding contrasts with the traditional accounting theory’s linear model, as documented by recent studies showing that cost behavior does not align with changes in activity levels proportionately—a phenomenon termed “asymmetric cost behavior” (Rouxelinet al., 2018, p. 310).

Definition of Asymmetric Cost Behavior

Various definitions have been proposed for the term “asymmetric cost behavior,” which, according to the researcher’s knowledge, can be summarized as follows:

1. Asymmetric cost behavior results departments within an economic unit, as well as among core and support activities (Carrollet al., 2018).

2. Asymmetric cost behavior results from surrounding economic risks due to uncertain information on cash flows, which impacts the economic unit’s profits (Homburg & Nasev, 2009).

3. Asymmetric cost behavior is observed across economic units in different countries due to managerial perspectives, appointment systems, internal controls, and agency theory differences (Heet al., 2010).

4. This cost behavior varies when the economic unit’s management changes incentives and rewards granted to the administration (Wiersma, 2011).

5. Asymmetric costs arise due to deliberate and measured managerial decisions regarding resource utilization (Subramanian & Weidenmier, 2016).

6. These costs exhibit asymmetric behavior due to differences in unit size and variations in operating flexibility (Bosch & Blandón, 2011).

7. Cost behavior varies asymmetrically due to the full or partial alignment between production capacity and product demand, which influences cost behavior (Cannon, 2013).

8. Asymmetric costs result from the economic unit’s degree of governance over SG&A costs (Chenet al., 2012).

9. Cost behavior varies asymmetrically due to labor protection laws within a single country (Bankeret al., 2013).

The researcher defines asymmetric cost behavior as cost behavior that changes disproportionately with increases or decreases in activity levels due to internal or external factors within the economic unit, leading to this distinctive behavior.

Types of Costs Exhibiting Asymmetric Behavior

Both variable and fixed costs are influenced by activity levels, with the direction of change impacting costs differently depending on the nature of the activity change. These costs can be categorized as follows:

• Indirect Costs: Indirect costs were the first to exhibit asymmetric behavior, as demonstrated in a 1994 study showing that these costs do not change proportionally with increases or decreases in activity levels. This behavior has been observed in public agencies and profit-seeking companies (Ellisonet al., 2018).

• Marketing and Administrative Costs: Marketing and administrative costs are central to cost structures within economic units and serve as evidence of asymmetric cost behavior relative to revenue. Studies have shown that the percentage increase in these costs due to a rise in sales revenue is greater than the percentage decrease when sales revenue declines by an equivalent amount. Numerous studies have since explored this type of cost behavior (Bradbury & Scott, 2018).

• Cost of Goods Sold (COGS): Several studies have examined the relationship between sticky costs and the cost of goods sold, comparing revenues with the stickiness of marketing and administrative costs. Findings indicate that these costs do not exhibit sticky behavior with minor revenue changes; however, when revenue changes significantly, both marketing/administrative costs and COGS show sticky cost behavior (Kooet al., 2018).

• Operating Costs: Various studies on operating costs across countries have shown that operating costs exhibit shared characteristics, with operational costs being less sticky when data is aggregated over extended periods. Cost stickiness intensifies when a firm experiences significant revenue declines (Ellisonet al., 2018).

• Labor Costs: Multiple studies highlight a strong connection between labor costs and asymmetric behavior, especially in countries with high labor protection levels. These studies show a robust relationship between wages and asymmetric cost behavior, particularly in countries with strict worker protection laws (Kooet al., 2018, p. 155). Another study emphasized that a shortage of skilled labor increases sticky costs (Carrollet al., 2018, p. 11).

