Packages or systems? Working capital management and financial performance among listed U.S. manufacturing firms

In this paper, the perception of WCM as a package is based on configurational theorizing. Configurational theorizing is based on the complexity theory that suggests that each variable in a configuration is possibly both interrelated and interdependent on other variables for contributing to the outcome. Changes are believed to be made in a stepwise and episodic manner, creating a distinct fit between context, structure, and performance (Gerdin and Greve 2004). For instance, such episodic change is illustrated by Guenther et al. (2014), as they studied the concept of ‘cost stickiness’. Applying the same insight in this context means that there is not necessarily a linear cost development for making switches or rapid changes in the working capital level. For instance, Huson and Nanda (1995) argue that the inventory variable has limitations in production capacity or inventory storage, which makes the shift from one configuration to another costly. As a consequence, there are resource constraints on making incremental changes in the inventory level. This means there exist distinct configurations of inventory set-up, and movement between one configuration and another represents a movement to a different type and not an incremental adjustment of inventory set-up.

There have been only some limited attempts at empirical WCM research to adopt configurational theorizing when studying WCM. One of the earliest attempts was by Weinraub and Visscher (1998) who studied the correlation between OWC and FWC. However, the results only showed associative relationships at the aggregate level. Furthermore, firm performance was not considered in this study. While Howorth and Westhead (2003) linked packages of WCM to financial performance, they studied only the OWC dimension. For instance, one of the four distinct packages in their study was solely related to inventory management. The same goes for Galeazzo and Furlan (2018) and Karatzas et al. (2016), as they only link packages consisting of production and inventory techniques to financial performance. Talonpoika et al. (2016) shifted focus from OWC to FWC. The results showed 11 different packages for achieving effective FWC. However, as the FWC was the outcome variable, it is difficult to say whether this also contributes to financial performance. Furthermore, they did not study how each component in OWC and FWC may be combined into effective WCMPs.

However, most of the empirical WCM literature is based on Cartesian theorizing, typically trying to establish direct and indirect effects between independent and dependent variables (Ragin 2008). Ragin (2000) termed this type of research as based on ‘net-effects thinking’. In this case, WCM was seen as consisting of variables that exist independently of each other, having a unique net effect on financial performance. The empirical WCM literature suggests a mostly direct type of net effects between variables from WCM and financial performance (García-Teruel and Martínez-Solano 2007; Baños-Caballero et al. 2010, 2012, 2014, 2016, 2019; Martínez-Sola et al. 2014; Gama and Pais 2015; Lyngstadaas and Berg 2016; Zeidan and Shapir 2017). The main conclusion is that a firm should keep WCM level low to positively affect financial performance measures such as ROA.

Another related research stream has focused on determining the optimal level of WCM (García-Teruel and Martínez-Solano 2010; Venkiteshwaran 2011; Baños-Caballero et al. 2014; Kim and Bettis 2014; Mun and Jang 2015; Nason and Patel 2016). The main idea is that moving too far away from the optimal level of WCM would have a detrimental effect on financial performance. These studies commonly find a negative effect of deviating too much from the optimal level of WCM on financial performance. However, these scholars assumed that there is only one optimal working capital level existing at a time (given the constraints that the same contingencies impose), and incremental changes can be made for closing the gap between actual and optimal levels of working capital.

WCMPs, in this paper, are understood as combinations of OWCs and/or FWCs. If there exists systematic interdependency within or between OWC and FWC, then they are viewed as systems. A systematic interdependency is believed to exist if there is a statistically significant interaction term between two or more components in a given WCMP.

OWC is commonly interpreted and operationalized in the same manner as the cash conversion cycle (Gitman 1974). This means that inventory, accounts receivable, and accounts payable are the three main components constituting OWC. OWC is the working capital requirement for conducting daily business operations. The requirement is determined by the time gap between the outflow and inflow of cash from these daily business operations (Gitman 1974). However, there are also other components related to OWC that may be relevant (such as prepaid expenses). As such, other current assets will also be added as a component of OWC in this study.

FWC reflects how working capital is financed. While accounts payable are part of OWC, they also represent a source for financing working capital. As such, accounts payable, cash holding (cash plus short-term investments), and other short-term liabilities (such as bank-based loans) are often part of the financing debate regarding complements or substitutes among working capital components. The debate about FWC being a complement or a substitute also represents two different main strands in current corporate financial theory. Meltzer (1960) is often associated with the substitutive perspective, while Biais and Gollier (1997) and Jain (2001) propose a complementary perspective. The substitution perspective argues that supplier trade credit is an expensive financing form (a typical example is the supplier’s offer of a 2% discount if paying before 10 days, otherwise pay per 30 days without discount) (Fabbri and Menichini 2010). The main argument is that other sources of financing are cheaper compared to trade credit (Gatev and Strahan 2006).

