Source: own elaboration.

As observed in Table 2, only 20% of companies report the presence of a risk committee, and to a lesser extent (in 9% of cases) of a risk manager. However, it is quite common for Spanish listed companies to measure their risks (57%) and use the risk map in decision making while about 35% adopt formal risk management policies implemented in accordance with the COSO or ISO standard instead. Finally, interest risk is the most common form of coverage, followed by exchange and then credit risk.

Source: own elaboration.

Independent variables

Taking the above into account regarding the determinant factors for adopting an Enterprise Risk Management model or ERM, we have considered the variables listed below to specify our explanatory model for the determinants for implementing an Enterprise Risk Management system in the company.

Ownership variables

Firstly, we considered a continuous variable that represents the percentage of capital concentrated in individual investors or families (Famcont). As shown in Table 4, stakes in Spanish family-run businesses capital are very common, with an average value of 40%. In 10% of cases, controlled capital exceeds 85%, while in another 10% there is no presence of families or other individual investors in the shareholding.

Source: own elaboration.

The presence of other owners, in particular, investment funds (FIcont), has also been considered. Thus, the aim is to evaluate to what extent the presence of other relevant partners can influence the implementation of ERM. As previously stated, the existence of multiple relevant shareholders can positively influence risk taking and management (Mishra, 2011).

Variables related to the characteristics of company governance

Variables related to Corporate Governance of companies have also been considered specifically, information regarding the number of members that make up Boards of Directors (Totalmembers). In addition, the number of women that make up Boards of Directors (Boardwom), and of shareholders who are members of Boards of Directors (Sharboard) have been calculated. In general, companies opt for boards with an average of 14 members, although in some cases they may have 40 representatives. Women participate in virtually all boards, which are outnumbered by men so that out of the 14 members mentioned above, women only account for 1.5 on average. In more than 85% of cases, managers are shareholders, an element that can contribute to aligning interests. Finally, we should point out that a high percentage of the members of Boards of Directors (31.2%) are also company shareholders.

Table 5. Characteristics of Corporate Governance

Table 6. Summary of ownership and corporate governance variables

Source: own elaboration.

Control variables

The level of ERM adoption is also related to the size of the company, since there are economies of scale and minimum sizes required to implement risk management programmes and these can incur very high costs. The size of the company is usually related to the diversification level. Therefore, larger companies can use their market power to obtain greater benefits (Ang et al., 1985) and have a greater capacity to face the effect of economic changes (Sullivan, 1978; Hardwick, 1997). On the other hand, smaller companies are affected by a number of financial disadvantages that result in economic restrictions, greater difficulties in acquiring medium and long-term financing (Hellmann and Stiglitz, 2000) and a higher financial cost (Melle, 2001). Thus, there are studies that identify size and sector as relevant factors (Colquitt, Hoyt and Lee, 1999; Beasley, Clune and Hermansom, 2005; Pagach and Warr, 2011; Baxter, Bedard, Hoitash and Yezegel, 2013), although the work by Liebenberg and Hoyt (2003) is inconclusive. While Liebenberg and Hoyt (2003) found no significant differences in the use of ERM from one company to another of a similar size or industry, Beasley, Clune and Hermanson (2005) found that the companies with a higher implementation of a risk management policies had risk managers, were larger and operated in the financial, insurance or education sectors. As for Baxter, Bedard, Hoitash and Yezegel (2013), they found that larger and more diversified companies had better ERM programmes.

H5: Larger companies adopt ERM to a greater extent.

There are also differing theories which have the aim of explaining the relationship between liquidity and risk. In this regard, Bonfim and Kim (2012) show that the relationship can be either positive or negative. Based on the agency theory of free cash flow (Jensen, 1986), a positive relationship is established between both variables, with the argument that there is a greater risk of inappropriate investment when there is a very high level of liquidity; this is because managers prefer to retain excess funds and have greater discretion, which at times, can be materialized by way of the implementation of investment projects having a negative net current value. On the contrary, Logue and Merviue (1972) and Moyer and Chartfield (1983), postulate a negative relationship between liquidity and risk, maintaining that high liquidity indicates a low level of short-term liability and therefore a lower risk, bearing in mind that a higher liquidity reduces risk because there are more resources available to meet the company’s obligations (Edge, 1998). In the initial investigation of the correlation between both variables, Beaver, Kettler, and Scholes (1970) found a negative relationship with risk. However, the empirical studies of Borde (1998), Rosenberg and McKibben (1973) and Pettit and Westerfield (1972) showed liquidity ratios to be positively associated with risk. Nonetheless, the studies by Gu and Kim (1998) and Logue and Merville (1972) found no significant relationship between liquidity coefficients and risk.

