Abstract
This article analyses enterprise risk management (ERM) in small and medium-sized enterprises (SMEs) by developing a structural model based on a survey questionnaire. Preconditions for ERM implementation, applied ERM approaches and their effects on strategic orientation are derived. The results suggest that SMEs follow either an active or a passive ERM approach, which affects their strategic orientation; a passive approach results in a defensive strategy and an active approach, an offensive strategy. Firm size, sector affiliation and ownership structure influence the implementation of ERM. The applied conceptualization of ERM may help SMEs adjust to a changing environment to gain strategic advantage thus, increasing competitiveness and business success.
Introduction
Organizations operate in increasingly dynamic, complex and unpredictable contexts (McMullen and Shepherd, 2006), and thus, exploring and managing related risks are a prerequisite (Alchian, 1950). Risk management has considerable implications for competitiveness and business; it enables, for instance, the development of a strategy to reduce potential losses while exploiting windows for opportunity (Radner and Shepp, 1996).
Many firms lack the resources and reliable mechanisms to support their risk-management activity and this is particularly notable for small and medium-sized enterprises (SMEs). While larger firms tend to manage risks collectively, for example, through expert boards of directors, within SMEs this task is more often undertaken by the firm owner possibly supported by a small management team (Watt, 2007). Entrepreneurs are generally described as ‘risk takers’ (Cantillon, 1755), as ‘the ultimate owners of business enterprises, those who make the final decision and assume the risks involved in such decisions’ (Ely and Hess, 1893: 95), and as those who organize, manage and assume the risks of a business venture (Tate et al., 1982). An entrepreneur’s perception of risks and the ability to manage them, contingent upon personal and company-related resources, influences the respective risk-management approach (Herbane, 2010; Leopoulos, 2006; Nocco and Stulz, 2006).
In recent years, a paradigm shift has occurred regarding the view of risk management. Instead of evaluating risks from an individual perspective, the trend is toward an all risks-encompassing perspective – commonly referred to as enterprise risk management (ERM). The aim of this holistic framework is the identification, assessment and monitoring of all threats and opportunities facing a firm (Meulbroek, 2002; Pagach and Warr, 2011). As such, ERM promotes increased risk-management awareness supporting a firm-wide risk-management approach, translating into mature operational and strategic management decisions (Nocco and Stulz, 2006). Working from theoretical risk concepts, this framework provides broad guidance, suggesting key principles but leaving details to the firm itself.
This article analyses ERM in SMEs by developing a structural model based on a postal questionnaire. Preconditions for ERM implementation, applied ERM approaches and their effects on strategic orientation are derived; thus, we offer an initial and explorative attempt to model ERM in SMEs. We contribute to the ERM literature as follows: first, we analytically explore ERM in SMEs; second, the study adds a European perspective to the literature. Finally, ERM is conceptualized in a simple but concrete manner. To date, existing studies address neither applied ERM approaches nor differences between firms; they largely focus upon large North American firms while studying ERM at a high level of aggregation (Beasley et al., 2005; Gordon et al., 2009; Kleffner et al., 2003). In addition to the theoretical contributions, practical implications are indicated. We commence by exploring the theoretical background to this literature; subsequently, the empirical study is described. We conclude with an outline of our contribution followed by limitations and implications.
Theoretical background
Risk is a multifaceted concept (Janney and Dess, 2006) clouded by polysemous meanings, among which there is limited agreement. This may be due to different measures seeking to capture different phenomena that all carry the same name: risk. Risk is usually associated with expected adverse effects; however, the concept also embraces expected positive effects. In classical decision theory, risk is the probabilistic uncertainty of outcomes stemming from a choice and regarded as reflecting variation in the distribution of potential outcomes, their probabilities and subjective values (Dickson and Giglierano, 1986; March and Shapira, 1987).