However, Fabbri and Menichini (2010) contend that there is a lack of empirical support for that above argument, which means the two views are more of a theoretical than practical relevance. Wilner (2000) shows that most firms use supplier trade credit despite the associated costs. Probably, trade credit may serve as a signalling dynamic between buyer and supplier and for the financial institutions. The supplier may interpret early payment as a sign of a customer’s creditworthiness and be willing to extend further supplier trade credit (Wilner 2000). Banks may interpret the extension of supplier trade credit as a signal of creditworthiness of their client firm and be willing to extend to the firm more short-term credit (Biais and Gollier 1997; Cuñat and Garcia-Appendini 2012). This is also supported by Aktas et al. (2015) and Kling et al. (2014) who demonstrated the signalling effect of the tenor of supplier trade credit on external investors.

Cash holding can also be used in a complementary or substitutive way. Bates et al. (2009) list several potential motivations for keeping cash. Precaution in one of the motives. Holding cash gives financial flexibility and slack (Bates et al. 2009; Han and Qiu 2007; Kim and Bettis 2014). Cash holding can complement the other components of working capital by providing a buffer against shocks in demand and supply.

The substitutive perspective of cash holding rather suggests it should be kept at a minimum. This is based on three main arguments (Demiroglu and James 2011). First, from an agency perspective, it overcomes the potential managerial problem of not investing cash in the most value-enhancing ways (Jensen 1986; Bertrand and Mullainathan 2003; Richardson 2006). Second, cash holdings typically earn less than the cost of short-term debt used to fund it. Therefore, accumulation of cash is unproductive (Demiroglu and James 2011; Picconi and Oler 2014; Nason and Patel 2016). Third, interest payments on credit lines are tax-deductible, while interest earned on cash holding is subject to tax (Demiroglu and James 2011). This leads to the reasoning that it is better to borrow cash as and when needed from credit providers rather than accumulate it in the first place (Gatev and Strahan 2006).

This study uses the CRSP/Compustat database for obtaining financial statements for listed North American manufacturing firms registered at the New York Stock Exchange (NYSE). The sample is collected for the fiscal period 2012–2019. The fiscal period of 2011 is included for variables requiring estimation of annual change. Standard Industrial Classification (SIC) codes are used for identifying manufacturing firms. Those firms registered in the SIC code span 2000–3999 are here defined as manufacturing firms. All financial statements are collected at the consolidated level. To capture firms with regular business activity over time, a minimum of 4 years’ financial statements are required.

Table 1 provides descriptive statistics about the firms included in the study. Frequency measures the number of unique firms within a given manufacturing industry. The Compustat database consists of 1.785 unique firms in the sample period (2012–2019) that are registered at NYSE. Filtering firms with a minimum of 4 years’ business history creates a sample of 589 unique firms, totalling up to 4255 firm-year observations. Typically, listed firms are large and may be different as compared to smaller firms (differences may stem from various factors, such as risk orientation, competitive advantage, financial flexibility, and access to capital). For instance, one qualifying criterion to be listed at NYSE is to ensure that the market value of public shares of the firm is at least $40 million. Therefore, the data sample may not be representative of all manufacturing firms in the U.S.

Table 2 provides a summary of the relevant variables included in the study, as well as their corresponding variable name in the CRSP/Compustat database. The following section will describe why these variables are included.

All variables of interest have been winsorized at the 1% level to mitigate the effect of abnormal values, i.e. outliers. The winsorization process was filtered by year. The final sample consists of an unbalanced panel of 589 firms (4255 firm observations). The median values for financial performance and components in WCM for the sample period were used to conduct fsQCA (see Table 3). Control variables were added when running panel data regressions.

The accounting return dimension of financial performance is examined by using both profitability and liquidity measures (Hamann et al. 2013). ROA is used as a measure of profitability and is often adopted in empirical WCM research as the outcome variable (see for instance García-Teruel and Martínez-Solano 2007; Baños-Caballero et al. 2010; Gill et al. 2010; Enqvist et al. 2014; Gama and Pais 2015; Lyngstadaas and Berg 2016). ROA is measured as EBIT/total assets. A liquidity (CFLOW) measure will be used for adding robustness to the findings. CFLOW is measured as net cash flow from operating activities/total assets.