Rational Decisions

Origins and Concept of Decision-Making

The social school of thought, led by Herbert Simon, was the first to establish the foundations of decision-making. Simon’s 1946 book Administrative Behavior, which earned him a Nobel Prize in 1978, identified four stages in the decision-making process. However, this work was more directed at administrators than economists. Recognizing this limitation, Simon later published Les Organisations, focusing on decision-makers’ understanding of the limited rationality in their choices (Juillet/Août 2002). Simon argued that rationality in decision-making is limited due to the potential changes that may arise, as decision-making relies on various assessments. He contended that decision assistance might be a more fitting term than decision science since “facts discovered through models and tools remain bound to varying interpretations of systems or evaluation frameworks.” Therefore, decision science does not truly exist, though a science of decision support might be feasible (Boyeret al., 2000).

Asymmetric Cost Behavior and Its Relationship with Rational Decision-Making

Numerous studies have shown that costs arise from two types of resources used within an organization, which vary depending on management’s short-term decisions to adjust these resources:

1. Pre-committed Resources: These are fixed costs, established by the organization in advance of knowing the level of activity (such as building rent, depreciation of facilities, and machinery consumption).

2. Variable Resources: These costs fluctuate based on actual organizational needs and activity, such as direct and indirect material costs, which can be adjusted in the short term and are classified as variable costs (Heet al., 2010).

According to Subramaniam and Weidenmier, traditional cost behavior models suggest that pre-determined fixed costs are not directly related to actual levels of activity within a feasible range, while variable costs reflect the consumption of resources that adjust with the level of activity. Thus, resource consumption increases when activity rises and decreases to a lesser extent when activity falls, leading to “sticky costs” (Subramaniam & Weidenmier, 2003, p. 11). Conversely, when management opts to decrease resource usage with declining activity or refrains from increasing resources with rising activity, an inverse asymmetric cost behavior (anti-sticky costs) emerges (Weiss, 2017).

The researcher summarizes the relationship between managerial decisions and asymmetric cost behavior in Table I.

Table I. Relationship Between Managerial Decisions and Asymmetric Cost BehaviorSource: Prepared by the researcher

Situation	Activity level	Type of cost observed
Absence of rational decisions	Increase	Sticky costs
Absence of rational decisions	Decrease	Sticky costs
Presence of rational decisions	Increase	Anti-sticky costs
Presence of rational decisions	Decrease	Anti-sticky costs

Importance of Studying Asymmetric Cost Behavior and Its Relation to Rational Decision-Making

Managers need to understand cost behavior to make informed decisions regarding products, planning, control, and performance evaluation. A correct understanding of cost behavior is essential for managerial accounting, forming the foundation of rational administrative and investment decisions, along with decisions made by various stakeholders within economic units. Numerous managerial decisions, such as new product launches, production volumes, pricing, seasonal pricing, promotions, and routine choices, are influenced by cost behavior, ultimately impacting the continuity and growth of a company. Therefore, understanding and applying asymmetric cost behavior positively influences these decisions, making them more effective and beneficial for a company’s growth and competitive ability (Subramanian & Weidenmier, 2016).

Types of Cost Behavior and Their Role in Decision-Making

Managers consider costs related to resource retention, particularly labor, and compare them to costs associated with reducing unused labor or hiring and training new employees. Understanding how both variable and fixed costs change with activity levels helps managers decide whether to retain resources or reduce them in response to activity fluctuations. These costs are categorized as follows (Wiersma, 2011):

1. Fixed Costs:

• Committed Costs: These are costs incurred prior to or during production that remain unchanged with variations in activity, as they are pre-paid or committed.

• Discretionary Costs: Future costs that can be adjusted based on activity level changes.

2. Variable Costs:

• Symmetrical Variable Costs: These costs change proportionally with production, typically including direct production costs.

• Asymmetrical Variable Costs: These costs, often sticky or anti-sticky, change at different rates with increases or decreases in activity.

Research Methodology

The study aims to investigate the relationship between asymmetric cost behavior and managerial decisions, measured by financial performance indicators (Return on Assets, Return on Equity, and Tobin’s Q) in the company under study. Descriptive and inferential analysis was applied to determine whether there is a correlation between variables, with multiple linear regression employed as the primary analytical technique. For data collection in the practical aspect, the researcher utilized financial statements of the target companies, organized in Excel, followed by analysis and hypothesis testing in Eviews 12. For the theoretical component, relevant books, internal and external scientific articles, and peer-reviewed journals were referenced.