Although theoretical risk concepts are extensive, they are weakly grounded in the particular realities of entrepreneurs (Hagigi and Sivakumar, 2009; Janney and Dess, 2006; Kahneman and Tversky, 1982; Pablo, 1999). According to MacCrimmon and Wehrung (1986) and March and Shapira (1987), three differences are apparent. First, most entrepreneurs do not treat uncertainty related to positive outcomes as an important factor in risk. From a decision theory perspective, a risky option is one with a wide range of possible outcomes; from an entrepreneur’s perspective, however, a risky option is one that entails the threat of a poor outcome. Second, for most entrepreneurs, risk is not primarily a probability concept. Although they see uncertainty as a factor in risk, the extent of potential negative outcomes appears more influential. Finally, most entrepreneurs strive for precision in estimating risk, but show little desire to reduce it to a single quantifiable measure. From these findings, it appears that it is an entrepreneur’s personal interpretation and evaluation of risks that is relevant for risk-management activity. This means that perception of risks and the ability to manage them are likely to influence the respective risk-management approach adopted (Child, 1972; Huang, 2012; Lindsay and Norman, 1977).
Entrepreneurship risk management
It has been recognized that risks are no longer merely threats to be avoided, but, in many cases, opportunities to be embraced; instead of evaluating risks from an individual perspective, the trend is towards an all risks–encompassing perspective of risk management (Beasley et al., 2005; Liebenberg and Hoyt, 2003). ERM is an evolving process that is
effected by an entity’s board of directors, management and other personnel, applied in strategy setting and across the enterprise, designed to identify potential events that may affect the entity, and manage risk to be within its risk appetite, to provide reasonable assurance regarding the achievement of entity objectives. (Committee of Sponsoring Organizations of the Treadway Commission (COSO), 2004)
The aim of this holistic framework is the identification, assessment, and monitoring of all threats and opportunities facing the firm (Meulbroek, 2002; Pagach and Warr, 2011).
ERM promotes increased risk-management awareness and supports a firm-wide risk-management approach, translating into mature operational and strategic management decisions (Nocco and Stulz, 2006) and, hence, offers competitive advantage (Meulbroek, 2002; Stroh, 2005). Thus, ERM supports the development of a business strategy to reduce potential losses and exploit windows for opportunity (Beasley et al., 2008; Gordon et al., 2009; Hoyt and Liebenberg, 2011).
The ERM framework draws from theoretical risk concepts to provide broad guidance, suggesting key principles but leaving details to the adopting firms. Although theoretical guidelines are useful for SMEs, many face open-ended questions in implementing ERM with little concrete guidance at the operational and instrumental level. Consequently, ERM approaches differ across such firms (Beasley et al., 2005). Although various approaches are applied in practice, the existing literature studies ERM at a high level of aggregation; several studies rely on data on the appointment of a Chief Risk Officer (CRO) as their sole indicator for the implementation of ERM (Beasley et al., 2008; Liebenberg and Hoyt, 2003; Pagach and Warr, 2011). Others use ordinal scales ranging from, ‘no plans exist to implement ERM’ to ‘complete ERM is in place’ to capture applied ERM approaches (Beasley et al., 2005; Paape and Speklé, 2012). While this research addresses neither the particularities of applied ERM approaches nor differences in approaches, it does demonstrate an increasing interest in analysing how ERM is applied in practice (Mikes, 2009; Wahlström, 2009). In addition to this analysis, two other streams are evident: the first, seeking to identify the advantages of ERM (Gordon et al., 2009, Hoyt and Liebenberg, 2011), and the second, exploring the preconditions for ERM implementation (Beasley et al., 2005; Pagach and Warr, 2011). Such studies rely on North American data for listed firms with Paape and Speklé (2012) as an exception when exploring European SMEs but their study neglects firms with annual revenues of less than €10 million and fewer than 30 employees. For a detailed overview of previous studies, see Table 1.
Previous ERM studies.
ERM: enterprise risk management; CRO: Chief Risk Officer; CEO: Chief Executive Officer; CFO: Chief Financial Officer; SEC: Securities and Exchange Commission.
Notes: 1, financial leverage; 2, company size; 3, finance sector; 4, energy sector; 5, education sector; 6, CRO appointment; 7, listed company; 8, board independence; 9, auditor; 10, CEO and CFO support; 11, cash ratio; 12, industry competition; 13, board encouragement; 14, institutional ownership; 15, owner management; SEC, US Securities and Exchange Commission filings. Characters in parentheses indicate a positive (+) or negative (−) influence on ERM implementation.