WCM is here defined as OWC + FWC. OWC consists of four variables: inventory, accounts receivable, accounts payable, and other current assets. Inventory, accounts receivable, and accounts payable are three variables from the cash conversion cycle. However, WCM in its broadest sense is the difference between current assets and current liabilities. As such, this study will also include other current assets (for instance, prepaid expenses) to the measurement of OWC. All variables in OWC are scaled by net sales (Mun and Jang 2015). Compustat defines net sales as gross sales minus cash discounts, trade discounts, and returned sales and allowances. This measure excludes inter-company sales, interest and rental income, and non-operating income. Inventory is measured as total inventory/net sales (INV). Accounts receivable is measured as trade receivables/net sales (ACR). Accounts payable is measured as trade payables/net sales (ACP). Other current assets are measured as total current assets minus total inventory, trade receivables, and cash holding/net sales (CA_OTHER). FWC consists of two variables: cash holding and current liabilities (excluding accounts payable). Cash holding is measured as cash plus short-term investments/net sales (CASH). Current liabilities are measured as total current liabilities minus trade payables/net sales (CURR_LIAB).

The panel data regression will be run with several control variables. Control variables are added to diminish the risk of endogeneity due to model misspecification (i.e. omitted variable problem) (Chenhall and Moers 2007; Van Lent 2007; Larcker and Rusticus 2010; Zaefarian et al. 2017). Endogeneity creates concern, as it can lead to biased and inaccurate results, potentially leading to erroneous conclusions about systematic interdependencies. What happens is that the residual error term correlates with the independent variables, making the error term not random. The effect of the independent variable on the dependent variable becomes biased by other unobservable effects that correlate with the independent variable and that also explains the dependent variable (Chenhall and Moers 2007; Van Lent 2007; Larcker and Rusticus 2010; Zaefarian et al. 2017). Three types of control variables are added: debt structure, firm growth, and financial situation.

The debt structure is related to how working capital is financed. Current liabilities will most commonly be used for financing working capital because both are of a short-term nature. However, as leverage increases, the risk of default may increase as well. The cost advantages of financing working capital through short-term debt may quickly turn into cost disadvantages (Baños-Caballero et al. 2016). The controlling variables for debt structure are leverage, current assets ratio, and current liabilities ratio. Leverage is measured as total liabilities/total assets (LEV) (Martínez-Sola et al. 2013), current asset ratio as current assets/total assets (CA_RATIO) (Gama and Pais 2015; Lyngstadaas and Berg 2016), and current liabilities ratio as current liabilities/total liabilities (LIAB_RATIO) (Gama and Pais 2015; Lyngstadaas and Berg 2016).

Firm growth may indicate future sales and production and, potentially, affect the firm’s working capital requirements. Firm growth is measured as the annual change in the number of employees (GROW).

The financial situation affects a firm’s financial flexibility. Financial flexibility is understood here as the capability to capitalize on financial resources by taking preventive and exploitive actions (Byoun 2011). There are two different ways of understanding the constraints on financial flexibility: financial constraint and probability of default (financial distress). Financially constrained firms have less access to external and internal capital. The consequence may be forgoing positive net present value projects that would potentially negatively affect future production and sales. Following Almeida et al. (2004) and Faulkender and Wang (2006), the payout ratio is used as a proxy for financial constraint. While it may be argued that even profitable firms do not necessarily pay out dividends, Fazzari et al. (1988) show that financially constrained firms have a lower payout ratio compared to firms that are not financially constrained. Financially constrained firms also have higher investment sensitivity and fewer internal sources of funding (Arslan et al. 2006). This may also impede the firm in accessing the necessary sources for financing working capital. The payout ratio is measured as total dividends/net sales (FINCON) (Faulkender and Wang 2006).

The probability of default indicates the firm’s degree of financial distress and the probability that the firm may become insolvent. Since this study looks at the manufacturing sector, the Altman Z-score will be used to measure the level of financial distress (FINDIST). The use of the Altman Z-score is appropriate because it was originally developed for application in the manufacturing sector (Altman 1968, 2013). The Altman Z-score is a function of five variables (see Table 2). A higher Z-score indicates less probability of default.

