Testing proposed systematic interdependencies

There are different levels that WCM can be operationalised from a balance sheet perspective, ranging from most aggregate (holistic) levels to looking at specific WCM components (variables). To test systematic interdependencies, a distinction will be made between the kinds of levels to which such interdependencies may exist.

Three different levels of interdependencies will be tested. Table 9 shows the most aggregate level of WCM with regard to financial performance. Table 10 summa-rises the six proposals that have been derived from fsQCA. Table 11 presents the most detailed level of interdependency, examining the pairwise interdependencies between specific WCM components.

Table 9 presents the most aggregate level of operationalising WCM. This oper-ationalisation is aimed at verifying that there exists an interdependency between OWC and FWC at the highest level. Here, it is separated along two paths of opera-tionalising OWC and FWC, denoted as WCM 1 and WCM 2. WCM 1 is in model 1a-1b and is operationalised as OWC (inventory + accounts receivable + other cur-rent assets − accounts payable) + FWC (cash holding − other curcur-rent liabilities).

WCM 2 is in model 2a–2b and is operationalised as Current assets − Current liabili-ties. It can be seen from Table 9 that WCMs are statistically and positively interact-ing with each other with regard to financial performance. This is independent of how WCM is measured (WCM 1 or WCM 2).

The semi-aggregate level used in Table 10 to test the six proposals is lower than that in Table 9. Each of the six proposals consists of just some components from both OWC and FWC, and when combined, they constitute OWC and FWC.

For instance, proposal 1 consists of INV and ACR from OWC, and CURR_LIAB from FWC. Each of the WCM components is included separately in the model and also includes an aggregate interaction between OWC (INV + ACR) and FWC (CURR_LIAB) (Table 10, model 1a and 1b). This is done to test the systematic interdependency between the relevant WCM components in proposal 1. This offers a deeper insight into whether a potential systematic interrelationship exists between specific components of OWC and FWC. This is especially relevant for those groups of firms that use the particular WCMP that is being tested. Table 10 shows that estimating a fixed effect (FE) or random maximum likelihood (RML)

panel data regression yields similar results. The OWC and FWC seem to form a complementary system, as all six proposals are statistically significant. That said, proposal 3 has the weakest relationship between OWC and FWC with financial

Table 9 Panel data regressions results (N = 589)

Two types of panel data regressions are run. First is FE panel data regression with year dummies and cluster robust standard errors. The second regression is RML panel data regression with year dummies and bootstrapped standard errors. ROA is the dependent variable. Model 1a and 1b measures WCM 1 = OWC + FWC. Model 2a and 2b measures WCM 2 = Current assets − Current liabilities. LEV is lev-erage; LIAB_RATIO is current liabilities ratio; CA_RATIO is current asset ratio; GROW is firm growth;

FINDIST is financial distress measured by Altman Z-score; FINCON is financial constraint. Obs. is the number of observed firm years. Variable coefficients are reported with t-statistics in parenthesis for FE-regression and z-statistics for RML FE-regressions. Variables with *, **, and *** denote significance at the 10%, 5%, and 1% levels, respectively

Dep. var.: ROA 1a 1b 2a 2b

Table 10 Panel data regression results from testing proposals between OWC and FWC (N = 589) Dep.var.: ROA1a1b2a2b3a3b4a4b5a5b6a6b FE-modelRML- modelFE-modelRML- modelFE-modelRML- modelFE-modelRML- modelFE-modelRML- modelFE-modelRML-model INV

The dependent variable is ROA; INV is inventory; ACR is accounts receivable from trade; ACP is accounts payable from trade; CA_OTHER is other current assets; CASH is cash holding; CURR_LIAB is current liabilities excluding accounts payable. Proposals 1–6 are created as interactions between OWC and FWC, dependent on which component is proposed to constitute a system (see Proposed Systematic Interdependencies section). Variables with *, **, and *** denote significance at the 10%, 5%, and 1% levels, respectively Table 10 (continued) Dep.var.: ROA1a1b2a2b3a3b4a4b5a5b6a6b FE-modelRML- modelFE-modelRML- modelFE-modelRML- modelFE-modelRML- modelFE-modelRML- modelFE-modelRML-model Systematic interdependency (OWC * FWC) Proposal 1