Indicates contingency upon certain firm characteristics.
Preconditions for ERM implementation
Regarding preconditions associated with ERM implementation, existing studies focus on, for example, firm size and sector affiliation, CRO appointment, auditor presence, financial leverage and ownership structure (Hoyt and Liebenberg, 2011; Liebenberg and Hoyt, 2003; Paape and Speklé, 2012; Pagach and Warr, 2011). However, no general theoretical framework on key preconditions is documented in the literature (Gordon et al., 2009). Accordingly, this article focuses on preconditions frequently applied in previous research and meaningful for analysis in the context of SMEs (Kelliher and Reinl, 2009). The respective literature suggests key issues to be firm size, sector and ownership structure.
Firm size
As size increases, the scope for threatening events is likely to differ in nature, timing and extent. This implies the need for a comprehensive risk-management strategy (Gordon et al., 2009). Larger firms will profit from greater resources and economies of scale when operating ERM. Therefore, it is noted that larger firms are more likely to implement ERM than their smaller counterparts (Beasley et al., 2005; Hoyt and Liebenberg, 2011; Pagach and Warr, 2011).
Sector
Operating sector affects ERM implementation as, on the one hand, regulated industries have been at the forefront of ERM implementation, for example, financial industry (Beasley et al., 2005), and on the other hand, the risk of not earning a sustainable level of profit encourages ERM within highly competitive sectors. The level of competition should be positively related to the value of ERM; thus, firms operating in more regulated/competitive sectors are more likely to implement ERM (Kleffner et al., 2003; Paape and Speklé, 2012).
Ownership structure
The implementation of ERM cannot succeed without strong support from owners and their awareness of its value (Beasley et al., 2005; Brustbauer and Peters, 2013). Accordingly, where an owner-manager dominates or where there are no professional manager, ERM is likely to be lower. This may be particularly evident in family owned firms (Lovata and Costigan, 2002; Paape and Speklé, 2012).
Effects of ERM approaches on strategic orientation
While the importance of ERM is an important lesson arising from the financial crisis of the early 21st century (Herbane, 2010; Mikes, 2009), the effects of ERM have only recently been explored (Beasley et al., 2008; Hoyt and Liebenberg, 2011; Pagach and Warr, 2011). ERM is about recognizing opportunities during upturns and protecting against risks during downturns (COSO, 2004). Firms with a wide range of investment opportunities are likely to benefit from negotiating a more accurate risk-adjusted rate (Meulbroek, 2002); if this is so, investments should be allocated more efficiently and returns increased. ERM should, therefore, increase competitiveness and business success (Nocco and Stulz, 2006). Moreover, firms that implement ERM note the benefits of improved information efficiency and better strategic positioning (Kleffner et al., 2003).
We explore this argument by analysing the firm’s strategic orientation to gain competitive advantage. Strategic orientation is defined as the strategy to adapt to the environment to gain a more favourable alignment (Miles and Snow, 1978). Strategic orientation can take two forms: first, defenders who adopt defensive strategies in terms of risk-taking, experimentation, opportunity-seeking and initiating actions, and second, prospectors who adopt a more offensive strategy (Covin et al., 2000; Miles and Snow, 1978; Miller and Friesen, 1982). Defenders compete on the basis of price, quality, delivery or service, and put a strong emphasis on maintaining existing markets. It follows that defenders tend to be reactors, that is, they act based on the experiences of others and have a preference for the short-term. Prospectors however, tend to be analysers, that is, they are more innovative, market-oriented and have a preference for the long-run (Laforet, 2008). The primary capability of prospectors is finding and exploiting strategic opportunities. Following a full analysis of directional strategy and how to compete, they seek new product and market opportunities and focus on new and efficient production and process technologies. Prospectors profit from their agility to adapt and respond rapidly and creatively to changing business conditions (Laforet, 2008; Zhou et al., 2005). In order to be able to categorize SMEs as defenders or prospectors, three key aspects of strategic orientation are analysed: market expansion, product introduction and investments in production and process technologies (Danneels and Kleinschmidt, 2001; Dyer and Song, 1998; O’Regan and Ghobadian, 2005).