Finally, profitability can also be decisive for the risk level, because following financial valuation models, a positive relationship between the profitability and risk of all investments has been confirmed both theoretically and empirically (Blume and Friend, 1973; Fame and MacBeth, 1973). If the company carries out aggressive strategies to increase profitability, which can increase risk (Edge, 1998), it seems logical that the riskiest investments are those that promise the highest rates of return. However, and as indicated by Bowman (1980), and Chen (2013), if the company is very profitable, there is a lower chance of incurring losses and bankruptcy. Bowman (1980) maintains that correlations between the accounting measures of profitability and risk are negative for most of the sectors analysed, that is, the most profitable companies have a lower risk. Consequently, the most at-risk companies obtain worse results on average. The same result is found by Fiegenbaum and Thomas (1988). This double relationship is justified due to the double attitude towards the risk that managers may take based on the prospect theory formulated by Kahneman and Tversky (1979). According to these authors, managers show a risk-averse attitude when the expected result is higher than desired, but they are prone to risk when the expected result is lower than desired.

The values of the control variables considered in the study are shown in Table 7 Summary of control variables. As can be seen, companies are heterogeneous in terms of size, liquidity, solvency and profitability.

Table 7. Summary of control variables

Source: own elaboration.

While, in general there is not a very high correlation between the variables considered.

Table 8. Correlation coefficient between control variables

Source: own elaboration.

Table 9 shows all the variables considered, as well as the expected sign for each case.

Table 9. Variables and initial hypotheses

Source: own elaboration.

Methodololgy

Most empirical studies carried out, which this paper belongs to, test the hypotheses established in the theoretical framework by means of conditional probability models. Therefore, we have chosen to apply a logit model to analyse the implementation of the variables related to ERM. This method establishes a linear relationship between the set of independent variables and the dependent variable. The dependent variable, which varies in the [0; 1] interval, is the logarithm of the ratio of opportunities or probabilities (odds ratio), probability of a certain event (default) and probability of its complement (no default).

We take P as the probability of the event occurring (value “1”) and 1-P, the probability of the complementary event occurring (value “0”). It is a Bernoulli or dichotomous variable whose mathematical expectancy represents the random result of default or no default. Taking the variable as Y, the probability that the company has some of the issues evaluated in the differing dependent variables is , where represents the explanatory or independent variables (Caballo, 2013). The logistic regression model approximates the probability of the event “1” with the value of the explanatory variable as follows:

[1]

thus,

[2]

If we now express the number of times that an event occurs versus how many times it does not occur (odds-ratio or probability ratio), we can deduce the following:

[3]

obtaining a linear relationship in both the independent variables and parameters Caballo, 2013).

The proposed model is as follows:

[4]

The regression signs are interpreted in the following way: a positive sign shows an increase in the probability that and a negative sign means the opposite. It is important to interpret the sign and not the magnitude since the latter must be done in terms of marginal effects that are calculated as:

[5]

Mean difference

Before analysing the results of the logit models, we performed an analysis of mean differences for some of the dependent variables. First, we chose the implementation of COSO as a proxy for ERM, where we observed that in general, companies that had implemented the standard have significant and positive differences in terms of ownership and the boards of directors. Thus, in general they have a higher percentage of institutional participation in capital, larger boards of directors, more women on boards of directors and more shareholders who are members of the boards.

Table 10. Mean difference considering the COSO variable as proxy of ERM

Source: own elaboration.

Source: own elaboration.

Source: own elaboration.

Source: own elaboration.

Results of the logistic regression

In Table 14 we can see that the Famcont variable is significant in six of the estimated models, showing a U-shaped relationship. This means that in general, a greater concentration of capital in the hands of family businesses leads to a lower likelihood of the company adopting risk management and control structures and policies. However, at very high concentration levels, it is observed, as before, that companies have more incentives to implement ERM. In the same way, companies with moderate levels of capital are less likely to have a structure with a risk committee or to hire a CRO, or even have fundamental management tools such as a risk map either. In addition, control of the company at moderate levels by a family member reduces the probability that the company implements a risk coverage programme. However, this situation changes when the concentration levels exceed approximately 50% of the capital. The results obtained are partially in line with hypothesis 1 and with the approaches by Brustbauer (2016), who showed that family businesses have less of an incentive to implement an ERM system and Paape and Speklé (2012), when they confirm that the coincidence of owners and managers makes implementing ERM less worthwhile because there are fewer agency problems. Therefore, our results support a non-linear relationship between the family control level and the ERM implementation degree, so at moderate levels there is a lower incentive to invest in risk management systems, whereas the propensity to implement ERM systems increases when the capital concentration level is very high.