Several researchers argue that fsQCA and statistical methods may be used in a complementary manner (Rihoux 2006; Ragin 2008; Schneider and Wagemann 2010). While some studies use fsQCA for analysis of both packages and systems (see for instance Misangyi and Acharya 2014), Ragin (2008, p. 190) argues that fsQCA is not an inferential method per se. Even though probabilistic criteria may be incorporated into fsQCA, the scientific assumptions are based on set theory (Ragin 2008). This means that fsQCA can inform the researcher about the necessity and sufficiency of how conditions are related to an outcome of interest, but it is not possible to draw statistical inferences (Rihoux 2006; Ragin 2008; Schneider and Wagemann 2010). This limits the possibility that fsQCA could serve to draw causal inferences about systematic interdependencies between conditions. Moreover, the number of conditions that may be analysed simultaneously with fsQCA is limited. The number of logical combinations between conditions increases exponentially at the rate of 2^k (k is the number of conditions), which swiftly raises the complexity of the analysis to an overwhelming degree (Ragin 2008). As a result, control variables commonly used in regression analysis are left out in fsQCA. Rihoux (2006, pp. 695–687) and Greckhamer et al. (2013) argue that set theory was initially developed for cross-section small N-samples, focusing on thick descriptions of the cases. The move towards using large N-samples and capturing time dynamics have enhanced the importance of complementing with other statistical methods in fsQCA-based research (Rihoux 2006; Greckhamer et al. 2013; Meuer and Rupietta 2017). Vis (2012) argues that although fsQCA and statistical methods contain different epistemological assumptions,⁵ they should be viewed as complementary rather than mutually exclusive analytical methods.

Table 4 contains the correlation matrix of the included variables. There are two correlations between 0.5 and 0.6. These are between ROA and FINDIST (0.51) and CA_RATIO and LIAB_RATIO (0.60). However, the maximum variance inflation factor (VIF) is 3.96. This is well below the suggested threshold of a maximum of 10 (Hair et al. 2013). This indicates that multicollinearity is not a problem in the study.

Table 5 presents the descriptive statistics of the variables included in the study. Inventory is further decomposed into its main four sub-components (raw materials, work in process, finished goods, and other inventory⁸). The inventory sub-components are reported according to how much of the total inventory they constitute, while the inventory variable is scaled by net sales.

A test for causal necessity was conducted before the fsQCA to ascertain the conditions that are necessary to produce the outcome and the sufficient (combinations of) conditions are those that will produce the outcome. If there is a necessary condition, it suggests that the outcome is a subset of the condition and the condition is not a subset of the outcome (Ragin 2008). This study uses an approach suggested by Dul (2016) for ascertaining if a single condition is necessary for producing the outcome of interest. This involves using a necessity consistency threshold of 0.9 (Dul 2016, p. 1519). The test included both presence and absence of conditions because it is not known ex-ante what produces the outcome. The threshold for the test for single necessary conditions ranged between 0.56 and 0.70, which is far below the suggested threshold. The conclusion is that there is no sole WCM component (condition) that is necessary for achieving high financial performance (an outcome of interest).⁹

The following results from fsQCA are presented with the use of the common fsQCA notation system (Fiss et al. 2013a; Misangyi and Acharya 2014; Bedford et al. 2016). A solid circle (●) shows the presence of a working capital variable (i.e. condition). A circle with a cross (⊗) represents the absence of a working capital variable. The presence of a condition means that a firm has a membership score above 0.5 from the calibration procedure. In other words, if a condition/outcome is present, it means that the value of the variable is above the median for the sample group. If a condition/outcome is absent, it means that the value of the variable is below the median for the sample group. Blank spaces mean that conditions are redundant for obtaining the presence of the outcome.¹⁰ To simplify the remaining presentation, ‘low’ will be used to denote the absence of a condition, while ‘high’ will be used to denote the presence of a condition. What is low/high is then relative to the other empirical cases (i.e. firms) in the data sample.

Table 6 shows the results from the fsQCA. ROA is used as the outcome variable, while the variables in OWC and FWC act as conditions. Each configuration lies close to the defined consistency level of 0.85 and far above the minimum consistency threshold of 0.75 (Ragin 2008). This means that the combination of conditions is sufficient for achieving financial performance. It is also important to split coverage into ‘raw’ and ‘unique’ coverage when there are many different paths to the same outcome (Ragin 2006b). While there are many sufficient configurations, there are only a few configurations that are empirically important in terms of having high coverage. Table 6 shows that configurations 1–5 and 11 hold the most empirically relevant solutions and therefore most relevant to focus on in further analysis.