Towards a structural model of ERM in SMEs
The extant literature suggests that ERM activity influences strategic orientation and its implementation depends on certain preconditions. An empirical study is conducted in order to analyse these theoretical assumptions. From the results, a structural model of ERM in SMEs is proposed. The model builds on the theory developed in the respective streams of the ERM literature and on the analytical framework suggested by Gordon et al. (2009).
Methodology
The empirical study was undertaken using information gathered from firms located in Tyrol, a state in western Austria. Data collection was supported by the Austrian Chamber of Economics (ACE); firms included in the sample were randomly drawn from the ACE database that comprised 43,386 firms in all nine Tyrolean counties and from seven sectors. According to the Chamber’s classification, the respective sectors were skilled crafts, manufacturing, trade, finance, transport, tourism and consulting.
Questionnaire
The questionnaire was conducted in German; a qualitative pilot study and a review of the literature were conducted to test the schedule (Khattab et al., 2007; Reichmann, 2001; Romeike, 2005, Schmitz and Wehrheim, 2006). The focus of the study centred upon various risk subject areas; firm and respondent characteristics were stated at the end of the questionnaire schedule.
ERM activity in SMEs was analysed along three dimensions: risk identification, risk assessment and risk monitoring. Each of these dimensions comprised four items, termed ERM classifying items (COSO, 2004; Laforet, 2008; O’Regan and Ghobadian 2005). 1 Responses were given on a Likert scale ranging from 1 (‘strongly disagree’) to 7 (‘strongly agree’); the same scale was applied for items characterizing strategic orientation. The items covered key aspects of strategic orientation: market expansion, product introduction and investments in production and process technologies (Danneels and Kleinschmidt, 2001; Dyer and Song, 1998; Ozsomer et al., 1997). ERM classifying items and strategic orientation items were taken from the respective literature, but adapted for the purpose of this study in a simple but concrete manner. The complete wording of the items and response alternatives is given in Table 2. Firm size, sector and ownership structure were considered preconditions for ERM implementation (Hoyt and Liebenberg, 2011; Paape and Speklé, 2012; Pagach and Warr, 2011).
Wording of the ERM and strategic orientation items.
ERM: enterprise risk management.
Notes: Responses for ERM and strategic orientation items were given on a 7-point Likert scale. Response alternatives were as follows: 1 (‘strongly disagree’), 2 (‘mostly disagree’), 3 (‘somewhat disagree’), 4 (‘neither agree nor disagree’), 5 (‘somewhat agree’), 6 (‘mostly agree’) and 7 (‘strongly agree’).
The questionnaire was delivered by post; entrepreneurs – who fulfilled the definition outlined by Brockhaus (1980) – were asked to complete it. 2 The sample corresponded to the database population with regard to sectoral structure and regional influences. The questionnaire was sent to 4339 firms, representing 10% of the database population; responses were provided by 358 entrepreneurs. This corresponds to an 8.25% response rate and is largely in line with that of comparable studies (Ahmed et al., 2002; Hagen et al., 2012). The focus on SMEs restricted the sample to 341 respondents; to qualify for inclusion, no missing responses were allowed. Of the 341 questionnaires, 311 met this prerequisite.
The sample is representative with regard to the percentage distribution over sectoral structure, county and personal characteristics compared to the database population values stated by the ACE. While the percentage distribution over the county is consistent with the population of SMEs, our sample shows some deviations from the database population; those in the finance sector are overrepresented, while those in the tourism sector, micro firms and family firms are underrepresented. We drew random samples according to the distribution in the population in order to analyse the sensitivity of results. As the findings do not differ significantly, we deem it to be representative.
