Abstract
Leveraging on the behavioural mixed gamble lens, we contend that heterogeneity in organisational goals leads to different diversification behaviours in family firms. Using survey and archival data on a sample of 988 family SMEs homogenous in their (high) family involvement level, we show that family SMEs that pursue nonfinancial (financial) goals exhibit lower (higher) probability of extending the boundaries of the firm to new product and/or market domains. Interestingly, in the face of threats, only those family SMEs that prioritise financial goals exacerbate their propensity to diversify, while increased vulnerability leads to an even lower probability of adopting diversification decisions among family SMEs with nonfinancial goals.
1 Introduction
Drawing on behavioural theories (Kahneman and Tversky 1979; Wiseman and Gomez-Mejia 1998), a growing body of research has devoted substantial attention to examining why family firms’ diversification decisions may differ from that of their nonfamily counterparts (see, for instance, Anderson and Reeb 2003; Gomez-Mejia, Makri, and Kintana 2010; Pindado and Requejo 2015; Sharma et al. 2020) and what causes family firm heterogeneity (Chua et al. 2012; Muñoz-Bullón, Sanchez-Bueno, and Suárez-González 2018; Requejo et al. 2018). An important assumption underlying existing studies is that the diversification strategies of family firms are influenced by nonfinancial goals—collectively known after Gomez-Mejia et al. (2007)’s study as “socioemotional wealth” (SEW)—such as family control preservation (Berrone, Cruz, and Gomez-Mejia 2012; Chrisman et al. 2012; Chua, Chrisman, and Sharma 1999; Gomez-Mejia et al. 2011; Kotlar et al. 2014; Kotlar and De Massis 2013) or ensuring transgenerational control (Zellweger et al. 2012). Specifically, a common assumption in this research stream has been that, although family firms do not completely neglect the economic consequences of diversification strategies, the noneconomic consequences are often likely to predominate in their decision-making. Thus, drawing on the prediction that family owners will be loss averse with respect to SEW (diversification decisions imply certain changes in traditional modes of operation, and the dilution of family control that derives from the greater involvement of external expertise and capital), various studies have found that higher family involvement tends to correspond to a lower probability of entering new product/geographic areas of activity (Gomez-Mejia, Makri, and Kintana 2010; Hafner 2019).
Although this body of research has provided important insights, there are still gaps in our understanding of diversification decisions in family firms. First, most of these studies have solely focused on certain SEW losses (due to, for instance, diluted family influence), without considering the potential SEW gains stemming from successful diversification decisions. That is, prior family firm diversification research has not considered the possibility that the decision to expand into new areas of activity constitutes a mixed gamble—that is, one that offers both positive and negative outcomes (Holmes et al. 2011). For example, entering new markets could be seen as a way to reverse a deteriorating financial situation to preserve SEW. Second, when it comes to operationalising the pursuit of family nonfinancial goals, this literature is characterised by too many reductionist proxies (such as the level of family ownership and engagement in the business or the generational stage) (Gomez-Mejia et al. 2011), and too few direct measurements of what goals are actually prioritised by the family (Vazquez and Rocha 2018). Arguably, this approach might be well-suited to large listed family businesses, but these proxies seem inappropriate to explain the causes that drive family SMEs heterogeneity for two main reasons: (1) their pursuit of a distinctive but varied set of goals (and, in turn, their behavioural variability) is likely to be much higher due to their insulation from capital market pressures; and (2) the higher discretion to accomplish any goal due to both concentrated ownership and control (Carney et al. 2015; Chrisman and Patel 2012). Accordingly, little is known about how heterogeneity in family goals causes variations in the diversification behaviour among family SMEs.
This study aims to fill these gaps by directly testing how different family goals affect diversification decisions using the SEW mixed gamble approach. We focus on this corporate strategy for two reasons. First, it is an efficient strategy for family SMEs—with highly concentrated business risk—to pursue projects with imperfectly correlated cash flows relative to existing projects. Second, diversification decisions generate an interesting contradiction within family SMEs, as they seem to collide with the family’s SEW. We start by identifying the goals that family firms prioritise through a survey, classify them into the traditional conflicting categories of financial (associated with a “rational” aspect) and nonfinancial (correlated with “nonrational” characteristics) (Vazquez and Rocha 2018), and analyse how specific goals affect the probability of entering new product/geographic areas of activity. We expect that nonfinancial goals negatively influence the probability of entering new industries/markets (Gomez-Mejia, Makri, and Kintana 2010). However, the mixed gamble logic predicts that this relationship depends on the competitive situation faced by the firm (Chua et al. 2012; Gomez-Mejia, Makri, and Kintana 2010; Westhead and Howorth 2007). Under conditions of performance below their aspirational level, financial peril threatens the survival of the firm, which is ultimately the source of the family’s SEW. Consequently, family firms should prioritise financial over SEW-related goals and thus be willing to take risks—in our case, diversification strategies—even if this occurs at the expense of SEW.
We test our hypotheses using survey data from a representative sample of 988 Spanish family SMEs. The ubiquity and economic significance of this type of firm in Spain justify the interest and suitability of exploring this geographical context. Our results show that prioritising nonfinancial vis-à-vis financial goals reduces the probability of entering industries/markets new to the firm. Interestingly, partly disagreeing with the predictions of the mixed gamble perspective, our findings suggest that family SMEs that prioritise financial (nonfinancial) goals exacerbate (attenuate) their propensity to undertake new industry/market entry endeavours when they perform poorly (lagging behind their own historical performance or behind their industry peers). In other words, family SMEs that pursue financial goals seem to fulfil our behavioural model predictions (performance below aspiration levels exacerbate the positive effect of financial goals on the likelihood of new industries/markets entries), while family SMEs that prioritise nonfinancial goals seem to exhibit an “irrational” risk aversion when it comes to entering into new industries/markets (performance below aspiration levels exacerbate the negative effect of nonfinancial goals on the likelihood of new industries/markets entries).
This paper makes several contributions. First, it adds to the literature that has recently integrated the concept of mixed gambles and the SEW approach to enhance its predictions about family firms’ strategic decision-making (e.g., Bauweraerts, Diaz-Moriana, and Arzubiaga 2020; Eddleston and Mulki 2021; and Lohe, Calabrò, and Torchia 2021). As noted above, most recent studies on diversification decisions in family firms predict that family owners act to preserve SEW, assuming that they only consider potential SEW losses. Our behavioural framework extends those arguments by allowing family SMEs to consider also the potential SEW gains that could be derived from successful diversification decisions. Interestingly, our findings of a lower probability of adopting diversification decisions in the face of threats among family SMEs with nonfinancial goals cast doubt on the predictive validity of the mixed-gamble approach in the particular context of family SMEs.
Second, we contribute to prior research that conceives family firms as a heterogeneous universe by providing evidence of high variability of strategic behaviours caused by goal divergence in a sample composed of SMEs with high and homogeneous family involvement in ownership, a proxy measure traditionally used to capture heterogeneity among family firms. Related to this point, our study answers the calls for more information on family goals (Chua, Chrisman, and De Massis 2015; Kotlar and De Massis 2013; Miller, Le Breton-Miller, and Lester 2011) and provides a more complete picture of strategic conduct by using direct empirical tests of the effect of prioritising nonfinancial and financial goals on family business behaviour, rather than just discussing implied linkages. This is, to the best of our knowledge, among the first studies that incorporate direct measures that capture the preponderance of financial and nonfinancial goals in family SMEs to apprehend the mixed gamble of strategic decisions. As a result, we are able to provide practitioners with new insight on the type of goals that might shape family SMEs growth, a much-debated topic.
Finally, although the decision to engage in diversification strategies is altered in the face of threats for family SMEs that prioritise nonfinancial goals, we find that their behaviour runs in the opposite direction to that predicted by previous literature for other risk-taking decisions (for example, Gomez-Mejia et al. 2014). In fact, family SMEs that prioritise nonfinancial goals do not seem to become more aware of potential SEW gains derived from entering into new industries/markets when choices are framed negatively, that is, if there is a perception that the current strategy would result in a certain loss. Contrary to family SMEs that give priority to financial goals, we find that family SMEs that prioritise nonfinancial goals are affected by “threat-rigidity” effects that constrain diversification decisions in the face of threats (Staw, Sandelands, and Dutton 1981), hence refuting the idea of financial and nonfinancial goals alignment when the firm faces greater peril (Gomez-Mejia, Patel, and Zellweger 2018). Unlike publicly traded family firms, the main focus of previous studies (Alessandri, Cerrato, and Eddleston 2018; Chrisman and Patel 2012; Gomez-Mejia et al. 2007; Gomez-Mejia, Makri, and Kintana 2010; Gomez-Mejia, Patel, and Zellweger 2018), family SMEs are not under so much pressure to “do something” when performance or sales are falling. Thus, our results highlight the importance of financial goals within family SMEs as drivers of strategic change.
