The nature of current assets and current liabilities is that they are subject to continuous rotation (Jaworski & Czerwonka, 2022). Consequently, this necessitates careful management of the relationship between these items, which constitutes the essence of working capital management (WCM).
Efficient management of working capital is crucialfor all businesses (Akdoğan & Dinç, 2019). It plays a vital role in a company’s overall corporate strategy, influencing its operational performance, value creation (Chang, 2018; Shin & Soenen, 1998), and competitiveness (Yazdanfar & Öhman, 2014). Therefore, WCM is considered one of the most significant aspects of corporate financial management (Deloof, 2003; Gołaś, 2020).
Working capital arises from the temporal gap between spending on raw materials and receiving payment from the sale of the finished product (Shin & Soenen, 1998). The longer this time lag, the greater the investment in working capital (Deloof, 2003). While WCM is considered a component of shortterm financial management, there is a widespread consensus that it can significantly impact a firm’s profitability (Baños-Caballero et al., 2012; Chang, 2018; Lazaridis & Tryfonidis, 2006). A fundamental issue, therefore, lies in understanding the role and factors that affect WCM, which can help mitigate risk and enhance a firm’s overall performance (Nazir & Afza, 2009).
A commonly used tool for assessing the effectiveness of WCM, which is fundamental to the concept (Akdoğan & Dinç, 2019), is the cash conversion cycle (CCC) (Deloof, 2003). This comprehensive measure of WCM is based on the analysis of three sub-cycles: inventory, accounts receivable, and accounts payable. The sum of the inventory and accounts receivable cycles constitutes the operating cycle. When combined with the accounts payable cycle, it enables the determination of the CCC. An aggressive working capital policy is linked to a shorter CCC, whereas a conservative one is associated with a longer CCC (Akdoğan & Dinç, 2019). Given the company’s interest in strategies to sustain and improve its financial performance, meticulous observation of the CCC becomes crucial (Linh & Mohanlingam, 2018).
A comprehensive review of previous research concerning the relationship between WCM and financial performance reveals two dominant trends of investigation. The first trend focuses on the linear relationship between these variables. In contrast, the second trend is oriented towards verifying the nonlinear nature of this relationship, specifically in the form of a quadratic function. Within the framework of the first trend, a substantial body of empirical research suggests that a reduction in the CCC has a positive influence on corporate profitability. Examples of this research include studies conducted by Bieniasz and Gołaś (2011), Chang (2018), García-Teruel and Martínez-Solano (2007), Gołaś (2020), Gul et al. (2013), Jose et al. (1996), Lazaridis and Tryfonidis (2006), Linh and Mohanlingam (2018), Mathuva (2010), Shin and Soenen (1998), and Yazdanfar and Öhman (2014). Additionally, the findings presented by Singh et al. (2017) offered robust support for this viewpoint. The authors conducted a meta-analysis of 46 research articles and found evidence that an aggressive working capital policy is associated with increased profitability.
While the prevailing view is that an extended CCC negatively impacts firm profitability, various studies have found a positive relationship between these two. For instance, research conducted by Akdoğan and Dinç (2019), Charitou et al. (2012), and Gill et al. (2010) provided empirical evidence supporting the notion that a longer CCC is associated with higher firm profitability.
Despite the extensive research conducted on the linear relationship between the CCC and profitability, analyses that consider the nonlinear nature of this relationship are significantly less prevalent. This group includes works by Afrifa (2016), Baños-Caballero et al. (2012), Fernández-López et al. (2020), Jaworski and Czerwonka (2022), and Yilmaz and Acar (2019). However, the findings of the aforementioned studies are inconsistent. Specifically, while Baños-Caballero et al. (2012) demonstrated a concave relationship between the level of working capital and profitability, Afrifa (2016) indicated that the nature of this relationship depends on cash flow; it is concave in the absence of cash flow and becomes convex when cash flow is considered. In contrast, Fernández-López et al. (2020) and Jaworski and Czerwonka (2022) found no compelling evidence to support the nonlinear nature of this relationship.