To test the second main hypothesis and its subdivisions, (1) to (3) were applied as follows:

(1) $$\begin{array}{r}RO{A}_{t.i}={\beta }_0+{\beta }_{1}SC{H}_{t.i}+{\beta }_{2}SC{I}_{t.i}+{\epsilon }_{t.i}\end{array}$$

(2) $$\begin{array}{r}RO{E}_{t.i}={\beta }_0+{\beta }_{1}SC{H}_{t.i}+{\beta }_{2}SC{I}_{t.i}+{\epsilon }_{t.i}\end{array}$$

(3) $$\begin{array}{r}Tobins{Q}_{t.i}={\beta }_0+{\beta }_{1}SC{H}_{t.i}+{\beta }_{2}SC{I}_{t.i}+{\epsilon }_{t.i}\end{array}$$

where

ROA – return on Assets, calculated as Net Profit before Taxes/Total Assets

ROE – return on Equity, calculated as Net Profit before Taxes/Shareholders’ Equity

TobinsQ – Tobin’s Q, calculated as Market Value/Book Value

SCH – level of asymmetric cost behavior on the upside, measured by the negative coefficient (β₂) in the ABJ model

SCI – level of asymmetric cost behavior on the downside, measured by the positive coefficient (β₂) in the ABJ model

ε – error term

In this study, the independent variable is asymmetric cost behavior (ACB), assessed through the model developed by Anderson and Lanen (2009). This ACB model explores the regression relationship between the natural logarithm of the cost ratio (dependent variable) and the natural logarithm of sales revenue, which acts as a proxy for the activity level (independent variable). The model is expressed as follows:

$$\begin{array}{rl}Log\left(Cost{i}_{i,t}/Cost{i}_{i,t-1}\right)& ={\beta }_0+{\beta }_{1}Log\left(Rev{i}_{i,t}/Rev{i}_{i,t-1}\right)\\ & +{\beta }_2{D}_{t.i}\ast Log\left(Rev{i}_{i,t}/Rev{i}_{i,t-1}\right)\\ & +{\epsilon }_{t.i}\end{array}$$

where

D_{t.i} – dummy variable taking the value of 1 when sales revenue decreases, and 0 when it increases

β₁ – reflects the cost increase rate for a 1% increase in sales revenue

β₂ – negative, significant β₂ indicates sticky costs (upward asymmetry), while a positive, significant β₂ indicates anti-sticky costs (downward asymmetry). A β₂ of zero suggests symmetric cost behavior.

The dependent variable in this study is managerial decision-making, measured by financial performance indicators. The researcher used three metrics to gauge performance: Return on Assets, Return on Equity, and Tobin’s Q, along with the annual profit growth rate.

Data Collection Methods

• Primary Sources: Cost data from the Iraqi Cement Company for the period from 2003 to 2017.

• Secondary Sources: Theoretical literature was gathered from prior studies, academic references, and related published books.

Study Variables

Study variables are presented in Table II. This detailed approach allows a rigorous assessment of how asymmetric cost behavior impacts managerial decision-making and financial performance, aiming to provide actionable insights on cost efficiency for sustainable competitive advantage (Table III).