2 Theoretical Background and Hypotheses
2.1 The Mixed Gamble Logic
The management literature on family business risk-taking is firmly anchored in the behavioural tradition (Berrone, Cruz, and Gomez-Mejia 2012; Fang et al. 2019; Lude and Prügl 2019; Sharma et al. 2020; Wiseman and Bromiley 1996; Wiseman and Gomez-Mejia 1998). Based on a long stream of research that originates from the prospect theory (Kahneman and Tversky 1979), this literature posits that decision makers are loss averse and their strategic choices change with problem framing (Bromiley 2010; Wiseman and Gomez-Mejia 1998). Loss aversion means that individuals are risk averse in the domain of gains in order to maintain their current wealth. However, they become risk takers to preserve the status quo against further deterioration when threatened by a certain loss. Problem framing means that the utility derived from a strategic choice is defined over gains and losses relative to some reference point, being the status quo or the do-nothing scenario the most common reference point for individuals in their “framing” of a strategic choice. Embracing both concepts, family business scholars contend that family owners are less likely to take risky decisions if they anticipate losses with respect to their nonfinancial or SEW-related goals (Chrisman et al. 2012; Kotlar and De Massis 2013), as such goals act as the pivotal reference point for the family (Gomez-Mejia et al. 2007).
A refinement to this “pure gamble” framework is the mixed gamble perspective (Holmes et al. 2011), which allows for the possibility of potential gain and loss outcomes when making decisions under risk. Scholars agree that family firms foresee the detrimental effect of entering new industries and/or markets on nonfinancial goals (Gomez-Mejia et al. 2011). But diversifying activities have also been linked to their achievement because they reduce volatility in earnings and increase growth opportunities (Anderson and Reeb 2003). As a consequence, the wellbeing of family members is ultimately ensured, insofar as it is inextricably tied to the future of the family business itself (Gomez-Mejia, Makri, and Kintana 2010).
Acknowledging that most strategic decisions constitute a mixed gamble (Bromiley 2010), prior research accounts for the potential gains and losses relative to nonfinancial reference points when explaining how diversification decisions by family and nonfamily businesses differ from each other. Table 1 summarizes the few studies that have analysed family firms’ diversification decisions under the mixed gamble lens.
Literature review on family firms’ diversification decisions under the mixed gamble lens.
| Author(s) (Year) | Time span | Sample | Findings |
|---|---|---|---|
| Lohe, Calabrò, and Torchia (2021) | 2015–2016 | Case study on 8 large German family firms | The authors identify different types of family firms. One group is pushed towards international markets by competition and they put at risk the stock of SEW. Other group faces a mixed gamble of push and pull factors especially when managers are not family members. |
| Fuad, Thakur, and Sinha (2021) | 2008–2018 | 221 cross-border acquisition (CBA) deals | Family-controlled firms have a higher preference for early movement compared with nonfamily-controlled firms. Further, founder’s presence on the board and acquirer’s superior performance strengthens the relationship between family control and early movement within CBA waves. |
| Xu, Hitt, and Dai (2020) | 1982–2018 | 15,859 firm-year observations across 93 countries | Family-dominant firms prefer low breadth-high depth international diversification. However, they find that when their performance falls below an aspiration level, SEW becomes less important to family-dominant firms and they adopt high breadth international diversification. |
| Hussinger and Issah (2019) | 2003–2019 | 423 acquisitions conducted by S&P 500 manufacturing and services firms. 158 acquisitions were made by 46 family firms and 265 acquisitions were made by 83 nonfamily firms | Family firms more likely to undertake a related acquisition than nonfamily firms, especially when they are performing above their aspiration level. |
| Alessandri, Cerrato, and Eddleston (2018) | 2003–2006 | 935 S&P publicly traded companies, 273 of them being family-controlled and the rest (662) nonfamily-controlled | While nonfamily firms exhibit the highest levels of internationalisation, there is much variance among family firms. How family firms evaluate strategic options varies with the type of organisational slack, generally becoming more conservative with increasing available slack and more risk seeking with increasing recoverable slack. |
| Gomez-Mejia, Patel, and Zellweger (2018) | 1997–2011 | S&P 500 manufacturing firms | Family control implies a general reluctance to acquire and, when an acquisition happens, there is a preference for related targets. Performance below aspiration levels leads to a heightened propensity to prioritise financial over SEW goals, which is reflected in the acquisition of unrelated targets. |
| Cruz and Justo (2017) | 2004–2013 | 2609 Spanish entrepreneurs | Family entrepreneurs are more likely to engage in portfolio entrepreneurship than non-family entrepreneurs. Family entrepreneurs could engage in risky strategic choices even in the absence of major external threat, that is, when they anticipate potential SEW gains derived from that risky endeavour. |
| Gomez-Mejia, Makri, and Kintana (2010) | 1998–2001 | 360 publicly traded companies, 160 of them being family-controlled and the rest (200) non-family-controlled | Family firms diversify less both domestically and internationally than nonfamily firms. They also find that family firms are more willing to diversify as business risk increases. |
Our work builds on these advancements in the behavioural model and further points out the fact that nonfinancial or SEW-related goals may not always be the pivotal reference point for the family. As explained in the following sections, we hypothesise that family SMEs may position themselves somewhere along a continuum that goes from exclusively prioritising nonfinancial goals to exclusively prioritising financial goals (Mahto et al. 2010). Then, we empirically show that the ensuing heterogeneity of organisational goals influences family SMEs’ assessment of the new industry/market entries mixed gamble.
2.2 Nonfinancial and Financial Goals as A Source of Heterogeneity
Behavioural theorists have long suggested that firms pursue a potentially varied set of economic as well as noneconomic goals (Cyert and March 1963), which can explain the wide variation in the behaviour of family businesses (Chrisman et al. 2012; Chrisman and Patel 2012; Kotlar and De Massis 2013). In this study, we follow a general classification of goals in dichotomous categories that reflect two entirely opposite poles: nonfinancial or SEW-related goals and financial goals (Mahto et al. 2010; Vazquez and Rocha 2018).
First, SEW represents the stock of affect-related value a family derives from its ownership position in a particular firm (Gomez-Mejia, Makri, and Kintana 2010). SEW is a broad construct that has been linked to several dimensions, such as the enjoyment of exerting control over the business, the identification of family members with the firm, the emotional attachment of family members, and the utility derived from transferring the business to the next generation (Berrone, Cruz, and Gomez-Mejia 2012). In particular, when it comes to extending the boundaries of the firm to new product or market domains, the family firm literature particularly acknowledges that family firms are primarily concerned with the following nonfinancial goals: (1) the desire of retaining family control and influence over the firm’s affairs, (2) the procurement of various forms of wealth for the family, and (3) the intention of transgenerational control by ensuring firm survival (Berrone, Cruz, and Gomez-Mejia 2012; Chua et al. 2018; Zellweger et al. 2012). In the context of Spanish family SMEs, we link the goal of generating wealth for the family with the creation of employment opportunities and an adequate standard of living for family members (Instituto de la Empresa 2015; Kotlar and De Massis 2013). Undoubtedly, Spain offers an ideal research context to analyse the effect of this particular family goal on diversification decisions, given the high average unemployment rate (25.1%) during the study period (2013–2015).
Second, financial goals are strictly related with the economic aspects of the business, such as profits, growth and increasing market value. Figure 1 illustrates how financial and nonfinancial goals can be heterogeneously distributed across family SMEs, forming a continuum where family SMEs go from exclusively prioritising nonfinancial goals to exclusively prioritising financial goals.
Heterogeneity of family SMEs based on their goals.