This can be compared somewhat analogously when making an assessment of which variables in a multiple regression explain most of the variation in the outcome variable. In this context, each WCMP accounts for some of the explanations for achieving high financial performance. This is the raw coverage of WCMPs. Since several WCM components are represented in several packages, they overlap in terms of explaining high financial performance. At the same time, since some packages contain WCM components that are unique to that given package, they can also have unique coverage (Ragin 2008; Schneider and Wagemann 2010). This can, to some degree,¹¹ be compared when partialling out explained variation in multiple regression. It is possible to assess a variable’s unique contribution in explaining a variation in the outcome variable by creating a fully specified model (all relevant explanatory variables) and then recompute the model while excluding one explanatory variable of interest (Ragin 2006b).

For instance, we can compare configurations 5 and 7 in Table 6. They overlap to a great extent as both configurations contain high levels of inventory, low levels of accounts receivable, and low levels of other current liabilities. The difference is that configuration 5 has low levels of accounts payable, and configuration 7 has low levels of cash holding. The degree of overlap suggests that the coverage in the two configurations is counted twice. This yields a relatively high raw coverage. But both configurations has variables that is unique to the given configuration. Configuration 5 has low levels of accounts payable that is unique compared to configurations 1–4. This results in a unique coverage of 0.03. Configuration 7 has low levels of cash holding that is different from configuration 5, but low cash holding is also part of both configurations 4 and 6, so it is not unique in that sense. This result is an unsubstantial coverage of close to zero. Following the same approach as Ho et al. (2016), the interpretation for answering the second research question will not be based on WCMPs 6–10. The reason is that their unique coverage is approximately 0, meaning they have low empirical importance for explaining high financial performance.

Eleven different WCMPs are found to be sufficient for achieving high financial performance. Out of the 11 WCMPs, six WCMPs have a unique coverage above 0.00. These are WCMP 1–5 and 11. Based on the unique coverage, WCMP 1, 4, and 5 seem to have the greatest empirical importance. Of all firms in the data sample, 204 firms are associated with high financial performance relative to the other firms. However, as an empirical case can be represented in more than one WCMP, there are 129 unique firms in the fsQCA solution.

Table 7 provides descriptive statistics of average ROA, OWC, and FWC in the WCMPs. The total number of firms in the gross sample is in the fsQCA solutions (N = 204), while the net sample is the number of unique firms (N = 129). Those firms belonging to one of the packages are removed before estimating the total average for the remaining data sample (N = 460). The average ROA of those firms in the high-performing group is approximately three percentage points higher than that of the firms in the remaining data sample and seven percentage points above the firms in the low-performing group (negation of outcome). The high-performing group has a somewhat lower average WCM compared to the remaining data sample. That being said, there are large between-group variations. As can be seen in Table 7, firms belonging to WCMP 2 held on average 39% of net sales in WC, while firms belonging to WCMP 7 held on average only 9% of net sales in WC. Such large between-group variations indicate that some have relatively high levels of WC while others have relatively low levels. While WCMP 2 contains approximately 25% positive FWC, WCMP 7 contains approximately an FWC of negative 16%. While Mun and Jang (2015) argue that firms with positive OWC (OWC ≥ 0) and positive FWC (FWC ≥ 0) may have poor cash-generating capability, the accumulation of cash may be what is required among manufacturing firms. This is based on how cash holding may act as a precaution against future demand shocks and offer strategic flexibility (Bates et al. 2009) to create a positive effect on FWC.

Two research questions informed this study. The first research question was the following: How do manufacturing firms combine OWC and FWC into effective WCMPs that contribute to financial performance? The fsQCA results from Table 6 show 11 packages (configurations) that effectively achieve high financial performance. However, out of the 11 packages, six packages show the greatest empirical importance. This is based on the assessment of the fsQCA solutions and unique coverage derived from each WCMP. The six packages were used for developing six proposals to show how financial performance is affected by systematic interdependencies that exist among the components within and between OWC and FWC. Table 10 shows that while all six proposals were statistically significant, proposal 3 was only weakly supported. The six proposals seem to confirm that there are systematic interdependencies between OWC and FWC that act in a complementary manner to affect financial performance. As can be seen in Table 6, out of the six components constituting WCM (i.e. inventory, accounts receivable, cash holding, other current assets, accounts payable, and other current liabilities), accounts payable is the component that is identified as a core condition in the fewest of the WCMPs. This suggest that in order to achieve high financial performance, accounts payable from WCM seem to have the least empirical importance according to the fsQCA solutions. One possible reason is that there are few gains to be derived from accounts payable compared to competitors. For instance, if there exist strong industry norms for suppliers who offer credit terms, then there is little competitive advantage to be gained as all firms compete on equal supplier terms. Looking at other WCM components, such as cash holding, there is potentially more flexibility in conducting cash management. Some firms may maintain the lowest possible cash holding, while, at the same time, trying to ensure that short-term obligations are met. Other firms may use cash holding more strategically, such as for managing fluctuations in demand, or to provide investment for future growth. Such differences in the conduct of cash holding management can distinguish the firms that achieve high financial performance from that do not achieve such performance.

