Entrepreneurial intention‑action gap in family firms: bifurcation bias and the board of directors as an economizing mechanism

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Entrepreneurial intention‑action gap in family firms: bifurcation bias and the board of directors as an economizing mechanism

Jelle Schepers¹ · Wim Voordeckers¹ · Tensie Steijvers¹ · Eddy Laveren²

Received: 5 August 2020 / Revised: 20 November 2020 / Accepted: 4 March 2021 / Published online: 30 April 2021

Abstract

This study investigates under which conditions entrepreneurial intentions will transform into entrepreneurial actions in a family firm context. Although entrepreneurial intentions are often a good predictor for entrepreneurial activity, intentions will not always lead to the expected action. We aim to explain this intention-behavior gap in family firms by investigating the moderating role of bifurcation bias, defined as the de facto asymmetric treatment of family vs. nonfamily assets. Our results support the argument that bifurcation bias in family firms hinders the smooth transi- tion of entrepreneurial intentions into entrepreneurial actions. Nevertheless, results also support the notion that the appointment of outside directors in the board could serve as an economizing mechanism for bifurcation biased family firms to transform entrepreneurial intentions into entrepreneurial actions.

Keywords Entrepreneurship · Family firms · Intention-action gap · Bifurcation bias · Board of directors

1 Introduction

In psychological literature, an intention to perform a certain behavior has proven to be the best predictor for that specific behavior (Ajzen & Fishbein, 1980; Fishbein &

Ajzen, 1975). Nevertheless, more recent studies have demonstrated that the intention-behavior relationship is far from perfect since several factors may obstruct or hinder smooth implementation of these intentions leading to an ‘intention-behavior gap’ (Gieure et al., 2020; Godin et al., 2005).

* Jelle Schepers

jelle.schepers@uhasselt.be

1 Research Center for Entrepreneurship and Family Firms (RCEF), Hasselt University, Agoralaan, Building D, 3590 Diepenbeek, Belgium

2 University of Antwerp, Prinsstraat 13, 2000 Antwerp, Belgium

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In entrepreneurship literature, a large amount of literature has been devoted to revealing and understanding the drivers of entrepreneurial intentions because they are assumed to explain and predict entrepreneurial activity (Krueger et al., 2000;

Liñán & Fayolle, 2015). In doing so, researchers generally relied on the theory of reasoned action (Fishbein & Ajzen, 1975) or the theory of planned behavior (Ajzen, 1991) to examine antecedents of entrepreneurial intentions. Although these studies have highly contributed to our understanding of the entrepreneurial process, they hardly question the underlying assumption that entrepreneurial intentions will indeed bring about the desired entrepreneurial actions (Jenkins & Johnson, 1997;

Kautonen et al., 2015; Shepherd et al., 2019). Given these observations, it is surpris- ing that to date only few studies have investigated the intention-behavior relationship (e.g. Kautonen et al., 2015; Kolvereid & Isaksen, 2006) and the conditions under which entrepreneurial intentions will lead to entrepreneurial behaviors (Carsrud &

Brännback, 2011; Van Gelderen et al., 2015, 2018). Moreover, these studies generally investigated entrepreneurial intentions and actions among samples of university students or within the context of new venture creation. However, entrepreneurship is not limited to the start of a new business but also comprises a much wider range of entrepreneurial scenarios including entrepreneurial activities in existing organiza- tions such as product innovation or the pursuit of new markets (Fayolle & Liñán, 2014; Kellermanns & Eddleston, 2006), aimed at achieving firm-level growth.

Although most existing intention-action studies in entrepreneurship literature have mainly focused on the level of the individual (nascent) entrepreneur, some recent evidence indicates that the so-called intention-action gap might also exist in existing organizations (e.g. Delmar & Wiklund, 2008; Kolvereid & Åmo, 2019; Sten- holm, 2011). Step by step, entrepreneurship researchers are disentangling the complex mechanism that causes this “gap” between firm-level entrepreneurial intentions (EI), defined as the willingness to create new value within existing organizations (Fini et al., 2012) and entrepreneurial actions (EA), defined as the effective entrepreneurial actions carried out by the firm rather than their mere intentions (Miller

& Friesen, 1982; Zahra & Covin, 1995). Nevertheless, our current understanding of this black box at the firm-level is still limited. Therefore, this article aims to contribute to this stream of research by introducing family firms, as a unique organizational form, into the EI–EA discussion.