Although the relationship between WCM and corporate financial performance is a thoroughly documented subject in the literature, empirical analyses focused on this matter have granted relatively little consideration to the agriculture and food sector. Within the limited body of literature addressing this issue, Lyroudi and Lazaridis (2000) examined the relationship between CCC and profitability for Greek food and beverage companies. The findings of this study indicated a positive relationship between CCC and return on assets, as well as between CCC and net profit margin. In the same vein, Akdoğan and Dinç (2019) identified that Turkish agribusinesses might enhance their profitability by adopting a conservative working capital policy, i.e., by lengthening their CCC up to an optimal level. Ahmadi et al. (2012) employed data from 33 companies operating within the food industry sector that were listed on the Tehran Stock Exchange from 2006 to 2011. Their analysis revealed an inverse relationship between WCM variables and the profitability of the entities under examination. Linh and Mohanlingam (2018) investigated the impact of the CCC on profitability in 34 listed agricultural and food companies in Thailand over five years from 2009 to 2013, demonstrating that the CCC exhibited a significant inverse relationship with the profitability of these enterprises. Additionally, a survey conducted by Šeligová and Koštuříková (2022) on food companies in the Czech Republic, covering the period from 2009 to 2019, revealed that the CCC negatively affected profitability, as measured by the return on sales. Furthermore, Fernández-López et al. (2020) analyzed a sample of 444 Spanish cheese-manufacturing companies from 2010 to 2016, illustrating an adverse effect of the CCC on the profitability of these firms.
Research on the relationship between WCM and the profitability of agribusiness and food sector enterprises has also been conducted under the conditions of the Polish economy. In this field, Bieniasz and Gołaś (2011), using data from food industry companies in Poland from 2005 to 2009, noted that for all size groups of companies return on nonfinancial assets was negatively impacted by cycles of inventory, accounts receivable, current liabilities, and cash conversion, meaning that their extending was accompanied by a decline in profitability. In a subsequent paper, Gołaś et al. (2013) corroborated the advantageous impact of reducing all categories of the working capital cycle on the profitability of nonfinancial assets within the examined sector. This conclusion was derived from an analysis of data collected from 30 food branches in Poland between 2005 and 2009. Later, Gołaś (2020), based on data from Polish dairy companies spanning from 2008 to 2017, demonstrated that while the extension of Days Sales of Inventory (DSI) and CCC exerted a negative impact on return on assets (ROA), the extension of Days Sales Outstanding (DSO) and Days Payable Outstanding (DPO) positively influenced ROA in dairy companies.
Given the nascent state of analyses devoted to the nonlinear relationship between WCM and corporate profitability, alongside the relatively limited scholarly interest in this issue within the food sector, this study aims to address this gap by focusing on the operations of dairy processing entities in Poland. In this regard, due to the distinctive characteristics of cooperatives compared to investor-owned firms (IOFs), this study concentrates on the former category of milk processors. The distinguishing characteristics of cooperatives, as compared to IOFs, become particularly evident in the disparity between their objectives. While noncooperative entities focus on maximizing returns on capital investments for their owners, managing a cooperative requires taking into account the interests of its members, which are more complex in nature (refer to, for example, Ziętek-Kwaśniewska & Zuba-Ciszewska, 2022, for further insights on this subject). Although the profitability of a dairy cooperative owned by milk suppliers is not its primary objective, the efficiency of the WCM, measured in part by the length of the CCC, impacts not only the financial results but also the overall effectiveness of the cooperative’s operations. This, in turn, may serve to facilitate the realization of its members’ objectives.
The primary aim of this article is to investigate the effect of the CCC on profitability within dairy cooperatives in Poland. The article uses a panel data methodology to examine 53 dairy cooperatives in Poland during the period 2018–2023. Given the inconsistent results on the impact of CCC on profitability in the existing literature, with some studies reporting a positive association, others a negative one, and some suggesting nonlinear effects, this study seeks to answer the following question:
Q1. What is the nature of the relationship between the CCC and the profitability of dairy cooperatives in Poland?