0.20* (1.76) 0.16* (1.81)

Proposal 2

0.29*** (5.41) 0.23*** (3.92)

Proposal 3

0.17* (1.80) 0.09 (0.98)

Proposal 4

0.23** (2.43) 0.24*** (2.89)

Proposal 5

0.20* (1.88) 0.20** (2.19)

Proposal 6

0.20*** (4.80) 0.16*** (3.47)

Constant

0.07*** (3.70) 0.07*** (3.98) 0.08** (4.76) 0.07*** (4.97) 0.06*** (3.42) 0.06*** (3.47) 0.06*** (3.21) 0.05*** (3.86) 0.07*** (3.68) 0.06*** (4.12) 0.08*** (4.94) 0.07*** (5.00)

Year fixed?Yes Obs.4255 R20.390.400.390.390.400.40

performance. The strongest effects from the combinations of OWC and FWC seem to come from proposals 2, 4, and 6.

What could be noted is that the net effect from CURR_LIAB is mostly not sig-nificant in different estimations of models in Table 10. However, the proposals that contain CURR_LIAB as a part of FWC have a statistically significant effect.

Table 11 Testing pairwise systematic interdependencies within and between OWC and FWC

First column shows pairwise interdependencies between specific WCM components. Second column shows the proposal (1–6) from which the pairwise interdependencies are derived from. The third and fourth columns show the panel data regression estimates (FE-model and RML-(FE-model), with each row and cell representing an estimated model. The same model is recomputed by altering the included potential interaction term between WCM components that is of interest. Variables with *, **, and *** denote significance at the 10%, 5%, and 1% levels, respectively

Dep. var.: ROA Proposal 1a 1b

FE-model RML-model

This may indicate that CURR_LIAB is important when viewed as a part of a sys-tem with several other WCM components, although it is not statistically significant independently.

Table 11 presents pairwise systematic interdependencies between specific WCM components. This is done by conducting pairwise comparisons, either within or between specific components of OWC and FWC. This is the lowest possible level of interdependency. While the proposals may not be statistically significant, it may be that some of the components are still interdependent. This is also relevant from the managerial point of view because it signals which components need to be seen as parts of the same decision-making and control process. Since the same pairwise combinations of WCM components are represented in different proposals, the sec-ond column in Table 11 indicates the proposal from which the pairwise interrela-tionships are derived. Table 11 contains the same control variables as Table 10, but only interaction effects between the pairwise combinations are reported to conserve space. Each cell in columns 3 and 4 in Table 11 equals a separate regression estima-tion. From Table 11, it may be noted that while several components from OWC and FWC may combine to form a system, they are not necessarily pairwise interdepend-ent. Looking at CURR_LIAB, the systematic interdependency is not statistically significant when running in pairwise interaction within FWC (between CASH and CURR_LIAB), or with other components of OWC (ACR and ACP). One possible reason for this is that the type of systematic interdependency occurs at the high-est (Table 9) and semi-aggregate (Table 10) levels, while the lowhigh-est level is mostly not supported. This may indicate that the effect from OWC and FWC on financial performance is created when they are combined into more holistic systems, as sug-gested by the WCMPs.

The main impression is that the level that WCM is studied affects our understand-ing of it as a decision-makunderstand-ing and control system. WCM consists of both loosely connected components, to a more all-embracing system of interdependencies. That said, it could be that different functional forms exist at a lower level which is not captured in the estimations, resulting in non-significant effect. It could also be that the specific pairwise effects with variables such as CURR_LIAB are only valid for those group of firms that use the WCMPs that contains this component.

5 Discussion and conclusion