Regardless of their nature, goals serve as reference points under prospect theory and alter decision outcomes (Kotlar et al. 2014). Therefore, we expect that the consideration of different family goals can lead to unique patterns of risk behaviour, providing new evidence of family firms’ heterogeneity. In particular, we explore the effect of goals, either financial or nonfinancial, on one important and risky decision: the expansion to new industries and/or new markets.
2.3 New Industries/Markets Entries as A Mixed Gamble for Family SMEs
The expansion to new industries and/or new markets can be an appealing strategic choice for family SMEs because it provides alternative sources of long-term growth, performance enhancement, and the exploitation of synergies (Montgomery 1994). However, contrary to the common belief that family principals have strong incentives to minimise risk due to the high concentration of the family’s wealth in a single organisation (Anderson and Reeb 2003), empirical results refute this hypothesis and show that family firms engage in significantly less corporate diversification (Gomez-Mejia, Makri, and Kintana 2010). Family firm research argues that this unexpected finding can be explained by the fact that new industry/market entry decisions are more complex within family firms because, when making such strategic decisions, they must weigh up the pros and cons not only in financial terms, but also in terms of the consequences for the pursuit of nonfinancial priorities. Even if new industry/market entries confer new growth opportunities and/or some risk protection to the family and the business, it often requires raising additional capital by taking on more debt (Doucet and Requejo 2022), expertise and resources from parties outside the family, as well as accepting a higher degree of uncertainty and the potential disruption to the status quo (Gomez-Mejia, Makri, and Kintana 2010). In conjunction, these potential consequences can erode family’s ability to achieve nonfinancial goals and lead to an increase in perceived losses and in the likelihood of ruling out these risky strategies.
However, many studies advocate that a simple distinction between family and nonfamily firms may lead to inconsistencies and imprecise findings (Chua et al. 2012). Building on the growing body of research on family firm heterogeneity, we contemplate the possibility that family firms do not always prioritise the achievement of nonfinancial goals, challenging the claim that higher family involvement in the business invariably reduces the willingness of family SMEs to engage in risky decisions. In the next section, we identify the possible gains and losses that new industry/market entries create for family SMEs depending on the goals they prioritise. Accordingly, we formulate hypotheses on how different types of goals will solve the dilemma of unexpected and/or contradictory evidence on risk-taking under the mixed gamble lens.
2.4 Nonfinancial Goals and New Industries/Markets Entry Decisions
According to the proposed behavioural framework, family SMEs are likely to view new industry/market entry decisions as a mixed gamble, given that the associated uncertainty potentially generates both positive and negative outcomes for either nonfinancial or financial goals. However, family SMEs that prioritise nonfinancial goals find themselves in a unique situation because they face potential losses not faced by family SMEs without this type of goals. This occurs because new industry/market entries pose a hazard to the nonfinancial or SEW-related goals (Gomez-Mejia et al. 2011) to the extent that such strategy entails increasingly complex operations that require specific capabilities associated with the management of organisational diversity (Hitt et al. 2006). Given the limited pool of family members, these requirements can be often accessed more readily through the greater involvement of professional, rather than family, managers and employees (Muñoz-Bullón and Sánchez-Bueno 2012; Stadler et al. 2017). Considering such human capital constrains, the family firm could be forced to hire nonfamily workers or experts that are familiar with the new industries/markets they want to enter. The expansion into new businesses and/or geographic markets similarly poses a major threat to the preservation of family current and transgenerational control because it often requires taking more debt, and this in turn implies an increase in the probability of losing the freedom to dictate business policies without creditors’ scrutiny and the incorporation of outsiders’ perspectives/opinions (Anderson and Reeb 2003; Gomez-Mejia, Makri, and Kintana 2010). Overall, new industry/market entry decisions may threat the achievement of nonfinancial goals, as it requires expertise and resources from parties external to the firm.
Conversely, as proposed by the mixed gamble perspective, the family may also anticipate that entering new industries/markets can be a boost to the achievement of nonfinancial goals if they are successful. Family SMEs that add new products or services to their current portfolio can generate career opportunities for more family members and enhance the wealth-generating capabilities of family assets for future generations (Zellweger et al. 2012).
However, whether such gains can be truly obtained in practice is highly speculative, while the threat to the family’s SEW is certain. Thus, we argue that family SMEs that prioritise nonfinancial goals are loss averse when it comes to undertaking these risky decisions. The potential threats to the achievement of nonfinancial goals shift the family decision against risk-taking behaviours relative to family SMEs that do not pursue this type of goals. Accordingly, despite possible and uncertain benefits that new industry/market entries can provide in achieving nonfinancial goals, family SMEs that prioritise nonfinancial goals will demand higher potential gains to relinquish their current status quo vis-à-vis those that prioritise financial goals.
2.5 Financial Goals and New Industries/Markets Entry Decisions
Thus far, we have focused on nonfinancial or SEW-related goals as the primary reference framework in family firms’ decision-making. Although such goals have long been recognised as the prominent reference point for family firms, there appears to be a consensus among family business scholars that they also pursue goals that are not explicitly oriented to the family and are strictly connected to the financial aspects of the firm, such as growth and profits (Chua et al. 2018; Kotlar and De Massis 2013). When financial goals are prioritised, new industry/market entries may be an attractive strategy for their achievement as diversification can increase sales and revenues, and can imply the allocation of firm’s underutilised organisational resources to new areas (Montgomery 1994). Therefore, we contend that, in family firms where financial goals are prioritised, the family calculus when weighing up the potential gains and losses of new industry/market entries will include the previously mentioned benefits, which families without financial goals do not even consider. As a result, the estimated gains are less likely to be outweighed by the potential losses.
We therefore contend that the prioritisation of nonfinancial over financial goals may reduce new industries/markets entry decisions. In light of the above arguments, we formulate the following hypothesis:
Hypothesis 1:
Family SMEs that prioritise nonfinancial goals will exhibit lower probability of new industries/markets entry vis-à-vis those that prioritise financial goals.
2.6 Performance Below Aspirations as A Moderator of The Influence of Nonfinancial and Financial Goals on New Industries/Markets Entry Decisions
We have previously hypothesised that the more important nonfinancial goals are for the family, the more reluctant the family SME will be to enter new activities and/or new markets. The reason is that such decision poses a hazard to the prioritised nonfinancial goals in the shape of greater delegation, uncertainty, and control loss. However, under the mixed gamble lens, family SMEs with nonfinancial goals are not systematically risk but loss averse (Gomez-Mejia et al. 2007; Wiseman and Gomez-Mejia 1998). This means that, if the family anticipates major threats to the achievement of nonfinancial goals within the do-nothing scenario, the family may prefer the risks/drawbacks associated with undertaking new industry/market entries in order to prevent major perceived losses/threats to decision maker’s endowment, that is, what is already accrued when pursuing nonfinancial goals. For example, being a financially viable entity is a condition sine qua non for the family to achieve their nonfinancial goals. In a scenario of poor performance, family firms that do not explore new industries/markets could be foregoing potential gains from these risky strategies that could help it preserve the family’s wealth invested in the firm (Alessandri, Cerrato, and Eddleston 2018; Cruz and Justo 2017; Gomez-Mejia, Makri, and Kintana 2010). Simply put, the achievement of nonfinancial goals is unfeasible if the company is not financially viable.
Therefore, we contend that threats to performance will affect the mixed gamble associated with new industry/market entries by spurring awareness of the possibility that new industries/markets entry decisions could help the family reach nonfinancial goals when the firm is in financial trouble. Hence, in a context of performance below aspirations, the family is likely to downplay the threats that new industry/market entry decisions pose to nonfinancial goals, while simultaneously becoming more aware of the potential gains associated with this risk-taking behaviour. Accordingly, when nonfinancial goals are increasingly under threat, family firms with these goals could be more willing to accept the risk associated with new industry/market entries in order to revert such unsatisfactory situation. In a nutshell, we argue that the expected negative relationship between the prioritisation of nonfinancial goals and the probability of diversifying into new industry/market domains is moderated by performance below aspirations, so that:
Hypothesis 2a:
Family SMEs that prioritise nonfinancial goals are more likely to enter new industries/markets when firm performance is below aspirations compared to when it is above.