Looking closer at each proposal presented in Table 8 and the estimated results in Table 10, there seem to be several WCM tactics that can enhance financial performance. WCMP 1 in Table 6 corresponds to proposal 1 in Table 8. Keeping inventory low decreases invested capital at a given time but seem to offer relatively longer customer trade credit. From Table 7, it is apparent that these firms have WCM close to one-third of their net sales. Thus, they have a relatively high share of total WC in their balance sheets.

WCMP 2 in Table 6 corresponds to proposal 2 in Table 8. These firms pursue an even more conservative WCM approach, as Table 7 shows they have the largest share of WC against net sales (approximately 40%). These firms held on average almost one-quarter of net sales in net cash holding (FWC), which may be because they use cash holding to gain strategic flexibility. However, it is not possible to identify the reason for cash holding. These firms may be the ones with high growth potential, or they may have sold assets and kept a large cash holding in their balance sheet. The low levels of ACR may indicate that even though their total WC is relatively high, the firms belonging to this group pursue a more aggressive approach for collecting customer receivables. It may also be that these firms use factoring or similar approaches to avoid too much capital invested in ACR.

WCMP 3 in Table 6 corresponds to proposal 3 in Table 8. As can be seen from Table 7, these firms also seem to be holding higher levels of net cash. Their motive in having high cash holding may be to avoid the overcapitalisation of business operations.

WCMP 4 in Table 6 coincides with proposal 4 in Table 8. What is interesting is that these firms seem to benefit from components other than what is typically associated with WCM, such as the components that form the cash conversion cycle (inventory, accounts receivable, and accounts payable). As can be seen in Table 7, these firms keep one of the lowest net cash holdings as compared to the other groups. Based on the fsQCA solution in Table 6, this seems to be empirically important for achieving high financial performance. While cash holding may offer a strategic opportunity, decreasing total assets by keeping OWC low may also contribute to the increased financial performance. WCMP 5 from Table 6 corresponds with proposal 5 from Table 8. WCMP 5 seems to benefit from exactly those components that are involved in the cash conversion cycle. Table 7 shows that OWC is relatively high compared to other groups (OWC constitutes approximately 27% of net sales). This may facilitate production and sales, as they can both attract customers and achieve purchase discounts from large orders. At the same time, FWC is relatively low compared to other groups (FWC constitute approximately − 4% of net sales), indicating that these firms try to keep their current liabilities low. This is not necessarily a problem because they may be able to quickly convert their OWC into cash when required for meeting short-term obligations. These firms seem to not hold cash for strategic flexibility but rather focus on stimulating production and sales by their higher levels of inventory and accounts receivable. Since these firms achieve high financial performance, the higher level of inventory does not seem to be a problem (i.e. these firms do not suffer substantial losses from unsold products and goods). WCMP 11 from Table 6 coincides with proposal 6 from Table 8. WCMP 11 is somewhat similar to WCMP 5, but cash holding seems to have higher empirical importance in WCMP 11. These firms seem to be using a combination of tactics. Relatively higher levels of inventory may facilitate production and sales, while relatively lower levels of accounts receivable generate cash holding. This may be used both for meeting short-term obligations and creating future growth opportunities.

Looking at interdependencies within and between OWC and FWC in Table 11, the results vary. This may indicate that components in the different WCMPs form holistic systems but at different levels. While some systematic interdependencies exist at the lower levels (two-way interaction between variables), there are also the ones existing at a higher level (packages of OWC and FWC).

Examining the within interdependencies among components of OWC and FWC, it can be seen from Table 11 that inventory and accounts receivable are strongly complementary. This is not surprising because they are the two main components in the cash conversion cycle. Although statistically significant and complementary, the relationship between inventory and accounts payable is less clear. One possible reason is that the inventory level is related to customer sales while procuring inventory is related to suppliers. Customer sales and suppliers are two different parts of the supply chain streams and they operate independently of each other. However, accounts receivable and accounts payable are statistically significant and strongly complementary. This may indicate that firms do match their policies related to receivables and payables because the two components affect the self-financing period in the cash conversion cycle. What is perhaps surprising is the weak relationship between cash holding and current liabilities (excluding accounts payable). The challenge is that the measure for current liabilities contains more information than just short-term debt/bank loans. For instance, a large tax-deferred liability could create large fluctuations among firms in this variable. This makes the variable potentially ‘noisy’, which may explain its loss of significance. Since CURR_LIAB captures current liabilities that are not accounts payable, it constitutes a relatively large share when scaled by net sales. This can explain why it is identified as a core condition in most of the WCMPs.