Method
In order to identify applied ERM approaches in SMEs, a cluster analysis was conducted. Ward’s clustering algorithm with squared Euclidean distance was performed using the ERM classifying items. This algorithm allows for various numbers of clusters, that is, it does not force firms into a specific number of clusters. As the number of clusters is not given a priori, coefficients in the agglomeration schedule are used to determine the cluster solution. 3 The stopping rule for cluster aggregation calls for changes in heterogeneity between cluster solutions to be assessed, represented by the agglomeration coefficient. The agglomeration coefficient is particularly amenable as small coefficients indicate the aggregation of fairly homogeneous clusters, while joining two diverse clusters results in a large coefficient. Since each aggregation of clusters results in increased heterogeneity, the aim is to focus on large percentage changes in the agglomeration coefficient to identify cluster aggregations that are markedly diverse. To determine the optimal number of clusters, the first largest percentage change in agglomeration coefficient will be used (Hair et al., 2006).
Because of the explorative nature of Ward’s clustering algorithm, the validity of the 2-cluster solution is checked: First, distributions of each of the ERM classifying items are analysed. Second, a discriminant analysis (DA) is conducted. DA investigates whether clusters are classified as predicted. This is called the classification accuracy, that is, the ratio of the number of agreements between predicted group membership with respect to cluster membership. The sample is split into an estimation sample and a validation sample. The estimation sample is used to compute the discriminant function, while the validation sample is used to construct a classification matrix containing the number of correctly and incorrectly classified firms.
To analyse the relationship between ERM classifying items (dependent variables) and preconditions for ERM implementation, multivariate analyses of variance (MANOVAs) are conducted. Analyses of variance (ANOVAs) investigate whether there are differences in ERM classifying items that can be ascribed to size, sector and ownership structure. The Bonferroni–Holm correction is applied to calculate statistically significant differences between the groups. The significance level is kept at 5%. Analyses are performed separately for each of the ERM classifying items.
Results
The results of the survey questionnaire suggest the structural model of ERM in SMEs proposed at the end of this section. Preconditions for ERM implementation, applied ERM approaches and their effects on strategic orientation are derived.
Sample description
The sample comprises of 204 micro firms (fewer than 10 employees), 71 small firms (10–49 employees) and 36 medium firms (50–249 employees); 187 were family-owned and 115 non-family-owned firms. 4 Almost half of the firms were located in the greater Innsbruck area, Innsbruck being the capital of the State of Tyrol. The sample comprised of 65 tourism, 11 finance, 30 consulting, 11 manufacturing, 78 trade, 20 transport and 82 skilled crafts firms. With regard to personal characteristics, women-owned firms account for approximately 37% of the sample, 5 a little more than 70% of the entrepreneurs were 35 years or older, married or in a relationship and had at least one child.
Applied ERM approaches in SMEs
In our sample, Ward’s clustering algorithm with squared Euclidean distance indicates a 2-cluster solution, as the aggregation step between two clusters and one cluster is accompanied by the first largest percentage change in agglomeration coefficient, that is, 24.1%. This suggests aggregation of two diverse clusters, indicating that the optimal number of clusters is two. Based on the respective risk-management activity, the first cluster is termed ‘active ERM approach’ (N = 101), whereas the second is termed ‘passive ERM approach’ (N = 210). Table 3 shows the cluster means obtained for each of the 12 ERM classifying items. Higher cluster means for a given item imply greater risk-management activity and vice versa. Cluster means of the 12 ERM classifying items are additionally visualized in the form of a star chart (see Figure 1).
Cluster means of the ERM classifying items.
ERM: enterprise risk management.
Notes: Cluster means outline the average over the Likert scale ranging from 1 (‘strongly disagree’) to 7 (‘strongly agree’). Standard deviations are given in parentheses. Comparisons between the clusters were made using the Mann–Whitney U test. The results show statistically significant differences in all ERM classifying items between clusters (p < 0.01).
ERM classifying items star chart.