Likewise, in the case of family SMEs that prioritise financial goals as a reference point, poor performance means that they lie in the domain of losses. Accordingly, they should be risk takers to recoup an unsatisfactory situation (Shinkle 2012). Thus, the expected positive relationship between the prioritisation of financial goals and the probability of new industries/markets entry decisions is moderated by performance below aspiration, so that:
Hypothesis 2b:
Family SMEs that prioritise financial goals are more likely to enter new industries/markets when firm performance is below aspirations compared to when it is above.
Note that confirming both Hypotheses 2a and 2b reinforces the behavioural models’ predictions for the potential alignment between nonfinancial and financial goals under certain circumstances (Alessandri, Cerrato, and Eddleston 2018; Gomez-Mejia et al. 2007; Gomez-Mejia, Makri, and Kintana 2010; Gomez-Mejia, Patel, and Zellweger 2018).
3 Methods
3.1 Sample and Data Collection
Our analyses are based on a representative sample of Spanish family SMEs. Following the strategy of Doucet, Requejo, and Suárez-González (2022), our sample is the result of combining two data sources: primary data collected through a survey and secondary information from the SABI database, which is provided by Bureau van Dijk. The starting point for the definition of the sample is a study conducted by the Spanish Institute of Family Business (Instituto de la Empresa Familiar or IEF, by its name in Spanish) in collaboration with the Spanish Network of Family Business Chairs (Instituto de la Empresa 2015). This work is based on the population of Spanish family firms included in SABI. Stratified sampling based on three criteria (that is, region, firm size, and sector) was used to select the firms. Primary data were collected from the CEO at each firm using computer-assisted telephone interviewing (CATI) technology. Additional technical details on the survey are available in the report published by the IEF.
As explained in the Instituto de la Empresa 2015 report, firms are classified into family and nonfamily considering the degree of ownership concentration. More precisely, in the case of firms with concentrated (dispersed) ownership, family firms are those in which all members of the family collectively own at least 50 percent (20 percent) of the business. More details on the several steps taken to define family firms are available in the study of the Instituto de la Empresa 2015. Many of the variables that come from the survey, including our dependent variable (the entry in new industries/markets), refer to activities developed by the firms in the period 2013–2015. Consequently, we match the survey data with longitudinal information (2012–2015, including predictors lagged by 1 year) from the SABI database, which provides detailed accounting and financial information. Because of the data requirements to test the proposed hypotheses, our final sample consists of 988 family SMEs (2784 firm-year observations) from Spain with complete information in both sources (that is, survey and SABI).
Combining the primary data collected from the survey with the archival secondary data extracted from the SABI database should reduce potential common method biases. Also, the design of the questionnaire minimises the problem of social desirability bias. In the context of Spanish family SMEs, some financial and nonfinancial goals might be understood as a culturally acceptable and appropriate behaviour that could lead to biased responses (Podsakoff et al. 2003). In order to prevent this problematic trend and in an effort to capture goal prioritisation, a forced binary scale instead of an ordinal multi-category answer format was employed. More precisely, to minimise the possibility of spurious relationships, the way in which the questions regarding the priorities of the firm were formulated forced the respondent to select one or two goals of the list provided.
Finally, to check if the responses provided by interviewees during the survey are reliable, we analyse the correlation between the archival data extracted from the SABI database and the information gathered through the survey. Specifically, the bivariate correlations are computed for firm size and export activity because there were items in the questionnaire regarding these firm dimensions and we also get measures for these two variables from SABI. The correlation coefficients are high (0.80 and 0.62, respectively) and significant at the 0.01 level (two-tailed test). Therefore, we conclude that the information collected through the survey is consistent and reliable.
3.2 Dependent Variable
3.2.1 New Industries/Markets Entry
Our dependent variable enables us to differentiate between family SMEs that enter in industries and/or markets that are new to the firm during the 2013–2015 period and those that did not. The variable is coded as 1 if a firm has entered one or several new industries and/or markets during the 2013–2015 period (T = 3) and 0 otherwise. Family SMEs that have entered several new industries/markets are also coded as 1 for two reasons: (1) the underlying rationale for risk-taking behaviours according to our theoretical framework is the same; (2) coding single and multiple industry/market entries as 1 is a more conservative approach (Gomez-Mejia, Patel, and Zellweger 2018).
3.3 Independent and Moderator Variables
3.3.1 Goals Orientation
Respondents were asked to prioritise a maximum of two from a list of six goals that were selected and identified as crucial for Spanish family SMEs by experts and researchers of the network of Family Business Chairs and the Research Team of the IEF: three nonfinancial or SEW-related goals, (1) offering job opportunities and an adequate standard of living to family members, (2) family control over the activities of the business, and (3) intentions for transgenerational control (firm survival); and three financial goals, (4) increase profits, (5) increase market value, and (6) increase business size. This strategy allows us to know what goal(s) is (are) most relevant to each interviewee. With this information, we compute an aggregated variable, goals orientation, which is measured on a five-point scale (1 = two financial (zero nonfinancial) goals are prioritised; 2 = one financial (zero nonfinancial) goal is prioritised; 3 = one financial and one nonfinancial goal are prioritised; 4 = one nonfinancial (zero financial) goal is prioritised; and 5 = two nonfinancial (zero financial) goals are prioritised). These points represent where the family SME lies along the continuum (represented in Figure 1) that goes from exclusively prioritising financial goals, to prioritising both types of goals, to exclusively prioritising nonfinancial goals. The higher the value of goals orientation, the greater the emphasis on nonfinancial goals vis-à-vis financial goals.
We use four alternative proxies to measure performance below aspirations:
3.3.2 Sales Below Aspiration Level
Following previous studies of family SMEs’ risk-taking (see, for instance, Gomez-Mejia et al. 2007), we capture how well or poorly the family firm is performing using the discrepancy between actual and target sales. First, sales below historical is calculated as the percentage of increase or decrease in sales (natural logarithm of sales at t divided by sales at t-1) with its sign switched to ease interpretation. Second, sales below social consists of a comparison of how well each firm is doing compared to firms in the same industry (the natural logarithm of sales at t minus the natural logarithm of the industry median-adjusted sales, with its sign switched and using two-digit SIC codes for the industry). Higher values of both measures mean that the firm is facing declining (below-target, either historical or social) sales.
3.3.3 ROA Below Aspiration Level
We use two additional proxy variables based on a firm’s return on assets (ROA) to test the moderating effect of how well or poorly the family firm is performing relative to its aspiration level. The first one, ROA below historical, is calculated as the difference between firm’s ROA at t and its historical ROA at t-1, with its sign switched. The second, ROA below social, is the difference between firm’s ROA and the median ROA of firms that operate in the same industry-year (two-digit SIC code), with its sign switched.
3.4 Control Variables
In our regression analyses, we need to account for other firm characteristics that may affect new industries/markets entry decisions of family SMEs. Therefore, the set of control variables comprises the following:
Innovation may increase the likelihood of entering into new areas of activity and/or serving new markets (Anderson and Reeb 2003). Four items in our survey capture innovation intensity: (1) whether the firm has introduced new or significantly improved goods and services; (2) whether the firm has introduced new or significantly improved production processes, logistic systems, or support activities; (3) whether the business has introduced new or significantly improved organisational methods; and (4) whether the business has introduced new or significantly improved marketing strategies. As expected, these items are highly correlated, so we build the variable Innovation through principal component analysis (PCA). We expect each variable to load highly on one single factor, which represents innovation intensity. The Kaiser-Meyer-Olkin (KMO) measure verified the sampling adequacy for the analysis, KMO = 0.71. Also, all KMO values for individual variables are larger than 0.69, which is above the acceptable limit of 0.5, and Bartlett’s test of sphericity indicates that correlations between items were sufficiently large for PCA.
As prior research finds that CEO characteristics may influence diversification decisions (Gomez-Mejia, Makri, and Kintana 2010; Zahra 2005), we control for several individual features that capture the CEO profile. First, we include a dummy indicating the presence of a family-CEO or chairman (CEO family); second, we control for CEO tenure as years in the firm; and third, the generational stage (First generation in charge versus later generations) (Muñoz-Bullón, Sanchez-Bueno, and Suárez-González 2018). Finally, we introduce other control variables in our models: Firm size, measured as the natural logarithm of the firm’s number of employees (Anderson and Reeb 2003; Gomez-Mejia et al. 2014); ROA, to capture firm performance, calculated as net income divided by total assets at time t; Leverage, measured as the ratio of total debt to total assets at t; Firm age, which is computed as the number of years since firm i’s foundation (Fuad, Thakur, and Sinha 2021); and Industry, a set of two-digit SIC dummies aimed at controlling for industry effects. All variable descriptions are provided in Table 2.