First and foremost, when it comes to existing organizations, family firms are the dominant organizational form worldwide (Gómez-Mejía et al., 2007) but their distinctive attributes are often overlooked in entrepreneurship literature. The finding that the development of entrepreneurial activities in family firms is not always self- evident (De Massis et al., 2013; Matzler et al., 2015; Muñoz-Bullón & Sanchez- Bueno, 2011) may be puzzling as extant research reported that parent’s and grand- parent’s entrepreneurial status has a positive effect on the offspring’s entrepreneurial intentions (Laspita et al., 2012). Hence, while entrepreneurial intentions may be def- initely present in family firms, there may exist important obstacles in transforming them in entrepreneurial activities. Namely, family firms distinguish themselves from non-family firms by the importance they attach to non-financial family goals (Ber- rone et al., 2012; Gómez-Mejía et al., 2011) which may lead family firms to apply a differential treatment of family or heritage assets versus non-family assets. When

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family firms apply a de facto asymmetric treatment between family and non-family assets by default, this phenomenon is referred to as “bifurcation bias (BB)” (Madi- son et al., 2018; Majocchi et al., 2018; Verbeke & Kano, 2012; Verbeke et al., 2020).

For example, when family firms choose to hire, evaluate, promote or compensate family employees in a more advantageous way than their non-family counterparts (Barnett & Kellermanns, 2006; Verbeke & Kano, 2010, 2012) irrespective of their actual capabilities, skills or contribution to the firm, this discrepancy is seen as an expression of bifurcation bias. The de facto asymmetric treatment of family versus nonfamily assets, will affect people’s perception of fairness in an organization (Mad- ison et al., 2018). We build on insights from equity theory (Adams, 1965) to suggest that perceived organizational injustice will cause behavioral and cognitive changes in employees that could be detrimental for transmitting a family firm’s EI into EA as they may decrease their entrepreneurial efforts due to bifurcation bias (Madison et al., 2018), resulting in a gap between EI and EA. We argue that in private family firms bifurcation bias moderates the relationship between EI and EA, in such a way that a family firm’s EIs will be less positively related to their EA when the level of bifurcation bias is higher, which is confirmed by our results.

As family firms are considered to be very heterogeneous in terms of goals, governance, and resources (Chrisman et al., 2013; Madison et al., 2018), they also vary in terms of bifurcation bias and the extent to which they are able to economize on this bias. Recent studies have argued that family firms might be able to economize on bifurcation bias by safeguarding themselves against dysfunctional effects of this bias (Jennings et al., 2018; Verbeke & Kano, 2012). In our study, we introduce the board of directors as a potential economizing mechanism as proposed by Verbeke and Kano (2012). Our findings show that the appointment of outside directors in the board could serve as an economizing mechanism for bifurcation biased family firms to transform EI into EA.

Our study contributes to the entrepreneurship as well as family business literature. First, while prior entrepreneurial intention-action research mainly defined entrepreneurial intentions as new venture creation, we contribute to this literature stream by empirically testing this relationship in a broader defined entrepreneurial context with a focus on existing organizations. Second, the literature on BB mainly focuses on its impact on family firm internationalization (Majocchi et al., 2018; Ver- beke et al., 2020), yet this study adds to existing knowledge on BB by analyzing if and to what extent BB intervenes in the general entrepreneurial process of private family firms. Third, although scholars have argued that BB may be an unambigu- ous characteristic of family firms as compared to other types of firms, measuring the construct in family firms stays a challenge. The current paper tries to advance empirical research on BB by operationalizing the construct with a self-constructed 7-item scale, taking into account family firm heterogeneity in terms of BB. Lastly, the role of outside directors has been a predominant research stream in the literature on boards of directors (Gao & He, 2019), but the evidence on their advantages is extremely scarce for private family firms relative to public firms. Our analysis contributes to an increased understanding of outside directors and their role in the entrepreneurial process of private family firms.

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The rest of the article is structured as follows. In the first section, we introduce EI and EA as two distinct but related constructs. Next, bifurcation bias and the presence of outside directors on the board are introduced as moderating variables on the EI–EA relationship and our central hypotheses are derived. In the third section, the results of our empirical study will be presented and discussed. Finally, the paper ends with a discussion section where the major conclusions are highlighted and future research opportunities are presented.

2 Theoretical foundation and hypotheses development

2.1 Entrepreneurial intentions as an immediate determinant of entrepreneurial actions

EI can be linked to EA by building on insights from psychological literature where a great amount of literature is devoted to the relationship between intentions and actions. According to the theory of reasoned action and the theory of planned behavior (Ajzen, 1985, 1991; Fishbein & Ajzen, 1975), a person’s intention to perform (or not to perform) a behavior is the immediate determinant of that action. In psychological literature, empirical evidence concerning the positive relationship between intentions and actions has been collected with respect to many different types of behavior (Ajzen & Fishbein, 1980; Schwarzer, 2008; Sheppard et al., 1988). Espe- cially when this behavior is hard to observe, rare, or involves unpredictable time- lags, intentions have proven to be the best predictor of this planned behavior.