The items are distributed in a dichotomous manner, thereby lending support to the 2-cluster solution. Regarding the DA findings, the validation sample reveals that overall 88.1% of the firms are correctly classified. This hit ratio differs negligibly from that of the sample used to estimate the discriminant function (90.4%), thus demonstrating the validity and generalizability of the 2-cluster solution. Furthermore, the hit ratio of the two clusters is analysed separately. The results are 92.1% for Cluster 1 (active ERM approach) and 86.2% for Cluster 2 (passive ERM approach). Active ERM firms are classified with slightly better accuracy than are passive ERM firms. Furthermore, the means of probabilities of predicted group membership, 0.889 for Group 1 (standard deviation (SD) = 0.145) and 0.919 for Group 2 (SD = 0.130), show that DA classifies firms accurately and convincingly; accordingly, there is support for the 2-cluster solution for applied ERM approaches in SMEs.
From these results, certain trends can be readily identified. First, cluster means of ERM classifying items increase significantly when moving from the passive to the active approach. Second, the passive ERM approach shows rather low risk-management activity. It is characterized by particularly low risk-identification activity and moderate levels of risk assessment and risk monitoring. The identification of risks by experts and the report on identified risks are not viewed as important issues. Instead, the emphasis focuses on surveying customer satisfaction, comparisons with competitors, definition of business objectives and checking work on completion. Finally, the active ERM approach displays rather high risk-management activity, apart from the identification of risks by experts. It is characterized by a moderate level of risk identification and by high risk-assessment and risk-monitoring activity. The results indicate that the significance of risk identification is less important than that of risk assessment and risk monitoring for SMEs applying either an active or a passive ERM approach, albeit on different levels.
Preconditions for ERM implementation
MANOVA results are statistically significant for each of the three preconditions (p < 0.01 for Hotelling’s trace and Pillai’s statistics). We also controlled for county and entrepreneur characteristics. None of the control variables has a statistically significant influence on the ERM classifying items. The p-values from Levene’s test for all ANOVAs are greater than 0.10, thereby confirming the null hypothesis of homogeneity of variance. Standardized residuals are normally distributed.
Firm size
ANOVA results are statistically significant for nine ERM classifying items (p < 0.05). Medium-sized enterprises are more likely to focus on risk identification by experts and the report on identified risks than micro firms. The same holds for comparisons with competitors and for the implementation of business plans, risk-assessment programs and contingency plans. However, checking completed work is more pronounced among micro than medium-sized firms. Differences between small and micro firms are limited: small firms are more likely to seek professional advice, compare themselves with competitors and act according to a business plan than are micro firms. No significant differences emerge between small and medium-sized firms.
Firm sector
ANOVA results are statistically significant for two ERM classifying items: ‘Identification of risks by qualified employees’ (p < 0.05) and ‘Check work when finished’ (p < 0.01). Checking work is less pronounced among SMEs in the transport and tourism sector than among those of the skilled crafts sector. No significant differences emerge for risk identification by qualified employees.
Ownership structure
ANOVA results are statistically significant for four ERM classifying items (p < 0.05). Non-family firms are more likely to implement risk-assessment programs and contingency plans and also place a stronger emphasis on risk identification by qualified employees.
Effects of applied ERM approaches on strategic orientation
Table 4 shows the cluster means obtained for the strategic orientation items. The mean of each of the three strategic orientation items increases significantly when shifting from the passive to the active ERM approach (p < 0.01). Both items ‘Expansion to new markets’ and ‘Introduction of new products’ exhibit a mean of less than four in the passive ERM approach. Interestingly, however, the mean for the item ‘Investments in new production and process technologies’ is greater than 4 for both approaches, that is, SMEs regard maintenance investments as a central strategic issue.
Cluster means of the strategic orientation items.
Notes: Cluster means outline the average over the Likert scale ranging from 1 (‘strongly disagree’) to 7 (‘strongly agree’). Standard deviations are given in parentheses. Comparisons between the clusters were made using the Mann–Whitney U test. The results show statistically significant differences in all ERM classifying items between clusters (p < 0.01).