Given the divergent findings in the literature, the following nondirectional hypothesis is proposed:
H1. The CCC has a statistically significant impact on the profitability of dairy cooperatives in Poland.
The present study makes a contribution to existing literature in the following ways. Firstly, it provides novel evidence on the relationship between WCM and corporate performance, considering the potential nonlinear nature of this relationship. Secondly, by focusing attention on the operations of dairy cooperatives, the study expands the existing body of knowledge regarding their functioning. This is particularly important in the context of the significance of these entities in ensuring national food security. By enhancing the comprehension of the impact of WCM on the financial performance of dairy cooperatives in Poland, this article presents substantial value for both academic researchers and industry professionals.
This article examines the impact of CCC on the profitability of dairy cooperatives in Poland from 2018 to 2023. The financial data employed in this study were retrieved from the Emerging Markets Information Service (EMIS) database (EMIS, 2024) in the following stages. First, entities operating under NACE classification 10.51 (operation of dairies and cheese making) were identified. Subsequently, the scope of examination was refined to include only entities that function as cooperatives. In the next phase of the study, entities that had ceased operations, were undergoing liquidation, or exhibited negative equity were excluded from further analysis. Finally, entities with missing data in key items of the financial statements for the period under review were removed, acknowledging that the averaging of balance sheet items necessitated financial data from 2017. Following the application of the aforementioned criteria, a final survey sample was obtained, representing a balanced panel of 53 dairy cooperatives.
Following Fernández-López et al. (2020), the primary dependent variable examined in this study, which reflects the profitability of entities, is the return on assets (ROA_DA), defined as the ratio of earnings before interest, taxes, depreciation, and amortization (EBITDA) to total assets. It has been observed that, given the greater difficulty involved in manipulating EBITDA compared to earnings before interest and taxes (EBIT), the former is considered a more reliable indicator of a company’s profitability (Fernández-López et al., 2020). For control purposes, the study also referred to an alternative measure of profitability expressed as the ratio of EBIT to total assets. The key independent variable employed to depict the WCM is CCC, calculated as the sum of the collection and inventory conversion cycles minus the payable cycle. The determination of the individual sub-cycles was based on sales as the reference (Bieniasz & Gołaś, 2011; Fernández-López et al., 2020). Drawing on previous studies, the following set of controls was included: firm leverage (Baños-Caballero et al., 2012; García‐Teruel & Martínez-Solano, 2007), firm size (Fernández-López et al., 2020; Gołaś, 2020), and firm growth (Baños-Caballero et al., 2012; Sharma & Kumar, 2011). In light of the prevalence of two predominant methodologies for measuring company size in the extant literature, this study employs two approaches to address this issue. The first approach refers to the size of total assets (Akdoğan & Dinç, 2019; Gołaś, 2020), and the second one refers to the company’s sales revenue (Baños-Caballero et al., 2012; Deloof, 2003). Table 1 summarizes the definitions of the variables used in the study.
Definition of variables used in the study
| Role | Variable | Definition |
|---|---|---|
| Dependent | Cooperative profitability | |
| [ROA_DA] | ||
| [ROA] | ||
| Independent | Cash Conversion Cycle [CCC] | CCC = Inventory period + Receivables period – Payables period |
| where: | ||
| Control | Size | |
| [SIZE1] | ln(average total assets) | |
| [SIZE2] | ln(sales) | |
| Leverage [LEV] | ||
| Growth [GROWTH] | ||
Source: author’s own elaboration.
To assess the impact of CCC on the profitability of dairy cooperatives, a panel data methodology was employed, enabling the control of unobservable individual heterogeneity (Fernández-López et al., 2020; Gołaś, 2020). To determine the most appropriate model, the Breusch–Pagan and Hausman tests were conducted. Their results suggested that a fixed-effects (FE) model was the most suitable.