On the contrary, the presence of an institutional investor is very significant in seven of the eight models studied, the adoption of ERM, the provision of a professionalized risk management structure and measurement tools, as well as the coverage of risks all showing a positive relationship. Thus, our results support hypothesis 2, which establishes a positive relationship between the presence of institutional investors and enterprise risk management. These results are in line with Mafrolla, Matozza and D´Amico, (2016), who postulate that the presence of institutional investors can lead to better risk management practices being applied in the company and CROs and Risk Committees being incorporated (Pagach and Warr, 2011). On the other hand, as it is a continuous variable, it also implies that the higher the level of control of the institutional investor, the greater the relationship. which again supports the approach by Mafrolla, Matozza and D´Amico (2016), when they maintain that if institutional investors have a higher stake, they perform in a professional way, improving the quality of management and therefore, of their risk system too. Equivalent results have been found by Brustbauer (2016) for the relationship between institutional participation and implementation of ERM systems.

The figure of the shareholder, who is also a board director, is a variable that has been significant in so many of the estimated models. Contrary to what is stated in the initial hypothesis, in the case of a director being a shareholder and, therefore, not independent, ERM is more likely to be adopted, risk management techniques incorporated and credit risk covered, the adoption of ERM is more positively affected. This situation could be explained by the fact that when the shareholder does not have control of the company, but participates in the board of directors, he or she may be interested in having a sophisticated risk system that allows managers to be better controlled. In addition, we have also observed that when the capital concentration level is very high, there is greater involvement in risk management. In this case, it is quite common for the shareholder to also be a member of the board of directors, whereby both roles would converge in boosting ERM. Size of the Board can also play a significant role due to its ability to control managers’ actions (Daud, Haron & Ibrahim, 2011). Our work only finds a significant relationship between size of the Board and the incorporation of a CRO, as it appears that a larger size makes it more difficult for a company to control its managers and leads to the incorporation of a CRO in order to monitor them. Finally, gender diversity does not seem to influence the characteristics of risk management which the company takes.

As for other more classic variables, the important role that size plays stand out, showing how important it is to be of a certain size in order for formal risk management processes to be undertaken.

Conclusions

We have evaluated the effect of ownership and corporate governance on the level of ERM implementation. This aspect has seldom been considered in previous literature, which in general has resorted to more conventional indicators. The results obtained show that the relationship between the level of family ownership concentration and risk presents a non-linear structure, in such a way that there is a reduction in the level of ERM implementation for moderate levels of ownership and an increase for higher levels. Thus, it seems that family businesses are less interested in implementing ERM, except when shareholders have greater control of the company, in which case they are more motivated to implement risk management systems. Similarly, when professional investors are present in the company, they boost management and control systems as well. In general, our results are in line with Mafrolla, Matozza and D´Amico (2016), who postulate that the presence of institutional investors could lead to better risk management practices being applied in the company and CROs and Risk Committees being incorporated. Furthermore, since it is a continuous variable, it also implies that this relationship is greater the higher the level of control the institutional investor has, so if institutional investors have a higher, stakes, they perform in a professional way, improving management standards and, therefore, its risk system too. Regarding the variables related to corporate governance, the importance of the characteristics of boards of directors in risk taking is confirmed. In this regard, we have observed that larger boards encourage risk managers to be hired and that the presence of shareholders on the board also acts as a catalyst for ERM to be adopted.

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Source: own elaboration.

Source: own elaboration.

Note: This Table shows the estimations of the logit model which were carried out by using different risk measures as dependent variables. Famcont is a representative variable of the percentage of capital in the hands of family or individual investors. Totalmembers is the number of members belonging to the board of directors, boardwom represents the percentage of women on the board and sharboard the percentage of shareholders who are also directors. Logta is the logarithm of the company’s total assets, ratliq is the liquidity ratio and rroa_ is profitability. Temporary and sectorial dummies have also been included. *Significant at 10%.** Significant at 5%.*** Significant at 1%.