Towards a structural model of ERM in SMEs
The results of the survey questionnaire are used to propose the model shown in Figure 2. The results lend support to the basic causal structure of the model. Corresponding to questionnaire sample and design, SMEs follow either an active or a passive ERM approach. Data reveal that risk-management activity increases when switching from the passive to the active ERM approach. Risk identification is less relevant than are risk assessment and risk monitoring for SMEs applying either an active or a passive ERM approach, albeit on different levels. The respective approach affects strategic orientation; a passive ERM approach results in a more defensive strategy, whereas an active ERM approach produces a more offensive strategy. Investments in SMEs following a passive ERM approach are used mainly for maintenance purposes. SMEs following an active ERM approach also direct funds toward product development and market expansion. Relevant preconditions for ERM implementation in SMEs are size, sector and ownership structure.
Structural model of ERM in SMEs.
Discussion
This study analyses ERM in SMEs by developing a structural model based on a survey questionnaire. Preconditions for ERM implementation, applied ERM approaches and their effects on strategic orientation are derived. Our structural model offers an initial and explorative model of ERM in SMEs.
Embedding the results
Although this study is open to criticism with regard to the preferred variables, the results are in agreement with the ERM literature and thus, to the causal structure of the model. Corresponding to both sample and design of the questionnaire, SMEs implemented various processes to identify, assess and monitor risks. SMEs thereby followed either an active or a passive ERM approach. The results suggest that an active ERM approach translates into an offensive strategic orientation with firms gaining competitive advantage (Hoyt and Liebenberg, 2011; Laforet, 2008; Richbell et al., 2006). SME investments following a passive ERM approach are used mainly for maintenance purposes, whereas SMEs following an active ERM approach are seen to also direct funds toward product development and market expansion. This indicates that an active ERM approach produces a somewhat prospective/offensive strategy, whereas a passive ERM approach results in a more defensive strategy (COSO, 2004; Georgellis et al., 2000; Salavou et al., 2004). SMEs following an active (passive) ERM approach appear to act more like prospectors (defenders); this implies that increased knowledge of risks and opportunities may enhance the assessment of the firm’s environment thus, permitting strategic opportunities to be exploited. Although previous studies relied on performance measures in order to analyse the effects of ERM (Beasley et al., 2008; Hoyt and Liebenberg, 2011), our findings can be interpreted in the spirit of positive ERM effects on competitiveness and business success.
ERM implementation in SMEs depends on certain characteristics, that is, firm size, sector and ownership structure. Larger firms seem to be more likely to have a more developed ERM; this reflects previous studies analysing the relationship between size and ERM (Liebenberg and Hoyt, 2003; Paape and Speklé, 2012). Family firms appear to have fewer incentives to implement ERM in that they show lower levels of ERM activity. The empirical findings reflect the literature (Beasley et al., 2005; COSO, 2004; Hoyt and Liebenberg, 2011; Paape and Speklé, 2012) and confirm that ERM approaches become more sophisticated with increasing size, institutional ownership and board encouragement and independence (Kleffner et al., 2003). However, ERM approaches in owner-managed firms are less sophisticated (Paape and Speklé, 2012). We refrain from drawing conclusions with regard to sector due to the limited number of significant results; regional and entrepreneur personal characteristics have no influence on ERM implementation.
Interpretation of the results
Our results correspond with preceding studies, even though the studies differ in design and sample. Larger firms benefit from economies of scale when implementing ERM processes, while smaller firms suffer from resource scarcity (Laforet, 2008; Pagach and Warr, 2011). This is consistent for samples of listed North American firms and European SMEs, albeit on possibly different levels of ERM complexity (Gao et al., 2012). Due to the absence of major differences, factors like cultural norms or the applicability of governance regulation do not seem to have an influence on ERM implementation (Paape and Speklé, 2012). The findings suggest that the implementation of ERM processes is driven primarily by firm characteristics.
With regard to the effects of ERM, a firm’s ability to assess its environment seems to result in a more offensive strategic orientation. However, exploiting strategic opportunities is a risky venture, even when an active ERM approach is in place. The risk of ‘sinking the boat’ is an issue (Dickson and Giglierano, 1986). Sinking the boat refers to the probability that a venture will fail to meet the targeted performance; extending the metaphor, following a passive ERM approach might be associated with the risk of ‘missing the boat’, that is, the risk of missing strategic opportunities.