The initial phase of the study assessed the existence of a linear relationship between the CCC variable and the profitability of cooperatives using the following model:
where: Profit represents ROA_DAit and ROAit of cooperative i at time t; CCCit is the CCC of cooperative i at time t; LEVit and GROWTHit are the control variables described in Table 1; SIZEit stands for SIZE1it and SIZE2it, as presented in Table 1; αi is the unobservable heterogeneity (constant over time); λt denotes year fixed effects; εit is the error term. Considering the possible combinations among the variables Profit and SIZEit, four linear models were developed: Model 1 with the ROA_DA and SIZE1 variables; Model 2 with the ROA_DA and SIZE2 variables; Model 3 with the ROA and SIZE1 variables; and Model 4 with the ROA and SIZE2 variables.
Subsequently, in light of the potential nonlinear association between the aforementioned variables, as evidenced by the several findings of preceding empirical investigations, a regression analysis of the cooperative’s profitability was conducted, with CCC and its square serving as independent variables:
where: CCC2it denotes the square of CCCit; other notations are consistent with those presented in Equation 1. Considering the possible combinations among the variables Profit and SIZEit, four nonlinear models were obtained: Model 5 with the ROA_DA and SIZE1 variables; Model 6 with the ROA_DA and SIZE2 variables; Model 7 with the ROA and SIZE1 variables; and Model 8 with the ROA and SIZE2 variables. The quadratic relationship proposed in Equation 2 indicates the presence of a turning point, which can be determined by differentiating the profitability variable with respect to the CCC. Setting the first derivative equal to zero yields the turning point at –β1/2β2. This point represents a maximum if the second derivative, 2β2, is negative; that is when β2 < 0 (Baños-Caballero et al., 2012). Therefore, a significantly negative coefficient on the squared CCC term confirms an inverted U-shaped relationship. If this scenario holds, it suggests that CCC has a positive effect on the cooperative’s profitability at lower levels of CCC and a negative effect at higher levels.
To account for unobserved time-specific effects, year fixed effects were included in the model specification. The calculations were performed using the Stata package (version 16).
Table 2 presents the summary statistics for the variables used in the estimations. The range of ROA_DA extended from –31.94% to 34.94%, with a mean of 6.32% and a median of 6.68%. The ROA ranged from –35.30% to 30.17%, with a mean of 1.98% and a median of 1.80%. The CCC varied from –25.58 days to 78.70 days, with a mean of 14.87 days and a median of 13.89 days. This result suggests that dairy cooperatives generally exhibit a high level of efficiency in converting expenses into cash. Notably, a study conducted by Ziętek-Kwaśniewska and Zuba-Ciszewska (2022), covering the period from 2018 to 2020, observed that the CCC was shorter for cooperative entities compared to their noncooperative counterparts. On average, debt financing constituted 44.60% of total assets, with a median of 45.12%. The mean annual sales growth rate was 6.69%, with a median of 4.28%.
Descriptive statistics
| Variable | Mean | SD | Min | Max | Q1 | Me | Q3 |
|---|---|---|---|---|---|---|---|
| ROA_DA | 0.0632 | 0.0685 | −0.3194 | 0.3494 | 0.0334 | 0.0668 | 0.0925 |
| ROA | 0.0198 | 0.0643 | −0.3530 | 0.3017 | 0.0000 | 0.0180 | 0.0455 |
| CCC | 14.8726 | 12.1390 | −25.5786 | 78.6988 | 7.1528 | 13.8873 | 21.7928 |
| LEV | 0.4460 | 0.1346 | 0.1371 | 0.7934 | 0.3418 | 0.4512 | 0.5526 |
| SIZE1 | 10.6090 | 1.6320 | 6.8503 | 14.7564 | 9.4411 | 10.3448 | 11.9224 |
| SIZE2 | 11.6077 | 1.5760 | 7.9663 | 16.0582 | 10.4530 | 11.2694 | 12.7689 |
| GROWTH | 0.0669 | 0.2049 | −0.5247 | 1.5916 | −0.0383 | 0.0428 | 0.1286 |
Note: Variables defined in Table 1.