Looking at interdependencies between components of OWC and FWC, cash holding seems to have the strongest complementary relationship with the other components in OWC. This lends more support for the conclusion that cash holding is a complement rather than a substitute for other sources of financing (Biais and Gollier 1997; Jain 2001). A possible explanation is that cash holding creates financial flexibility and slack that can be used in value-enhancing ways. As such, cash holding seems to have a strategic value (Bates et al. 2009; Han and Qiu 2007; Kim and Bettis 2014).

Three general remarks can be made regarding the fsQCA and panel data regression results. First, management accounting research has often assumed unifinality (Gerdin and Greve 2004). However, the different packages illustrate that there are various and equal ways to achieve high financial performance. Although components of OWC and FWC may be shown to be statistically significant individually, they do not necessarily contribute equally to financial performance when combined into a package. Some are more important than others in a given package. This distinguishes components as being core or redundant (Gerdin and Greve 2004; Bedford et al. 2016).

Second, identifying WCMPs is not necessarily the same as stating they are working capital management systems. While the six proposed systems were statistically significant at the most aggregate level, there were variations at lower levels of systematic interdependency.

Third, the level of systematic interdependency seem to vary. This may also shed some light on the challenges of relying solely on net-effect regression methods. Selecting and running regression models with only a few key variables can potentially omit relationship existing between variables at a higher level (Ragin 2006a; Woodside 2013). It is understandable to not run all possible two- or three-way interactions in the same model, as it has the potential of creating problem of multicollinearity. This can result in p-values changing from significant to non-significant, or coefficients changing direction (Woodside 2013; Huang and Huarng 2015). That being said, this study shows that combining fsQCA and panel data regression is useful because the two methods may, together and complementarily, add methodological strength. fsQCA can indicate which WCMPs exist and where to look for systems. Panel data regression can detect the strength and direction of the systematic interdependencies.

Robustness testing analyses the uncertainty of models and tests whether the estimated effects of interest are sensitive to changes in model specifications (Neumayer and Plümper 2017). The main idea is that uncertainty decreases if the robustness test models find the same or similar effects or point estimates from the analysis. Robustness checks become a methodological tool for increasing the validity of inferences (Neumayer and Plümper 2017).

Robustness testing consists of four steps. First, the optimal specification must be defined, which becomes the baseline model. Thereafter, potentially arbitrary assumptions about the baseline model must be identified because they could be potentially replaced by alternative model specifications. The alternative model specifications are changed in their assumptions, one at a time, and these are called the robustness test models. The estimated effects of the alternative model specification are compared with the estimated effects of the baseline model. This indicates the degree of robustness in terms of how much baseline and alternative models coincide with each other (Neumayer and Plümper 2017).

The types of robustness tests conducted in this paper are structured permutation tests and model variation tests (Neumayer and Plümper 2017). Structured permutation tests change specification assumptions repeatedly (such as sensitivity tests). Changes in the specification are not random variations but based on a rule on how much a parameter may be increased/decreased. The structured permutation tests can indicate how much a model specification has to change before the effect of interest becomes not valid, i.e. the boundaries of the observed effects in the baseline model (Neumayer and Plümper 2017).

Model variation tests change one model specification assumptions (such as about operationalisation or sample selection) and replace it with an alternative assumption. The model variation test is perhaps more suited when assessing model uncertainty with fewer discrete plausible alternatives (Neumayer and Plümper 2017).

fsQCA does a structured permutation test by conducting a sensitivity analysis of key parameters when performing a fsQCA. As the researcher’s choices affect the analysis and results, it is important to verify that the identified effects are not arbitrary upon researcher choices. Panel data regression uses a model variation test by changing sample selection (from unbalanced to balanced panel data set), and specification of the independent and dependent variables. The independent variable is altered by changing how WCM is operationalised. The dependent variable is operationalised as a form of liquidity measure rather than a profitability measure. This is based on how liquidity and profitability are two sub-dimensions of accounting-based financial performance measures. By showing the robustness across various dimensions of financial performance, it is possible to increase the validity of the financial performance concept (Neumayer and Plümper 2017).