Table II. Study Variables

Variable	Type	Symbol
Return on total assets	Dependent	ROA
Return on equity	Dependent	ROE
Tobin’s Q	Dependent	tobins_q
Sticky costs	Independent	×1
Anti-sticky (Sliding) costs	Independent	×2

Table III. Financial Data for the Sample Company

y	ROA	ROE	tobins_q	×1	×2	LNROA	LNROE	LNtobins_q	LN×1	LN×2
03	6.9	11.7003	5.782214	1447.2	1170.0	1.928291	2.459618	1.754786627	7.277357	7.064788
04	6.5	10.7103	6.238787	1408.2	1071.0	1.867567	2.37121	1.830785735	7.250038	6.97638
05	6.5	14.4203	6.13364	1916.2	1442.0	1.87602	2.66864	1.813788398	7.558078	7.27381
06	6.5	16.7303	5.928136	1056.0	1673.0	1.868103	2.817224	1.779709892	6.962243	7.422394
07	5.4	11.7000	3.704498	1188.0	1170.0	1.680987	2.459589	1.30954782	7.080026	7.064759
08	5.1	10.6100	3.622475	1168.0	1061.0	1.626354	2.361797	1.287157467	7.063048	6.966967
09	5.1	12.0000	3.424812	1177.0	1200.0	1.626354	2.484907	1.231046695	7.070724	7.090077
10	5.1	10.0300	3.546033	1188.0	1003.0	1.63813	2.305581	1.265829434	7.080026	6.910751
11	5.1	16.5000	3.506882	1155.0	1650.0	1.6271	2.80336	1.254727313	7.051856	7.408531
12	5.1	16.5100	3.34193	1166.0	1651.0	1.6271	2.803966	1.206548521	7.061334	7.409136
13	5.1	16.5200	3.443635	1190.0	1652.0	1.6271	2.804572	1.236527535	7.081709	7.409742
14	5.1	16.5300	3.404376	1077.3	1653.0	1.6271	2.805177	1.225061789	6.982244	7.410347
15	1.5	10.0616	4.739719	1408.3	1006.2	0.401919	2.308725	1.555977796	7.250162	6.913895
16	1.5	14.4716	4.291984	1169.3	1447.2	0.384582	2.672187	1.456749015	7.064189	7.277357
17	1.0	14.0816	3.600851	1760.3	1408.2	0.007467	2.644868	1.281170259	7.473258	7.250038

Hypothesis Testing

Main Hypothesis 1

The first main hypothesis posits that there exists asymmetric cost behavior (sticky and sliding costs) in the Iraqi Cement Company. Using Model 1, this relationship is expressed as:

$$\begin{array}{rl}& log\left(sg\mathrm{\&}a\mathrm{\_}growth\mathrm{\_}rate\right)\\ & =\beta 0+\beta 1log\left(rev\mathrm{\_}growth\mathrm{\_}rate\right)\\ & +\beta 2dv.log\left(rev\mathrm{\_}growth\mathrm{\_}rate\right)+\epsilon it\end{array}$$

$$\begin{array}{rl}& LOG\mathrm{\_}SG\mathrm{\_}A\mathrm{\_}GROWTH\mathrm{\_}RATE\mathrm{\_}\\ & =0.619\times LOG\mathrm{\_}REV\mathrm{\_}GROWTH\mathrm{\_}RATE\mathrm{\_}-0.37\\ & \times DV\mathrm{\_}LOG\mathrm{\_}REV\mathrm{\_}GROWTH\mathrm{\_}RATE\mathrm{\_}+0.039\end{array}$$

Regression results for the first hypothesis are presented in Table IV.

Table IV. Regression Results for the First Hypothesis

PRM
LOG_REV_GROWTH_RATE_	Coefficient	0.619
	Standard error	0.105
	t-Statistic	5.868
	Probability	0
DV_LOG_REV_GROWTH_RATE_	Coefficient	−0.370
	Standard error	0.126
	t-Statistic	−2.934
	Probability	0.0041
C	Coefficient	0.0390
	Standard error	0.027
	t-Statistic	1.413
	Probability	0.160
R-squared		0.777
Adjusted R-squared		0.773
S.E. of regression		0.249
Sum squared resid		6.789
Log likelihood		−1.949
F-statistic		190.852
Prob (F-statistic)		0

The analysis results confirm the hypothesis that the Iraqi Cement Company exhibits asymmetric cost behavior, with SG&A costs increasing when revenues rise (sticky costs) and decreasing when revenues fall (sliding costs). The statistical model used is significant (Prob(F) = 0), effectively capturing the relationship between revenue growth and SG&A costs, with an R-squared of 77.78% indicating that a substantial portion of the variation in SG&A costs is explained by revenue changes. This confirms the presence of asymmetric cost behavior and supports the relevance of using this model for more accurate financial and operational decision-making within the company.