Abbreviations: SD – standard deviation; Min – minimum; Max – maximum; Q1 – lower quartile; Me – median; Q3 – upper quartile.
Source: author’s own calculations based on EMIS (2024)
Table 3 reports the Pearson correlation coefficients among the variables used in the analyses. There is a significant (at the 10% level) negative correlation between ROA and CCC, and a negative relationship, albeit not statistically significant, between ROA_DA and CCC. Profitability—assessed through both ROA_DA and ROA—is significantly and positively related to the cooperative’s sales growth rate (GROWTH), as well as its size, as indicated by both the SIZE1 and SIZE2 variables. The correlation between the debt ratio and the entity’s profitability is not statistically significant.
Correlation matrix
| Variables | ROA_DA | ROA | CCC | LEV | SIZE1 | SIZE2 | GROWTH |
|---|---|---|---|---|---|---|---|
| ROA_DA | 1.000 | ||||||
| ROA | 0.926*** | 1.000 | |||||
| CCC | −0.091 | −0.107* | 1.000 | ||||
| LEV | −0.002 | −0.078 | −0.096* | 1.000 | |||
| SIZE1 | 0.189*** | 0.101* | 0.444*** | −0.196*** | 1.000 | ||
| SIZE2 | 0.224*** | 0.131** | 0.390*** | −0.123** | 0.980*** | 1.000 | |
| GROWTH | 0.444*** | 0.462*** | −0.182*** | 0.015 | 0.107* | 0.152*** | 1.000 |
Indicates significance at 1% level
Indicates significance at 5% level
Indicates significance at 10% level
Source: author’s own calculations based on EMIS (2024).
The coefficient values do not indicate the presence of strong correlations that could signal a multicollinearity problem. Furthermore, to assess the presence of multicollinearity, the variance inflation factor (VIF) was computed for each independent variable included in the specified models. None of the VIF values exceeded 5, a threshold generally considered indicative of problematic multicollinearity (Treischl, 2023; Welc & Esquerdo, 2017). Therefore, it was concluded that multicollinearity does not pose a significant concern for the estimated models.
Tables 4 and 5 present the results of the panel data analysis for the research sample. Given that the outcomes of the Lagrange Breusch-Pagan multiplier test and the Hausman test indicated the suitability of the fixed-effects model, the results of this model are presented in the tables.
The empirical findings in Table 4 do not confirm a linear effect of CCC on the profitability of cooperatives; although the coefficient’s sign is negative, as anticipated based on previous empirical evidence for the agriculture and food sector (e.g., Bieniasz & Gołaś, 2011; Fernández-López et al., 2020; Gołaś, 2020; Linh & Mohanlingam, 2018), the relationship is not statistically significant. This outcome diverges from the results reported by Gołaś (2020) for Polish dairy companies from 2008 to 2017. However, it is crucial to highlight the differences between the studies by Gołaś (2020) and the present one, particularly in terms of sample scope, model specification, and variable definition.