Variable descriptions.
| Variables | Description | Source |
|---|---|---|
| Dependent variable | ||
|
|
||
| New industries/markets entry | A dummy variable taking the value of 1 if the focal firm has entered one or several new industries and/or markets during the 2013–2015 period (T = 3) and 0 otherwise. | Survey |
|
|
||
| Independent variables | ||
|
|
||
| Goals orientation | Respondents were asked to prioritise a maximum of two from a list of six goals: three nonfinancial or SEW-related goals, (1) offering job opportunities and an adequate standard of living to family members, (2) family control over the activities of the business, and (3) intentions for transgenerational control (firm survival); and three financial goals, (4) increase profits, (5) increase market value, and (6) increase business size. With this information, a five-point scale was computed depending on the goals prioritised: (1 = two financial (zero nonfinancial) goals are prioritised; 2 = one financial (zero nonfinancial) goal is prioritised; 3 = one financial and one nonfinancial goals are prioritised; 4 = one nonfinancial (zero financial) goal is prioritised; and 5 = two nonfinancial (zero financial) goals are prioritised). The higher the value of goals orientation, the greater the emphasis on nonfinancial goals vis-à-vis financial goals. | Survey |
| Sales below historical | The percentage of increase or decrease in sales (the natural logarithm of sales at t divided by sales at t-1) with its sign switched to ease interpretation. | SABI database |
| Sales below social | The natural logarithm of sales at t minus the industry (two-digit SIC)-median-adjusted sales, with its sign switched. | SABI database |
| ROA below historical | The difference between a focal firm’s performance (EBITDA/assets) in t and its historical performance in year t-1, with its sign switched. | SABI database |
| ROA below social | The difference between the performance (EBITDA/assets) of the focal firm and the median ROA of firms that operate in the same industry-year (two-digit SIC code), with its sign switched. | SABI database |
|
|
||
| Control variables | ||
|
|
||
| Firm size | Natural logarithm of the firm’s number of employees. | SABI database |
| Firm age | The number of years since the firm i’s foundation. | Survey |
| Leverage | The ratio of total debt to total assets. | SABI database |
| ROA | EBITDA to total assets. | SABI database |
| CEO family | A dummy indicating the presence of a family-CEO or chairman. | Survey |
| CEO tenure | Number of years in the firm. | Survey |
| First generation | Dummy variable that equals 1 if the first family generation is in charge, and zero otherwise. | Survey |
| Innovation | The principal component of four items in our survey: (1) whether the firm has introduced new or significantly improved goods and services; (2) whether the firm has introduced new or significantly improved production processes, logistic systems, or support activities; (3) whether the business has introduced new or significantly improved organisational methods; and (4) whether the business has introduced new or significantly improved marketing strategies. | Survey |
| Industry | A set of two-digit SIC dummies aimed at controlling for industry effects. | SABI database |
4 Analysis
4.1 Empirical Strategy
For the final sample of 988 family SMEs, we merge the survey data (in which several of the items of interest refer to the 2013–2015 period) with financial data for these three years from SABI and end up with a longitudinal dataset of 2784 firm-year observations (T = 3). Note that for some sample firms we cannot get information for the three years covered in the study. Nonetheless, getting several years of data for each firm allows us to use a pooled logistic regression (PLR) method, which in turn enables more degrees of freedom and higher efficiency in the estimation process. Under the PLR estimation, the outcome variable becomes an event indicator that records whether the event occurs in T or not. However, the PLR technique does not account for the exact period within the interval when the event takes place. Thus, a firm that enters a new industry/market at the beginning of the period is treated in the same way as one industry/market entry that occurs at the end of the period. Overall, the PLR model relates the probability of an event occurring in an interval to a logistic function. All explanatory variables are lagged one year to mitigate potential endogeneity problems and robust standard errors are computed in all regression analyses.
5 Results
Table 3 presents the means, standard deviations, and correlations of the study variables. Around 91 percent of the family SMEs are micro or small-sized businesses that are over 30 years old, and 56 percent are founder-run. Around 91 percent of the respondents are family CEOs[1]. Approximately, 49 percent of our sample undertook new industries/markets entry decisions during the 2013–2015 period. None of the variance inflation factor (VIF) scores approached 10, which suggests that multicollinearity is unlikely to have serious effects on the regression results.
Means, standard deviations, and pairwise correlation coefficients among variables.
| Mean | SD | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1. New industries/markets entry | 0.49 | 0.50 | 1.00 | |||||||||||||
| 2. Goals orientation | 3.23 | 1.18 | −0.14*** | 1.00 | ||||||||||||
| 3. Sales below historical | 0.02 | 0.33 | −0.07*** | 0.01 | 1.00 | |||||||||||
| 4. Sales below social | −0.05 | 0.97 | −0.17*** | 0.08*** | 0.27*** | 1.00 | ||||||||||
| 5. ROA below historical | −0.19 | 6.89 | 0.00 | −0.02 | 0.30*** | 0.04* | 1.00 | |||||||||
| 6. ROA below social | −0.17 | 6.98 | −0.07*** | 0.01 | 0.26*** | 0.25*** | 0.44*** | 1.00 | ||||||||
| 7. Firm size | 2.65 | 0.91 | 0.15*** | −0.07*** | −0.14*** | −0.52*** | 0.01 | −0.03 | 1.00 | |||||||
| 8. Firm age | 3.55 | 21.50 | 0.02 | −0.00 | 0.02 | −0.11*** | 0.00 | 0.05** | 0.13*** | 1.00 | ||||||
| 9. Leverage | 115.19 | 308.36 | 0.03 | 0.01 | 0.01 | 0.04* | −0.02 | 0.02 | −0.02 | −0.04* | 1.00 | |||||
| 10. ROA | 3.19 | 7.41 | 0.10*** | −0.01 | −0.29*** | −0.24*** | −0.46*** | −0.95*** | 0.05** | −0.05** | −0.02 | 1.00 | ||||
| 11. CEO family | 0.91 | 0.29 | −0.04** | 0.06*** | −0.00 | 0.11*** | −0.01 | 0.02 | −0.10*** | −0.02 | 0.01 | −0.01 | 1.00 | |||
| 12. CEO tenure | 17.91 | 11.24 | −0.07*** | 0.05** | −0.01 | −0.04* | 0.01 | 0.01 | 0.04* | 0.25*** | −0.01 | −0.00 | 0.21*** | 1.00 | ||
| 13. First generation | 0.57 | 0.50 | −0.01 | 0.02 | −0.02 | 0.05** | −0.00 | −0.02 | −0.08*** | −0.46*** | −0.01 | 0.02 | 0.05** | 0.07*** | 1.00 | |
| 14. Innovation | 0.00 | 0.78 | 0.40*** | −0.12*** | −0.09*** | −0.20*** | −0.01 | −0.10*** | 0.19*** | 0.01 | 0.02 | 0.11*** | −0.01 | −0.04* | −0.02 | 1.00 |
-
Correlations two-tailed significance tests: *, **, and *** indicate significance at the 5 percent, 1 percent, and 0.1 percent level, respectively.
Table 4 presents the regression results on the determinants of new industries/markets entry. Note that, instead of coefficients, the table reports the estimated odds ratios (ORs) for each explanatory variable, which range between 0 and infinity. A value of an estimated odds ratio below one indicates that an increase in the corresponding variable reduces the probability of new industries/markets entry. Conversely, variables that get an estimated odds ratio higher than one have a positive impact on the new industries/markets entry probability. Model 1 in Table 4 (baseline model) presents a logit regression that captures the likelihood that a given family SME undertakes new industries/markets entry decisions as a function of the control variables. In line with previous research, we find that CEO tenure is negatively related to new industries/markets entry (Gomez-Mejia, Makri, and Kintana 2010), while Firm size is positively related (Gomez-Mejia, Makri, and Kintana 2010, 2018). We next check whether models that include nonfinancial goals and the interaction terms as explanatory variables are an improvement over the baseline model (column 1) by analysing whether the difference between the -2LLs of the baseline model and the rest of the models in Table 4 is significant. In all cases, the results of the tests produce a p < 0.001, thus supporting the idea that our variables of interest indeed contribute to explain the probability of new industries/markets entry.