Main Hypothesis 2

There is a statistically significant relationship between asymmetric cost behavior and short-term administrative decision-making, measured by financial performance indicators.

To test this hypothesis, we will examine the derived sub-hypotheses as follows.

First Sub-Hypothesis: Sticky Costs

There is a statistically significant relationship between sticky costs and short-term administrative decision-making, represented by financial performance indicators. This leads to the following hypotheses to be tested:

Sub-Hypothesis: There is a statistically significant relationship between sticky costs and short-term administrative decision-making, measured by the financial performance indicator Return on Assets (ROA)

According to stability tests, sticky costs are stationary, so we will apply static models.

The analysis of data in Table V reveals several insights into the relationship between sticky costs and the return on assets (ROA).

Table V. Relationship between Sticky Costs and Return on Assets

Variable	Coefficient	Standard error	t-Statistic	Probability
X1	−0.010100	0.002087	−5.053756	0.0000
C	6.068556	2.755657	2.202217	0.0463
R-squared	,0. 17683	Mean dependent variable		4.757887
Adjusted R-squared	0. 57880	Standard deviation of the dependent variable		1.894108
S.E. of regression	1.948152	Akaike info criterion		4.295205
Sum squared resid	49.33885	Schwarz criterion		4.389612
Log likelihood	−30.21404	Hannan-Quinn Criterion		4.294200
F-statistic	0.234020	Durbin-Watson stat		0.266521
Prob (F-statistic)	0.006606

The results indicate a probabilistic relationship, showing that as ROA decreases, sticky costs tend to increase, suggesting that cost behavior becomes stickier in scenarios with lower asset returns.

The average sticky cost is approximately 4.757887, providing an estimate for the company’s typical sticky cost level.

An R-squared value of 17.68% reveals that only 17.68% of the variance in sticky costs is explained by ROA changes, indicating that other factors beyond ROA play a significant role in influencing sticky costs.

The low p-value for the F-statistic (0.006606) confirms that the model is statistically significant and suitable for examining the relationship between ROA and sticky costs.

The Durbin-Watson statistic of 0.266521 indicates potential autocorrelation in the data, suggesting that the relationship between sticky costs and ROA may not be temporally stable and could vary over time.

This analysis confirms a statistically significant but modest relationship between sticky costs and ROA. While the model is useful for estimating sticky costs based on ROA, the company may improve accuracy by incorporating additional variables to gain a more comprehensive view of the factors influencing sticky costs.

Sub-Hypothesis: Relationship between Sticky Costs and Short-Term Administrative Decision-Making, Measured by Return on Equity (ROE)

To test this hypothesis, we conducted a regression analysis to examine the relationship between sticky costs and return on equity (ROE), based on the assumption that sticky costs exhibit level stability. This allowed the use of static models.

The analysis in Table VI provides insights into the relationship between sticky costs and return on equity (ROE).

Table VI. Relationship between Sticky Costs and Return on Equity

Variable	Coefficient	Standard error	t-Statistic	Probability
ROE	−17.49359	5.74835	−3.279406	0.0088
C	1534.573	353.8850	4.336360	0.0008
R-squared	0. 34290	Mean dependent variable		1298.321
Adjusted R-squared	0. 39996	Standard deviation of the dependent variable		249.5021
S.E. of regression	254.4427	Akaike info criterion		14.03959
Sum squared resid	841634.3	Schwarz criterion		14.13400
Log likelihood	−103.2970	Hannan-Quinn Criterion		14.03859
F-statistic	0.461593	Durbin-Watson stat		1.714767
Prob(F-statistic)	0.508797

The results indicate a statistically significant negative relationship between sticky costs and ROE, meaning that as ROE decreases, sticky costs tend to increase. This finding suggests that lower returns on equity are associated with higher sticky costs.