Estimation results for models 1–4
| Variables and tests | ROA_DA | ROA | ||
|---|---|---|---|---|
| Model 1 | Model 2 | Model 3 | Model 4 | |
| CCC | −0.00014 | −0.00003 | −0.00018 | −0.00011 |
| LEV | −0.3021*** | −0.2980*** | −0.3312*** | −0.3306*** |
| SIZE1 | −0.0145 | −0.0139 | ||
| SIZE2 | 0.0130 | 0.0040 | ||
| GROWTH | 0.1711*** | 0.1627*** | 0.1703*** | 0.1667*** |
| Constant | 0.3440 | 0.0389 | 0.3068 | 0.1135 |
| F test | F(9,52) = 6.42 p < 0.001 | F(9,52) = 6.29 p < 0.001 | F(9,52) = 6.62 p < 0.001 | F(9,52) = 6.52 p < 0.001 |
| Breusch-Pagan LM test | χ2(1) = 61.10 p < 0.001 | χ2(1) = 59.48 p < 0.001 | χ2(1) = 36.81 p < 0.001 | χ2(1) = 35.93 p < 0.001 |
| Hausman test | χ2(4) = 35.93 p < 0.001 | χ2(4) = 35.62 p < 0.001 | χ2(4) = 29.03 p < 0.001 | χ2(4) = 29.87 p < 0.001 |
| within R2 | 0.3724 | 0.3725 | 0.3723 | 0.3718 |
| No. of obs. | 318 | 318 | 318 | 318 |
Note: In models 1 and 2, the dependent variable is firm profitability, measured as ROA_DA, whereas in models 3 and 4, it is represented by ROA. Robust standard errors clustered at the firm level are reported in parentheses. Time dummies are included in the estimations, but not reported.
Indicates significance at 1% level
Indicates significance at 5% level
Indicates significance at 10% level
Source: author’s own calculations based on EMIS (2024).
Regarding the control variables, LEV and GROWTH are found to be statistically significant. Cooperative profitability is positively influenced by the GROWTH variable, indicating that entities experiencing higher sales growth tend to exhibit better performance. This finding is consistent with the results of, for example, Gołaś (2020) reported for the Polish dairy industry, and with those of García-Teruel and Martínez-Solano (2007) obtained for a sample of small and medium-sized Spanish firms. Conversely, cooperative profitability, as measured by ROA_DA and ROA, decreases as the debt level increases. This result aligns with the existing literature suggesting a negative impact of debt on profitability, as confirmed, among others, by the studies of Bieniasz and Gołaś (2011), García-Teruel and Martínez-Solano (2007), and Jaworski and Czerwonka (2022). Although numerous studies that examine the impact of CCC on profitability have identified the size of the entity as a variable that significantly affects its financial performance (e.g., Fernández-López et al., 2020; García-Teruel & Martínez-Solano, 2007; Gołaś, 2020), this study has not demonstrated the statistical significance of the cooperative size measured by the SIZE1 and SIZE2 variables. This outcome suggests that the size of the entity—a factor often employed as an inverse proxy of financial constraints (Baños-Caballero et al., 2014)—is not a substantial determinant of the profitability of dairy cooperatives in Poland.
The results obtained from models 5–8 are presented in Table 5. The findings indicate that β2 is negative and statistically significant in these models, thereby supporting the main hypothesis by confirming an inverted U-shaped relationship between CCC and profitability. The findings of this study are consistent with those of Baños-Caballero et al. (2012). This character of the relationship between CCC and profitability suggests that dairy cooperatives have an optimal CCC level that maximizes their profitability. Therefore, at lower CCC levels, increasing CCC has a positive effect on the cooperative’s profitability, while at higher levels, it has a negative effect. Regarding the control variables, LEV and GROWTH have a statistically significant effect on firm profitability, with negative and positive effects, respectively. In contrast, SIZE1 and SIZE2 are not statistically significant.