Logistic regression analyses of family SMEs’ new industries/markets entry as a function of goals orientation.
| (1) | (2) | (3) | (4) | (5) | (6) | |
|---|---|---|---|---|---|---|
| Firm sizet-1 | 1.231*** | 1.216*** | 1.219*** | 1.120+ | 1.217*** | 1.218*** |
| (0.069) | (0.069) | (0.070) | (0.073) | (0.070) | (0.069) | |
| Firm age | 1.000 | 1.001 | 1.000 | 0.999 | 1.001 | 1.000 |
| (0.002) | (0.002) | (0.002) | (0.002) | (0.002) | (0.002) | |
| Leveraget-1 | 1.000* | 1.000* | 1.000** | 1.000** | 1.000* | 1.000* |
| (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | |
| ROAt-1 | 1.010 | 1.010 | ||||
| (0.006) | (0.006) | |||||
| CEO family | 0.749+ | 0.772 | 0.751+ | 0.778 | 0.771 | 0.771 |
| (0.117) | (0.124) | (0.124) | (0.124) | (0.125) | (0.124) | |
| CEO tenure | 0.984*** | 0.985*** | 0.986*** | 0.985*** | 0.986*** | 0.984*** |
| (0.004) | (0.004) | (0.004) | (0.004) | (0.004) | (0.004) | |
| First generation | 1.145 | 1.160 | 1.144 | 1.140 | 1.147 | 1.151 |
| (0.119) | (0.121) | (0.121) | (0.119) | (0.120) | (0.120) | |
| Innovation | 3.344*** | 3.286*** | 3.329*** | 3.313*** | 3.350*** | 3.313*** |
| (0.211) | (0.208) | (0.212) | (0.214) | (0.213) | (0.210) | |
| Goals orientation | 0.858*** | 0.863*** | 0.847*** | 0.860*** | 0.851*** | |
| (0.034) | (0.035) | (0.034) | (0.034) | (0.034) | ||
| Sales below historicalt-1 | 2.100+ | |||||
| (0.884) | ||||||
| Goals orientation × Sales below historicalt-1 | 0.746* | |||||
| (0.093) | ||||||
| Sales below socialt-1 | 1.506** | |||||
| (0.222) | ||||||
| Goals orientation × Sales below socialt-1 | 0.833*** | |||||
| (0.036) | ||||||
| ROA below historicalt-1 | 1.016 | |||||
| (0.019) | ||||||
| Goals orientation × ROA below historicalt-1 | 0.998 | |||||
| (0.006) | ||||||
| ROA below socialt-1 | 1.038* | |||||
| (0.020) | ||||||
| Goals orientation × ROA below socialt-1 | 0.985* | |||||
| (0.006) | ||||||
| Observations | 2784 | 2784 | 2738 | 2780 | 2747 | 2784 |
| Pseudo R 2 | 0.198 | 0.202 | 0.205 | 0.209 | 0.204 | 0.204 |
-
Two-digit SIC code dummies (that is, industry dummy variables) are included in all models; New industries/markets entry is the dependent variable in all models; Goals orientation measures family CEO’s goals prioritisation, being the higher the value the greater the priority on nonfinancial goals versus financial goals; estimated odds ratios (ORs), instead of coefficients, are reported for each explanatory variable; robust standard errors are provided in parentheses; +, *, **, and *** indicate significance at the 10 percent, 5 percent, 1 percent, and 0.1 percent level, respectively.
Hypothesis 1 states that family SMEs that prioritise nonfinancial goals will exhibit lower new industries/markets entry probability than those that prioritise financial goals. As can be seen in column 2, Hypothesis 1 is strongly supported (OR = 0.858, p < 0.001). We compute the predicted probabilities for each level of the nonfinancial goals measure using Stata command ‘margins’. According to these results, the odds ratio for nonfinancial goals in Model 2 (OR = 0.858, p < 0.001) translates into an approximately 7.63 percent (56.47 percent compared with 48.84 percent) decrease in the likelihood of new industries/markets entry when an average family SME changes from not prioritising any to selecting one nonfinancial goal (that is, change from one to three in the variable), keeping other factors constant (we specified the ‘atmeans’ option to fix all other variables at their means). In addition, the regression result points to a further decrease of 7.58 percent (48.84 percent compared with 41.26 percent) in the new industries/markets entry probability when a family SME decides to pursue two nonfinancial goals (that is, change from three to five in our measure), ceteris paribus. Overall, we confirm the economic relevance of our results in addition to their statistical significance.
Hypotheses 2a and 2b suggest that the probability of new industries/markets entry decisions increases (decreases) in the case of family SMEs whose performance is below (above) aspirations. According to the mixed gamble perspective, this happens because family SMEs anticipate losses in their achievement of both nonfinancial and financial goals in the current do-nothing scenario, thus turning them into risk takers. The remaining columns in Table 4 (columns 3 to 6) allows us to test this prediction. In this respect, the odds ratio (Model 3) of the interaction between nonfinancial goals and sales below historical aspirations is significant (OR = 0.746, p < 0.05); the coefficient (Model 4) of the interaction between nonfinancial goals and sales below social aspirations is significant (OR = 0.833, p < 0.001); the coefficient (Model 5) of the interaction between nonfinancial goals and ROA below historical aspirations is not significant; and the coefficient (Model 6) of the interaction between nonfinancial goals and ROA below social aspirations is significant (OR = 0.985, p < 0.05). To help us disentangle whether the estimated coefficients are consistent with the mixed gamble logic stated in Hypotheses 2a and 2b, we plotted the interaction effects of both sales below historical and social aspirations (which were both statistically significant) in Figure 2. The same rationale applies when ROA below social aspirations is considered.
Moderating effects of sales below the aspiration level (based on historical and social comparisons) on new industries/markets entry decisions.
Interestingly, we find that sales below historical (Figure 2A) and social (Figure 2B) aspirations strengthen the negative effect of nonfinancial goals on the probability of new industries/markets entry, which runs in the opposite direction to the one predicted by the behavioural mixed gamble framework. As shown in Figure 2, the decrease in the new industries/markets entry propensity associated with the pursuit of nonfinancial goals is even more pronounced when family SMEs are doing worse. In other words, instead of being loss averse, family SMEs that prioritise nonfinancial goals are even more risk averse toward diversification decisions. These findings align with the threat-rigidity thesis, which suggests that organisations may not be risk-seeking in the face of economic adversity (Staw, Sandelands, and Dutton 1981). Hypothesis 2a is thus not supported. On the contrary, as observed for the case of family SMEs that prioritise financial goals (Figure 2), firm’s vulnerability seems to alter the mixed gamble implied by new industry/market entry decisions in the direction predicted by behavioural models. When choices are framed positively—the do-nothing scenario (current strategy) would result in more certain gains—, family SMEs that pursue financial goals are less likely to enter new industries/markets vis-à-vis family SMEs that prioritise nonfinancial goals. However, if performance falls below aspirations—the do-nothing scenario would result in potential losses—, the new industries/market entry propensity of this category of firms increases. These findings support Hypothesis 2b.
6 Robustness Tests
6.1 Controlling for Endogeneity
Self-selection bias may be present among family SMEs reporting the goals they prioritise. For example, family SMEs may prioritise nonfinancial goals over financial goals because they know they have unobserved low-quality characteristics that do not allow them to achieve financial goals. These suboptimal features might also be linked with lower prospects of success, lower confidence to adopt growth-oriented strategies, and so on. So, our findings may not be related to the prioritisation of nonfinancial goals at all, but rather they could be reflecting the influence of other unobserved factors that hinder new industries/markets entry decisions. We address this sample-induced endogeneity conducting a two-stage Heckman procedure (Certo et al. 2015). The first step is to estimate the likelihood of prioritising nonfinancial goals as a function of the previous control variables plus the CEO education level (which is the exclusion restriction). The resulting inverse Mills ratio is used as a control variable in the second step. As can be seen in Table 5, the inclusion of the inverse Mills ratio obtained from the first step does not change our results, thereby suggesting that sample-induced endogeneity has no relevant effect on our findings.