The coefficient for ROE highlights its negative impact on sticky costs, confirming that declines in ROE correspond with increases in sticky costs.

The R-squared value of 34.29% shows that approximately one-third of the variability in sticky costs can be explained by changes in ROE. However, a substantial proportion of sticky costs remains unexplained by ROE alone, hinting at other influencing factors.

The Prob(F-statistic) value of 0.508797 indicates that, while there is a significant relationship between ROE and sticky costs, the overall model’s statistical significance is limited, suggesting that ROE alone has constrained explanatory power on sticky costs.

The findings point to a negative correlation between ROE and sticky costs, implying that effective short-term administrative decisions might help reduce sticky costs. However, to better understand the dynamics between sticky costs and financial performance indicators like ROE, it would be useful to consider additional factors in future analyses.

Sub-Hypothesis: Relationship between Sticky Costs and Short-Term Administrative Decision-Making, Measured by Tobin’s Q

To test this hypothesis, we analyzed the relationship between sticky costs and Tobin’s Q, assuming level stability for sticky costs. This allows for the application of static models.

The analysis in Table VII sheds light on the relationship between sticky costs and Tobin’s Q, a measure related to short-term administrative decision-making:

Table VII. Relationship between Sticky Costs and Return on Equity

Variable	Coefficient	Standard error	t-Statistic	Probability
TOBINS_Q	−99.15648	54.71191	−1.812338	0.0931
C	870.5597	243.4680	3.575664	0.0034
R-squared	0.201698	Mean dependent variable		1298.321
Adjusted R-squared	0.140290	Standard Deviation dependent variable		249.5021
S.E. of regression	231.3397	Akaike info criterion		13.84922
Sum squared resid	695734.6	Schwarz criterion		13.94362
Log likelihood	−101.8691	Hannan-Quinn Criterion		13.84821
F-statistic	3.284570	Durbin-Watson stat		2.346038
Prob (F-statistic)	0.093084

The findings suggest a potential relationship between sticky costs and Tobin’s Q, yet it lacks statistical significance, with a p-value of 0.0931, indicating that the relationship is weak and does not hold at conventional confidence levels.

The coefficient for Tobin’s Q shows a negative effect on sticky costs, implying that as Tobin’s Q decreases, sticky costs may rise. However, this effect is not strongly supported statistically due to the low confidence level.

An R-squared of 20.17% reveals that only a small portion of the variation in sticky costs is explained by Tobin’s Q, indicating that Tobin’s Q alone is a weak predictor for changes in sticky costs.

The Prob (F-statistic) of 0.093084 reinforces the model’s limited statistical significance, suggesting that Tobin’s Q may not play a substantial role in explaining sticky costs within this context.

Although there is a minor indication of a relationship between sticky costs and Tobin’s Q, it is not statistically strong. This weak association implies that other financial indicators might more effectively explain sticky costs in relation to short-term administrative decisions. Further research into alternative financial metrics may offer additional insights into factors affecting sticky costs in the company’s decision-making processes.

Second Sub-Hypothesis: Sliding Costs

Sub-Hypothesis: Relationship between Sliding Costs and Short-Term Administrative Decision-Making, Measured by Return on Assets (ROA)

To test this hypothesis, a regression analysis was conducted to examine the relationship between sliding costs and ROA, under the assumption that sliding costs are level-stable, allowing for the application of static models.