Estimation results for models 5–8
| Variables and tests | ROA_DA | ROA | ||
|---|---|---|---|---|
| Model 5 | Model 6 | Model 7 | Model 8 | |
| CCC | 0.00178* | 0.00183* | 0.00153 | 0.00161 |
| CCC2 | −0.00004*** | −0.00004*** | −0.00004*** | −0.00004*** |
| LEV | −0.2216*** | −0.2249*** | −0.2595*** | −0.2631*** |
| SIZE1 | −0.0203 | −0.0190 | ||
| SIZE2 | −0.0036 | −0.0114 | ||
| GROWTH | 0.1730*** | 0.1726*** | 0.1719*** | 0.1758*** |
| Constant | 0.3555 | 0.1846 | 0.3170 | 0.2482 |
| F test | F(10,52) = 26.26 | F(10,52) = 25.73 | F(10,52) = 23.93 | F(10,52) = 23.79 |
| Breusch–Pagan LM test | χ2(1) = 71.12 | χ2(1) = 68.97 | χ2(1) = 44.07 | χ2(1) = 43.22 |
| Hausman test | χ2(4) = 20.38 | χ2(4) = 21.57 | χ2(5) = 22.15 | χ2(5) = 24.73 |
| within R2 | 0.3900 | 0.3888 | 0.3865 | 0.3859 |
| No. of obs. | 318 | 318 | 318 | 318 |
Note: In models 5 and 6, the dependent variable is firm profitability, measured as ROA_DA, whereas in models 7 and 8, it is represented by ROA. Robust standard errors clustered at the firm level are reported in parentheses. Time dummies are included in the estimations, but not reported
Indicates significance at 1% level
Indicates significance at 5% level
Indicates significance at 10% level
Source: author’s own calculations based on EMIS (2024).
After the confirmation of an inverted U-shaped relationship between CCC and profitability, the optimal CCC levels were identified for models 5–8. The outcomes are displayed in Table 6. The data presented herein indicate that the optimal CCC level ranges from 21.46 to 23.05 days, depending on the model specifications. These findings suggest that cooperative financial managers should strive to maintain a close proximity to the optimal level and avert substantial deviations from this benchmark in order to maximize profitability.
Turning points for models 5–8
| Specification | Model 5 | Model 6 | Model 7 | Model 8 |
|---|---|---|---|---|
| Optimal CCC level | 22.26 | 23.05 | 21.46 | 21.95 |
Source: author’s own calculations based on EMIS (2024).
Efficient management of working capital constitutes a fundamental element in building a company’s financial performance. Although the impact of WCM on profitability is of significant importance, few studies have empirically explored this relationship within the food industry. The objective of this paper was to provide empirical evidence regarding the impact of the CCC—the synthetic measure of WCM—on the profitability for a sample of Polish dairy cooperatives over the period 2018–2023.
The empirical examination, employing panel data methodology, yielded evidence that supported the hypothesis of this study. The findings of the analysis indicate that CCC exerts a statistically significant impact on the profitability of dairy cooperatives in Poland. It was found that there is an inverted U-shaped relationship between the CCC and the profitability of cooperatives. This suggests the existence of an optimal level of the CCC; specifically, at lower levels of the CCC, it has a favorable effect on cooperative profitability, while at higher levels, it has an adverse effect. The findings posit that the optimal level of CCC ranges from approximately 21.5 to 23 days, suggesting that financial managers aiming to maximize profit should orient the CCC to this time horizon. Regarding the control variables, LEV and GROWTH were identified as statistically significant with negative and positive effects on profitability, respectively. The findings of the study demonstrated that the cooperative size did not prove to be statistically significant in terms of its effect on the profitability of dairy cooperatives.
This paper makes a significant contribution to the existing literature on the relevance of WCM to business performance by offering a novel perspective on the relationship between WCM and business outcomes. Firstly, it enhances the current body of empirical research on this topic by implementing the nonlinear nature of this relationship as a conceptual framework, a perspective that is underrepresented in the existing literature. Secondly, the decision to include dairy cooperatives in the sample serves to broaden the understanding of the functioning of these specific entities. It is important to note that the findings of this research hold considerable significance in both theoretical and practical contexts.
The present study is not without limitations, creating opportunities for further research. First, the study overlooks the variations in the investment phases, the market expansion strategies, and the characteristics of the milk suppliers between cooperatives. This oversight may impede the study’s ability to draw accurate conclusions. Subsequent studies may benefit from a more comprehensive examination of these disparities. Moreover, the scope of this study is limited to a sample of dairy cooperatives. To further expand the existing body of knowledge in this area, comparative research is recommended to explore the development of this relationship in the context of IOFs. Given the restriction of the present study to entities from Poland, it is recommended that similar studies extend their scope to entities from other countries.