Also in entrepreneurship, intention models have proven their relevance as entrepreneurship is clearly an intentional process (Krueger et al., 2000). According to Krueger et al. (2000), entrepreneurship is not something that ‘just happens’, it is something that happens over time and involves considerable planning. Therefore, entrepreneurship is exactly the type of planned behavior (Bird, 1988; Krueger et al., 2000) for which intention models could be applied. Even more, intention models have proven to be far more predictive in explaining entrepreneurial behavior than merely using situational or personal factors (Krueger et al., 2000). This makes the use of intention models an important opportunity to increase our ability to understand and predict EA.

Most entrepreneurship studies using these intention models have focused on the individual level (Emami & Dimov, 2017; Lortie & Castogiovanni, 2015), namely to predict an individual’s behavior in starting up a new business (Kautonen et al., 2015;

Van Gelderen et al., 2015). Recent entrepreneurship studies, however, have extrapo- lated these insights from psychological literature and used intention models on the firm level in order to explain organizational outcomes (Aghaei & Sokhanvar, 2019;

Rasmussen et al., 2018).

Also, firm level intentions have proven to provide useful insight to strategy formation. When we look at the literature on strategy formation in existing organizations (Mintzberg & Waters, 1985), we see that this stream of literature has a great deal of common characteristics with the intention-behavior literature found in psychological journals. More specifically, the concepts of intended and realized strategy

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are of central importance in strategic literature. Taken together, intentions offer criti- cal insight into the underlying process that leads to EA.

The predictive power of intentions might be even stronger in explaining entrepreneurial behavior of private family firms because these firms have high ownership concentration, and are frequently dominated by one person (Feltham et al., 2005) or a dominant coalition (Craig et al., 2014). Taken together, we propose the following baseline hypothesis:

Hypothesis 1 A family firm’s entrepreneurial intentions are positively related with their entrepreneurial actions.

2.2 Placing a curb on the implementation of entrepreneurial intentions

Although there appears to be general agreement among psychologists that intentions are a good predictor for the expected behavior, there are many factors that can obstruct the intention-behavior relation. First, most human behavior is goal-directed (e.g. Heider, 1958) but having the intention to act is only a first step to goal realization because people often face problems en route to goal attainment (Gollwitzer &

Sheeran, 2006; Mintzberg & Waters, 1985). The relative importance of intentions in the prediction of behavior is expected to vary across situations, the level of will power, and commitment towards performing the intended behavior (Ajzen, 1991).

It follows that when intentions are not (fully) realized, a ‘gap’ abounds between intentions and actions (Godin et al., 2005). When it comes to firm-level entrepreneurship, the gap between intentions and actions becomes crucial to understand the entrepreneurial process in private family firms. A review by Sheeran (2002) in the field of applied psychology found that those who intend to act but finally do not act accounted for the largest part of the cases. In sum, people might ‘get derailed’ (Goll- witzer & Sheeran, 2006) while pursuing their goal intentions because many situational contexts or self-states are not conductive to intention realization. In this paper, we focus on this ‘intention-behavior gap’ in private family firms and why people not always (fully) succeed in realizing their intentions by investigating bifurcation bias as an obstructing factor for goal attainment.

2.3 Bifurcation bias in private family firms

When looking at family firms as a group, individual differences exist between these firms. Indeed, in family business literature, the focus has switched (Gomez-Mejia et al., 2014) from a binary view of family vs nonfamily firms to the inquiry of sub- tle variations among them, i.e. a “heterogeneity view” (Chua et al., 2012). Most recently, family business scholars have proposed that bifurcation bias, defined as the de facto differential treatment of family or heritage assets versus nonfamily assets (e.g. Madison et al., 2018; Majocchi et al., 2018; Verbeke et al., 2020) could help explain family firm heterogeneity. The observed levels of bifurcation bias between family firms as well as the associated behaviors can vary across family firms and lead to significant family firm heterogeneity (Verbeke et al., 2020). Given the choice

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between rational economic logic decision-making and bifurcation biased decisions where family assets are prioritized, one can question why some family firms develop and maintain practices that are bifurcated between family and nonfamily assets.