The findings from Table VIII provide insights regarding the relationship between sliding costs and Return on Assets (ROA) as a measure of short-term administrative decision-making:

Table VIII. Relationship between Sliding Costs and Return on Assets

Variable	Coefficient	Standard error	t-Statistic	Probability
X2	−0.046004	0.001985	−46.233842	0.0000
C	4.131037	2.728084	1.514263	0.1539
R-squared	0.004189	Mean dependent variable		4.757887
Adjusted R-squared	−0.072412	Standard deviation of the dependent variable		1.894108
S.E. of regression	1.961488	Akaike info criterion		4.308849
Sum squared resid	50.01664	Schwarz criterion		4.403256
Log likelihood	−30.31637	Hannan-Quinn Criterion		4.307844
F-statistic	0.054682	Durbin-Watson stat		0.244692
Prob(F-statistic)	0.818750

The analysis suggests a relationship between sliding costs and ROA, but it is statistically insignificant, with a high p-value of 0.818750, indicating a lack of strong evidence for a meaningful relationship between sliding costs and ROA at conventional confidence levels.

The coefficient for ROA suggests a minor negative effect on sliding costs, indicating a slight reduction in sliding costs as ROA increases; however, this effect does not carry statistical significance.

An R-squared of 0.42% shows that only a minimal portion of the variation in sliding costs can be attributed to changes in ROA, highlighting the limited explanatory power of ROA regarding sliding costs.

The Prob(F-statistic) value of 0.818750 confirms the lack of statistical significance, indicating that ROA does not substantially predict or explain sliding costs within this model.

The results do not provide sufficient statistical evidence for a significant relationship between sliding costs and ROA. This suggests that sliding costs may not meaningfully impact ROA, nor do they play a significant role in short-term administrative decision-making when assessed through this financial performance metric. Further exploration of other financial indicators may yield a more comprehensive understanding of the factors affecting sliding costs in the company’s decision-making processes.

Sub-Hypothesis: Relationship between Sliding Costs and Short-Term Administrative Decision-Making, Measured by Return on Equity (ROE)

To test this hypothesis, we analyzed the relationship between sliding costs and ROE, assuming level stability for sliding costs, and allowing for the use of static models.

The findings from Table IX reveal several insights into the relationship between sliding costs and Return on Equity (ROE), as stated in the sub-hypothesis.

Table IX. Relationship between Sliding Costs and Return on Equity

Variable	Coefficient	Standard error	t-Statistic	Probability
X2	0.010000	1.10	9.12	0.0000
C	−7.04	1.51	−3.442428	0.0029
R-squared	0.00586	Mean dependent variable		1350.508
Adjusted R-squared	0.00798	Standard deviation of the dependent variable		264.1050
S.E. of regression	1.47	Sum squared resid		2.81
F-statistic	4.52	Durbin-Watson stat		1.530609
Prob (F-statistic)	0.000000

The results demonstrate a statistically significant relationship between sliding costs and ROE, with a p-value of 0.0000, indicating strong statistical significance. This allows us to confidently accept that a relationship exists between sliding costs and ROE.

The coefficient for ROE, at 0.010000, indicates a positive relationship, where a one-unit increase in ROE corresponds to a 0.010000 increase in sliding costs. This suggests that higher ROE is associated with increased sliding costs.

The R-squared value of 0.00586 suggests that while the relationship is statistically significant, only 0.586% of the variation in sliding costs is explained by changes in ROE. This low R-squared value indicates that other factors likely contribute more substantially to the fluctuations in sliding costs.

The adjusted R-squared is similarly low, underscoring that although ROE has a statistically significant relationship with sliding costs, it is not a strong predictor of these costs on its own.

These findings support the sub-hypothesis, confirming a statistically significant relationship between sliding costs and ROE. However, the low R-squared and adjusted R-squared values imply that ROE explains only a minimal portion of the variability in sliding costs, suggesting the need to consider additional factors to gain a comprehensive understanding of sliding cost influences within the company’s decision-making framework.

Sub-Hypothesis: Relationship between Sliding Costs and Short-Term Administrative Decision-Making, Measured by Tobin’s Q

To test this hypothesis, we analyzed the relationship between sliding costs and Tobin’s Q under the assumption that sliding costs remain level-stable, allowing for the use of static models.