Results of two-stage Heckman regression analyses of family SMEs’ new industries/markets entry as a function of goals orientation.
| (1) | (2) | (3) | (4) | (5) | |
|---|---|---|---|---|---|
| Firm sizet-1 | 1.133* | 1.132* | 1.062 | 1.127+ | 1.137* |
| (0.069) | (0.071) | (0.072) | (0.070) | (0.070) | |
| Firm age | 1.001 | 1.001 | 1.000 | 1.001 | 1.001 |
| (0.002) | (0.002) | (0.002) | (0.002) | (0.002) | |
| Leveraget-1 | 1.000** | 1.000** | 1.000** | 1.000** | 1.000** |
| (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | |
| ROAt-1 | 1.008 | ||||
| (0.006) | |||||
| CEO family | 0.910 | 0.893 | 0.898 | 0.922 | 0.906 |
| (0.152) | (0.153) | (0.150) | (0.156) | (0.152) | |
| CEO tenure | 0.988** | 0.990* | 0.988** | 0.989* | 0.988** |
| (0.004) | (0.004) | (0.004) | (0.004) | (0.004) | |
| First generation | 1.206+ | 1.191+ | 1.184 | 1.197+ | 1.196+ |
| (0.127) | (0.127) | (0.125) | (0.127) | (0.126) | |
| Innovation | 2.939*** | 2.961*** | 2.996*** | 2.968*** | 2.967*** |
| (0.209) | (0.213) | (0.216) | (0.212) | (0.212) | |
| Inverse Mills ratio | 3.116** | 3.254*** | 2.821** | 3.422*** | 3.060** |
| (1.085) | (1.150) | (0.995) | (1.206) | (1.067) | |
| Goals orientation | 0.876*** | 0.882** | 0.862*** | 0.880** | 0.869*** |
| (0.035) | (0.036) | (0.035) | (0.036) | (0.035) | |
| Sales below historicalt-1 | 2.034+ | ||||
| (0.860) | |||||
| Goals orientation × Sales below historicalt-1 | 0.754* | ||||
| (0.094) | |||||
| Sales below socialt-1 | 1.535** | ||||
| (0.227) | |||||
| Goals orientation × Sales below socialt-1 | 0.833*** | ||||
| (0.036) | |||||
| ROA below historicalt-1 | 1.017 | ||||
| (0.019) | |||||
| Goals orientation × ROA below historicalt-1 | 0.998 | ||||
| (0.006) | |||||
| ROA below socialt-1 | 1.038* | ||||
| (0.020) | |||||
| Goals orientation × ROA below socialt-1 | 0.986* | ||||
| (0.006) | |||||
| Observations | 2784 | 2738 | 2780 | 2747 | 2784 |
| Pseudo R 2 | 0.205 | 0.209 | 0.211 | 0.207 | 0.207 |
-
Two-digit SIC code dummies (that is, industry dummy variables) are included in all models; New industries/markets entry is the dependent variable in all models; Goals orientation measures family CEO’s goals prioritisation, being the higher the value the greater the priority on nonfinancial goals versus financial goals; estimated odds ratios (ORs), instead of coefficients, are reported for each explanatory variable; robust standard errors are provided in parentheses; +, *, **, and *** indicate significance at the 10 percent, 5 percent, 1 percent, and 0.1 percent level, respectively.
6.2 Alternative Definition of Family Control
We further test the robustness of our findings using an alternative definition of family SMEs. It can be argued that nonfamily CEO respondents have different perceptions of family owners’ economic and noneconomic goals. Therefore, we use a more conservative definition of family control by restricting our sample to family owned and managed SMEs. As shown in Table 6, our findings remain consistent when excluding nonfamily CEOs.
Alternative definition of family control (excluding firms with nonfamily CEOs).
| (1) | (2) | (3) | (4) | (5) | |
|---|---|---|---|---|---|
| Firm sizet-1 | 1.165* | 1.169* | 1.092 | 1.165* | 1.166* |
| (0.071) | (0.073) | (0.075) | (0.072) | (0.071) | |
| Firm age | 1.000 | 1.000 | 0.999 | 1.000 | 0.999 |
| (0.003) | (0.003) | (0.003) | (0.003) | (0.003) | |
| Leveraget-1 | 1.000* | 1.000** | 1.000** | 1.000* | 1.000* |
| (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | |
| ROAt-1 | 1.007 | ||||
| (0.006) | |||||
| CEO tenure | 0.985*** | 0.986** | 0.985*** | 0.985*** | 0.984*** |
| (0.004) | (0.004) | (0.004) | (0.004) | (0.004) | |
| First generation | 1.136 | 1.128 | 1.117 | 1.128 | 1.125 |
| (0.125) | (0.126) | (0.124) | (0.125) | (0.125) | |
| Innovation | 3.264*** | 3.316*** | 3.316*** | 3.321*** | 3.304*** |
| (0.217) | (0.223) | (0.227) | (0.222) | (0.222) | |
| Goals orientation | 0.842*** | 0.847*** | 0.838*** | 0.842*** | 0.835*** |
| (0.034) | (0.035) | (0.034) | (0.035) | (0.034) | |
| Sales below historicalt-1 | 2.711* | ||||
| (1.221) | |||||
| Goals orientation × Sales below historicalt-1 | 0.690** | ||||
| (0.092) | |||||
| Sales below socialt-1 | 1.702** | ||||
| (0.285) | |||||
| Goals orientation × Sales below socialt-1 | 0.811*** | ||||
| (0.040) | |||||
| ROA below historicalt-1 | 1.018 | ||||
| (0.020) | |||||
| Goals orientation × ROA below historicalt-1 | 0.997 | ||||
| (0.006) | |||||
| ROA below socialt-1 | 1.048* | ||||
| (0.021) | |||||
| Goals orientation × ROA below socialt-1 | 0.984** | ||||
| (0.006) | |||||
| Constant | 2.078+ | 2.042+ | 2.274* | 2.125+ | 2.109+ |
| (0.842) | (0.830) | (0.898) | (0.865) | (0.846) | |
| Observations | 2521 | 2480 | 2517 | 2488 | 2521 |
| Pseudo R 2 | 0.199 | 0.203 | 0.206 | 0.201 | 0.201 |
-
Two-digit SIC code dummies (that is, industry dummy variables) are included in all models; New industries/markets entry is the dependent variable in all models; Goals orientation measures family CEO’s goals prioritisation, being the higher the value the greater the priority on nonfinancial goals versus financial goals; estimated odds ratios (ORs), instead of coefficients, are reported for each explanatory variable; robust standard errors are provided in parentheses; +, *, **, and *** indicate significance at the 10 percent, 5 percent, 1 percent and 0.1 percent level, respectively.
7 Discussion and Conclusion
It is widely accepted that family SMEs’ diversification decisions are negatively affected by the pursuit of noneconomic goals collectively known as “socioemotional wealth” (SEW), given that those strategies collide with the achievement of such goals—due to diluted family influence that derives from the greater involvement of both external expertise and capital. Our study extends this literature by contemplating the possibility that the decision to expand into new areas of activity could also generate SEW gains in some situations. For example, new industries/markets entry decisions could be framed positively if they are able to reverse a deteriorating financial situation to preserve SEW. To the best of our knowledge, our work is among the first to use the mixed gamble approach relaxing the strong assumption that high family ownership/involvement always means the prioritisation of nonfinancial over financial goals. Instead, we directly observe which goals are prioritised by the family and how they alter the mixed gamble of new industries/markets entry.
Our findings confirm the negative relationship between nonfinancial goals and diversification decisions found in previous studies that use family involvement as a proxy for the desire to protect the SEW endowment (Hypothesis 1). Those family SMEs that give more importance to nonfinancial versus financial goals exhibit a significantly lower probability of entering new industries/markets during our study period. However, under our mixed gamble lens, we expect that family SMEs that prioritise nonfinancial goals would be more likely to enter new industries/markets if they anticipate SEW losses in the current do-nothing scenario, i.e., if performance is below historical or social aspirations (Hypothesis 2a). Interestingly, our results show that family SMEs that prioritise nonfinancial goals alter their propensity to undertake diversification decisions, but in the opposite direction to the one predicted. Here, family SMEs that prioritise nonfinancial goals seem to exhibit an “irrational” risk aversion. These results could be driven by the low levels of public disclosure to which family SMEs are subjected. Unlike publicly traded companies, the main focus of previous studies, SMEs are not under so much pressure to “do something” when performance or sales are falling, which could explain our results.