Bifurcation bias in family firms can be attributed to the “affect heuristic” (Slovic et al., 2002, 2007) where decision makers will favor affect-rich choices when a set of choice alternatives is presented. When this rule of thumb is applied, decision makers experience “good feelings”, i.e. a positive affect, and attribute a higher expected overall benefit to the positive feelings of favoring family assets than warranted by the objective attributes of this choice alternative. On the contrary, when decision makers experience “bad feelings” toward another choice alternative, this leads to reduced levels of expected overall value when compared to objective choice alternatives.

In making important strategic and operational decisions, family firms have to balance choice alternatives related to family-based resources and non-family resources.

While these family-based resources are often associated with a high uniqueness status and positive affect, non-family resources are often seen as commodities and being fungible (Verbeke et al., 2020). In line with Verbeke and Kano (2012), we view a family firm as bifurcation biased when asymmetric treatment of family versus nonfamily assets is embedded in overarching managerial practices, i.e. firm-level routines applied systematically and by default. The bifurcation bias in family firms thus prevents an objective assessment between family-based versus nonfamily-based assets (especially human assets).

This asymmetric treatment of employees in the firm, favoring family members over their non-family peers, could be expressed in many ways. One of the most obvi- ous examples of bifurcation bias in family firms is unequal monetary compensation favoring family employees, often referred to as ‘bifurcated compensation’ (Samara et al., 2019; Verbeke & Kano, 2012). Bifurcation bias especially applies to human assets, but is not limited to this asset category. For example, family firms may have the desire to maintain or even reinforce the family character of the business by prioritizing product lines or manufacturing locations that are part of the family history/

legacy as they were introduced by the founding family or predecessors (Verbeke &

Kano, 2012). Even when these decisions have negative performance consequences, the positive affect related to the family character of this asset category may bias family firms and result in unwillingness to discharge these family assets.

In line with Verbeke and Kano (2012), we view a family firm as bifurcation biased only when this asymmetric treatment of family versus nonfamily assets is embedded in overarching managerial practices, i.e. firm-level routines applied systematically and by default. When this affect-based distinction exists in family firms, it implies organizational dysfunction (Kano & Verbeke, 2018) which could be detrimental for firm-level outcomes, like entrepreneurship.

2.4 Bifurcation bias and the intention‑behavior gap in private family firms When bifurcation bias exists, it generates dysfunctional decision making in family firms leading to several forms of inefficiencies (Majocchi et al., 2018). These inefficiencies arise when assets are wrongly valued by the family, i.e. when the

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uniqueness and value-creating potential of family assets is overestimated and that of nonfamily assets is underestimated (Kano & Verbeke, 2018). When a family firm is unable to recognize the objective value of its assets, unique agency problems can arise due to the employment of family- and nonfamily members, such as those related to the perceptions of organizational justice (Baldridge & Schulze, 1999; Madison et al., 2018). Organizational justice refers to “people’s perceptions of fairness in organizations along with their associated behavioral, cognitive, and emotional reactions” (Greenberg, 2011) and is based on employees’ perceptions of justice and social balancing among individuals and groups within the organization (Adams, 1965; Greenberg, 2011). Safeguarding the commitment and support of both family and nonfamily employees is likely to be more difficult if they do not perceive that affect driven decisions are fair or just. According to equity theory (Adams, 1965), employees compare their own input-outcome ratio to the input-outcome ratio of relevant others. Consequently, employees decide what their fair return should be after balancing their inputs and outcomes with those of their peers. In this context, examples of an employee’s ‘inputs’ are an employee’s quality or quantity of work, experience, commitment, skill, etc. Examples of an employee’s outcomes can be sal- ary, recognition, benefits, status, reputation, etc. Perceived ratio inequality causes individuals to make behavioral and cognitive changes (Adams, 1965; Barnett & Kel- lermanns, 2006; Madison et al., 2018), that might result in organizational inefficiencies. Perceived organizational injustice is particularly relevant in bifurcation biased family firms, where family vs. nonfamily assets are de facto treated asymmetrically.

Perceptions of organizational injustice can have detrimental consequences for both family and nonfamily employees. Namely, when nonfamily employees perceive injustice, they may decrease their entrepreneurial effort or eventually even leave the family firm (Adams, 1965; Madison et al., 2018). Indeed, when non-family employees are treated as ‘second class citizens’ as a result of bifurcation bias (e.g. lower sal- ary, less responsibility, poor career opportunities), this often results in low employee morale and low productivity (Chrisman et al., 2017; Dyer 2006), which hampers the transmission of the family firm’s EI in EA. Also, employees who suspect their supervisor was hired because of a family tie (i.e., bifurcation bias) will exhibit lower levels of organizational commitment (Padgett & Morris, 2005), limiting their effort to put the firm’s EI into practice.