The results from Table X provide insights into the relationship between sliding costs and Tobin’s Q, as stated in Sub-Hypothesis 2-3.

Table X. Relationship between Sliding Costs

Variable	Coefficient	Standard error	t-Statistic	Probability
X2	−0.000682	0.001172	−0.582184	0.5704
C	5.235135	1.610200	3.251233	0.0063
R-squared	0.025410	Mean dependent variable		4.313998
Adjusted R-squared	−0.049559	Standard deviation of the dependent variable		1.130067
S.E. of regression	1.157731	Akaike info criterion		3.254367
Sum squared resid	17.42443	Schwarz criterion		3.348773
Log likelihood	−22.40775	Hannan-Quinn Criterion		3.253361
F-statistic	0.338938	Durbin-Watson stat		0.478398
Prob(F-statistic)	0.570402

The regression coefficient for sliding costs is −0.000682, a value close to zero, suggesting that the impact of sliding costs on Tobin’s Q is minimal to negligible, indicating a weak or non-existent relationship.

The model lacks statistical significance as indicated by the p-value for the F-statistic, showing that the relationship between sliding costs and Tobin’s Q does not meet conventional significance levels.

These findings provide insufficient statistical evidence to support a significant relationship between sliding costs and Tobin’s Q. In this model, changes in sliding costs do not appear to predict variations in Tobin’s Q.

The data does not support Sub-Hypothesis 2-3, as there is no statistically significant relationship between sliding costs and Tobin’s Q. Therefore, Tobin’s Q does not meaningfully contribute to short-term decision-making effectiveness with respect to sliding costs in this context.

Findings

Based on the hypothesis tests conducted, the study reached the following conclusions:

1. Existence of Asymmetric Cost Behavior: The study confirmed the presence of asymmetric cost behavior (sticky and anti-sticky costs) in the Iraqi General Cement Company. This finding underscores that certain costs in the company respond differently to changes in activity levels, supporting the idea of cost asymmetry.

2. Significant Relationship with Financial Performance: There is a statistically significant relationship between asymmetric cost behavior and short-term decision-making effectiveness, as indicated by various financial performance measures. Specifically, asymmetric costs appear to influence return on assets (ROA), return on equity (ROE), and Tobin’s Q ratio, highlighting the relevance of these metrics in evaluating decision quality within the company.

3. General Findings on Asymmetric Costs and Decision-Making: Overall, the study demonstrates a connection between asymmetric costs and improved short-term administrative decisions, as reflected in financial performance. These findings suggest that monitoring and analyzing cost behavior patterns, along with informed decision-making, can help the company meet its financial performance goals and enhance its operations.

4. Practical Implications for the Iraqi General Cement Company: Given the evidence of a relationship between asymmetric costs and short-term decision-making, the company can benefit from integrating these findings into its decision-making processes. By closely monitoring and analyzing sticky and anti-sticky costs, the company can better optimize cost management practices, streamline administrative decisions, and improve overall financial performance.

These findings underline the importance of strategic cost management and highlight the potential value in adjusting resource allocation based on activity levels, which may ultimately strengthen the company’s competitive position.

Recommendations

Based on the statistical analysis results:

1. Management should work to minimize excess costs associated with sticky costs, as these can negatively affect ROA. Improving efficiency can enhance company returns.

2. Given the positive impact of sticky costs on ROE, the company may consider increasing these costs carefully, ensuring feasibility through thorough analysis.

3. Despite a lack of significant findings between slippery costs, ROA, and Tobin’s Q, further investigation is recommended to assess potential influences on company value.

4. Periodic evaluation of financial decisions on performance is essential. Leveraging data analysis can support improved decision-making and company outcomes.

5. Ongoing research incorporating additional variables and performance metrics can provide a more comprehensive understanding of cost relationships and potential growth factors.