On the contrary, those family SMEs that give more importance to financial goals seem to behave in line with the mixed gamble perspective of decision making. Our results indicate that this group of family SMEs is more likely to pursue diversification decisions when performance falls below aspiration levels (Hypothesis 2b). As financial goals are the reference point for the family, decision makers feel greater pressure to improve the firm’s financial situation by exploring new industries/markets.
7.1 Practical and Theoretical Implications
This study has important implications for practitioners, scholars, and policymakers alike. For family SME owners, managers, and small business consultants, our findings offer practical insights by showing how nonfinancial or SEW-related concerns—such as assuring employment opportunities for family members and preserving control of the business—can lead to an enhanced or reduced new industries/markets entry propensity, and ultimately to higher or lower growth. Practitioners should weigh up the fit of family goals with other firm priorities when expanding business activities into new areas. In fact, our results are a reminder for managers and family owners of how their strategic decisions may be unconsciously biased depending on whether the family SME is more oriented towards nonfinancial or financial goals. For example, we find that those managers that prioritise nonfinancial goals seem to suffer higher threat-rigidity effects that hinder diversification decisions, particularly in the face of higher firm’s vulnerability. To the extent that those nonfinancial objectives are exogenously given, it may be practical for managers to understand and proactively be able to reduce the biases that prevent growth strategies before losses become too large.
Our study provides two important theoretical contributions to the study of family firm decision making. First, a main contribution lies in the recommendation to use family goals as strategic reference points to enrich the theoretical predictions of the behavioural model regarding differences among family firms in their diversification behaviour. In fact, our findings emphasise that family SMEs are not a homogeneous group, even if they have similar ownership and management configurations. Accordingly, it represents an important step forward in the family business literature, which has systematically conceived nonfinancial goals (or the preservation of family’s SEW) as the primary reference point when the level of family ownership and/or involvement is high (Hafner 2019; Zellweger et al. 2012). Unlike previous family business literature, we do not rely on traditional proxies that aim to capture the pursuit of nonfinancial goals, such as family ownership and control of the board, which clearly do not correctly capture the “true” heterogeneity among the most predominant type of firm throughout the world (that is, family SMEs). Indeed, because most family SMEs are isolated from stock market pressures, they have more discretion to behave idiosyncratically and to pursue a more varied set of goals (Chrisman and Patel 2012), including in many cases purely financial ones (Kotlar and De Massis 2013). If policymakers can account for this heterogeneity by collecting goal-related information, it could lead to better diagnoses, a more accurate segmentation of the corporate sector and, ultimately, a better adaptation and optimisation of the programmes and policies aimed at promoting SMEs’ growth strategies.
Second, this study shows the idiosyncratic nature of family SMEs’ diversification behaviour. In line with previous research, family SMEs seem to diversify less when the reference point used to assess gains and losses is the achievement of nonfinancial goals. However, we find that the mixed gamble behavioural model—which is a more realistic approach insofar as it also contemplates the possibility of both gains and losses in SEW—does not fit well with family SMEs where nonfinancial goals are the reference point for making strategic decisions. In fact, they seem to behave according to the threat-rigidity thesis (Staw, Sandelands, and Dutton 1981). These results suggest that the (lower) levels of exposition to public disclosure and scrutiny can have important implications for the role of SEW goals as drivers of strategic change when the firm faces greater peril. Therefore, future research using the mixed gamble lens to predict the strategic behaviour of family firms should incorporate the idea that not all types of firms are able to ‘react’ in order to recoup an unsatisfactory situation. Seen differently, under vulnerability, SEW goals are not drivers of strategic change in all types of firms.
7.2 Limitations and Future Research Directions
Our empirical analysis is not without limitations, which suggest new research opportunities. First, this study is restricted to Spain, with a specific institutional context, and we focus on specific risk-related behaviours, which may reduce the generalisability of the results. It should be noted that Spanish family SMEs could differ from firms that operate in other institutional contexts and from large family firms in terms of how they frame and prioritise their goals. Thus, exploring other countries as well as alternative strategic decisions, where distinct family goals could be prioritised, would be a promising avenue for future research.
A second limitation concerns the comprehensiveness of the list of goals. However, we believe that, in the context of family SMEs, our six goals have the capacity to capture the main goals that characterise family SMEs (Kotlar and De Massis 2013; Vazquez and Rocha 2018; Zellweger et al. 2012). Also, prioritising among an increasing number of goals (which in turn can be fairly complex and abstract constructs) can be overwhelming, resulting in respondents’ fatigue and carelessness that introduce biases (Podsakoff et al. 2003). Therefore, solving the trade-off between nuanced information on the importance of goals and developing items that are as clear, concise, and specific as possible remains a challenging task for future research.
Third, we do not investigate the degree of complexity that entails each new industry and/or market entry due to lack of data. Consequently, future research can explore, for example, how family goals affect the relatedness of new industry entries preferred by family firms. Another limitation linked to the empirical strategy is that discrete response models assume that the explanatory variables are strictly exogenous. If any relevant omitted regressor is correlated with our explanatory variables, endogeneity attributable to this cause could affect our results. Unfortunately, there is no method to address this issue in a convincing way when working with discrete choice models. However, we do our best with the available data and mitigate this problem by using a two-step Heckman self-selection model to control of unobserved factors that simultaneously affect goals selection and new industries/markets entry. The adoption of this empirical strategy, combined with the fact that we control for relevant CEO and firm characteristics, enables us to be confident that our empirical evidence is not seriously affected by endogeneity.
Finally, we encourage family business researchers to apply the proposed behavioural framework to other types of strategic behaviours, focusing on the family SME context in particular. The important role that family SMEs play around the world, along with the scarce academic attention they have received in comparison to large public family firms, calls for future empirical studies in this area.
7.3 Concluding Remark
Our study analyses the specific mixed gamble faced by family SMEs when they have to balance potential opportunities and threats that arise in the achievement of nonfinancial as well as financial goals associated with new industries/markets entry decisions. Using survey and archival data on a sample of 988 family SMEs homogenous in their (high) family involvement level, we find that family SMEs that pursue nonfinancial (financial) goals exhibit lower (higher) new industries/market entry propensity. Interestingly, in the face of threats, only those family SMEs that prioritise financial goals enhance their new industries/market entry propensity, while increased vulnerability leads to an even lower probability of adopting diversification decisions among family SMEs with nonfinancial goals.
Funding source: Spanish Ministry of Education
Award Identifier / Grant number: FPU18/02200
Funding source: Spanish Ministry of Science and Innovation
Funding source: AEI
Award Identifier / Grant number: PID2019-107546GA-I00
Funding source: Regional Government of Castile and León
Award Identifier / Grant number: SA069G18
Award Identifier / Grant number: SA106P20
Funding source: European Regional Development Fund
Award Identifier / Grant number: CLU-2019-03
Acknowledgments
We would like to thank the Associate Editor, Rania Labaki, and two anonymous reviewers for comments and suggestions on previous versions of this paper. We also benefitted from the comments of participants at the VIII Workshop de Jóvenes Investigadores en Economía y Empresa at the University of Zaragoza (2019). We are grateful to the Spanish Institute of Family Business (Instituto de la Empresa Familiar, IEF) and the Spanish Network of Family Business Chairs for their support in the definition of the sample and in the data collection process.
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Research funding: This work was supported by the Spanish Ministry of Education (Grant FPU18/02200). Financial support is also gratefully acknowledged from the Spanish Ministry of Science and Innovation and AEI (Grant PID2019-107546GA-I00), and from the Regional Government of Castile and León (Grants SA069G18 and SA106P20). Ignacio Requejo and Isabel Suárez-González are grateful to the Junta de Castilla y León and the European Regional Development Fund (Grant CLU-2019-03) for the financial support to the Research Unit of Excellence “Economic Management for Sustainability” (GECOS). Ignacio Requejo is also affiliated with the Nordic Finance and the Good Society (NFGS) research project coordinated by the Center for Corporate Governance at Copenhagen Business School (CBS). Any errors are our own responsibility.
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Disclosure statement: No potential conflict of interest was reported by the authors.
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