Managers should be aware that there is no one-size-fits-all solution to WCM. Some firms keep more/less of either OWC or FWC while still achieving high financial performance. The differences in OWC and FWC may be viewed in terms of finding packages that create a good fit for the given firm. A common denominator is that OWC lies between 14% and 27% of net sales, while FWC lies between − 15% and − 25% of net sales. This may indicate that these firms have found the right balance between risk, liquidity, and return.

Looking closer at each component in OWC and FWC, some differences become evident in the relative importance in a WCMP. Inventory, accounts receivable, other current assets, and current liabilities are the most common components that managers should pay particular attention to. While some firms seem to benefit from a just-in-case inventory strategy (WCMP 5 and 11), others benefit from a just-in-time inventory strategy (WCMP 1 and 3). However, a component cannot be seen in isolation, as every component is shown to be systematically interdependent with other working capital components.

More surprising is that accounts payable (trade) is a core condition in only one WCPM. One possible reason is that industry norms exist for the types of supplier terms and agreements offered to buyers. This can indicate that there is less opportunity to distinguish oneself from competitors by achieving substantially better terms or agreements. Another reason may be that all firms actively manage their supplier relationship with substantial resources, making it difficult to gain a competitive advantage.

While the study offers empirical, methodological, and managerial contributions, there are some limitations that must be acknowledged. First, generalising from the manufacturing sector to other sectors must be done cautiously. One of the key ideas behind fsQCA is to maintain the integrity of each empirical case. This mainly limits the findings to empirical cases in the manufacturing sector among listed firms. For instance, in other sectors such as retail, keeping larger inventory volumes comes with high operational risk. Seasonal sales, trends, and competitor dynamics change rapidly, making buffer inventory rapidly outdated and unsold. That being said, there are also some similarities. Both manufacturing and retail sectors use accounts receivable and accounts payable, although for different reasons. Jain (2001) suggests that receivables and payables are typically offered in sectors with the supply side more concentrated compared to the demand side, i.e. retail sector, or in sectors with high monitoring costs, i.e. manufacturing sector. However, accounts receivable is most likely less used in the retail sector as direct payments are common. This implies that there is a higher operational risk of offering customers trade credit in the manufacturing sector. For instance, if some of the largest customers in the manufacturing sector do not pay according to terms and agreements, there may be severe consequences for liquidity and solidity. The probability of default may explain why accounts receivable is identified as a core condition in four out of six effective WCMPs and in most cases suggested to be kept low. The interrelationship between inventory, accounts receivable, and accounts payable can be different among various sectors. For instance, online retail is not unfamiliar with having a negative cash conversion cycle (such as Amazon), while this seems to be more uncommon in the manufacturing sector.

The accumulation of cash is found to be beneficial for high financial performance in half of the effective WCMPs. This is contrary to the assumption of cash as a potentially unproductive resource, as argued by Mun and Jang (2015) in the service sector. It is difficult to assess why cash holding seems desirable in the manufacturing sector. One possible argument is based on the precautionary motive (Bates et al. 2009).¹⁴

It is also difficult to move from exploratory to explanatory arguments about WCM as fsQCA moves quickly into overwhelmingly complex solutions. For instance, it is possible to split inventory in OWC into its constitutive parts (raw, work in progress, finished, other). However, adding just one more condition would increase the theoretical solutions from 64 to 128.

The methodological combination of fsQCA and panel data regression is not without its challenges; fsQCA is mostly used for cross-sectional analysis and is preoccupied with analysis of either high or low outcome. This is based on the assumption of asymmetric effects. Panel data regressions are perhaps more common for detecting linear net effects, and as such, they may not necessarily support fsQCA solutions. Future research can benefit from using even larger data sets, as each group in the fsQCA solution could be large enough for testing specific proposals. Since specific proposals is relevant for specific groups of firms, this can make it possible to isolate the firms where there exist systematic interdependencies.

Lastly, it is difficult to state both the temporal dynamics and causal direction of effects between WCM and financial performance. The temporal dynamic effects could either be immediate, delayed or expected (Neumayer and Plümper 2017). In this case, since WCM commonly has a short-term orientation, it is modelled as an immediate effect with a beginning and end in the same period as financial performance. The onset (immediate, delayed, or expected effects), duration, and evolution of the causal relationship between WCM and financial performance could be different in different periods, creating multiple temporal functional forms (Neumayer and Plümper 2017). In addition, it is not possible to state anything about reverse causality or bidirectionality. It could be that superior financial performing firms can establish better WC terms, creating a more efficient WCM, thus leading to further financial gains. This indicates that there is a lot of future research opportunities for exploring the complexity of WCM.