Limitations and future research
Due to the initial and explorative nature of this study, we are aware of its shortcomings. First, ERM is a complex construct and our research design might conceptualize ERM in SMEs in too simple a manner, capturing aspects not adequately detailed. Second, we were not able to analyse whether the applied ERM approach and the pursued strategic orientation return dividends, as the study data did not include financial statements. Third, addressing the concept of entrepreneurial orientation (EO) would be a fruitful addition to the existing model whereby EO refers to a strategy-making process as the basis for entrepreneurial action (Lechner and Gudmundsson, 2014; Lumpkin and Dess, 2006). Our study, however, does not incorporate EO because of data limitations. Fourth, relating the risk of sinking or missing the boat to ERM would be a rewarding research focus. Finally, the number of identified clusters is derived from our sample. Although plausible and supported by the literature, the validity has to be tested in an additional sample. Testing the structural model using structural equation modelling and another sample might be a valuable contribution to the literature. It would be interesting to account for these shortcomings in future research.
Conclusion
Risk management is a major issue for SMEs; however, many lack the resources and have no reliable mechanisms to support their risk-management activity. Our data suggest that this is true for about two-thirds of SMEs, that is, these firms follow a rather passive risk-management approach and put little effort into the identification, assessment and monitoring of risks. The results of the study indicate that larger firms are more likely to have a more developed ERM and that family-owned firms have fewer incentives to implement ERM. These findings correspond to those of recent studies (Hoyt and Liebenberg, 2011; Paape and Speklé, 2012). The applied ERM approach thus affects the firm’s strategic orientation; while an active ERM approach appears to lead to a more offensive strategic orientation, a passive ERM approach may result in a more defensive strategic orientation. The findings can be interpreted to mean that ERM has positive effects on competitiveness and success reflecting previous studies (Meulbroek, 2002; Richbell et al., 2006; Stroh, 2005). Although the empirical findings must be interpreted with some caution, they support the assumptions derived from theory and can be considered preliminary evidence for the basic causal structure of ERM in SMEs.
Apart from a theoretical contribution, this study has practical implications: The analysis shows that the key to success for SMEs is an awareness of firm-related risks; being aware of risks is the prerequisite for ERM activity. Although SMEs in our sample make some effort to assess and monitor risk, their ability to do so will be limited if their knowledge of the risks facing the firm is poor. Nevertheless, this is precisely what we see in the data – a disregard for risk identification. This is true for SMEs applying an active or a passive ERM approach, albeit on different levels. Although entrepreneurs are familiar with their businesses, they are unlikely to be able to identify all related risks; a strong ERM approach may help them identify, assess and monitor risks, raise their risk awareness and facilitate them in better understanding and adjusting to the prevailing environment. The results of the present study support this argument. Moreover, thinking about firm-related risks not only makes small business entrepreneurs more aware of potential risks, but also opens windows for opportunity. Following an active ERM approach may support small business entrepreneurs in seeking strategic advantages therefore, increasing the competitiveness and business success of their firms. Accordingly, small business entrepreneurs should be encouraged to implement ERM in their firms. Although the implementation of ERM in SMEs demands a great effort, the benefits appear to persist in the long-run. This area of research will produce a better understanding of ERM in SMEs, the preconditions for ERM implementation and the effects of applied ERM approaches upon strategic orientation. Although more research is needed to validate these results, this article offers an initial impetus for future empirical research.
Footnotes
Acknowledgements
I would like to thank Susan Marlow and the two anonymous reviewers, whose constructive feedback and insightful comments significantly improved this paper. I am especially grateful to Janette Walde and Mike Peters for their helpful and valuable comments on this manuscript and for their continuous support during this project. I would also like to thank Andreas Koler and Stefan Ortner from alpS GmbH – Centre for Climate Change Adaptation for providing empirical data reported in this article. I thank Mary Heaney Margreiter for proofreading. Any remaining errors are the author’s alone.
Funding
This research was conducted within the framework of the alpS project ‘H07 KRisMa – Knowledge-Based Risk Management Tools’. I am grateful to the Austrian Research Promotion Agency (FFG) and the Hypo Tirol Versicherungsmakler GmbH for funding the project.