Even family employees may perceive organizational injustice as a result of bifurcation bias (Madison et al., 2018). Especially, when decisions are only based on family status, family employees might involuntary experience they are exploiting their birthright while they might prefer honest and fair judgement apart from their family status (Van der Heyden et al., 2005). This perceived organizational injustice will cause conflict in the family firm (Madison et al., 2018) which is harmful for putting a family firm’s EI into practice. Also, in a bifurcation biased family firm, family members may feel inevitable to the firm and may not be able to leave (Baldridge

& Schulze, 1999), causing them to resort to emotional resignations (Van der Hey- den et al., 2005). In addition, as siblings are generally known to be less altruistic to each other than parents to their children (Becker & Becker, 2009), bifurcation biased resource allocations can induce intra-family conflicts.

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Taken together, bifurcation bias in family firms induces perceived organizational injustice for both family and nonfamily employees. This perceived organizational injustice will result in changes in behavior (e.g. conflict, reduced effort and commitment) and/or cognition (e.g. emotional resignation) of both family and nonfamily employees that could be detrimental for the organization (Madison et al., 2018) and generate a certain amount of unrealized intentions. For example, when a family firm has planned to introduce a new product or service (EI), this generally requires crea- tive ideas from committed employees who generate, promote, discuss, modify, and realize these ideas (Jafri, 2010; Scott & Bruce, 1994). Prior research has shown that perceptions of organizational injustice will decrease the value-creating attitudes and behaviors of family firm personnel such as organizational commitment, in-role job performance, and extra-role citizenship behavior (Barnett & Kellermanns, 2006).

Thus, when bifurcation bias induces feelings of organizational injustice, this planned innovation (EI) might not be (fully) realized, leading to a gap between EI and EA.

Therefore, we propose the following:

Hypothesis 2 Bifurcation bias will moderate the relationship between a family firm’s EI and their EA, in such a way that a family firm’s EI will have a less positive effect on their EA when the level of bifurcation bias increases.

2.5 Economizing on bifurcation bias

Several family business scholars (Kano & Verbeke, 2018; Majocchi et al., 2018;

Verbeke & Kano, 2012; Verbeke et al., 2020) recently proposed that family firms can avoid or mitigate the dysfunctional effects of bifurcation bias. Corrective governance mechanisms (Verbeke et al., 2020) can help bifurcation biased family firms to economize on their bifurcation bias. This may include implementing governance structures that can balance the controlling family’s discretion to make entrepreneurial decisions and the need for restrictions against unfairly prioritizing family assets and in that way putting the family’s interests above the firm (De Massis et al., 2015).

When these economizing mechanisms are present, they can prevent heritage family assets from becoming liabilities (Bennedsen & Foss, 2015). This implies that the dysfunctional effects of bifurcation bias are not equal for all family firms. Recent studies have proposed that the negative effects of bifurcation bias can be mitigated by purposefully exposing family firms to the objective scrutiny of family outsiders (Kano & Verbeke, 2018; Majocchi et al., 2018). Therefore, we argue that the board of directors can act as a mechanism for family firms to monitor the dysfunctional effects of bifurcation bias on the transmission of EI into EA.

This premise is supported by the literature on family firm professionalization which suggests that the instalment of a board of directors is a proper outset to pro- fessionalize the family firm (Dekker et al., 2015; Stewart & Hitt, 2012). In this view, board professionalization is often assessed through the presence of outside directors (Dekker et al., 2015; Songini, 2006) as they fulfill an important role in objectively advising and supervising the firm’s activity. Apart from the increased diversity of perspectives and experiences outside directors bring to the family firm, they are

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especially known for their objective control function (Lane et al., 2006). Particularly in bifurcation biased family firms, outside directors can be very relevant in holding the controlling family accountable for their decisions and actions. For example, one way in which boards with outside directors can objectively monitor family firms is by monitoring strategic execution (Lane et al., 2006). Hence, even when the controlling family tends to be bifurcation biased in their daily operations, outside directors will hold them accountable for the proper implementation of their EI. Also, boards with outside directors tend to objectively evaluate executive compensation (Lane et al., 2006). Thus, even when BB is present, outside directors will prevent that the negative effects of bifurcation bias will affect proper implementation of the firm’s EI. This can be done, for example, by the installment of formal human resource control systems, such as formal recruiting systems or formal performance evaluation systems (Dekker et al., 2015), which provide a counterbalance for the potential negative repercussion of bifurcation bias. As outside directors are appointed by the controlling family, they cannot reduce BB an sich as it is an expression of the controlling family’s non-economic goals (Kano & Verbeke, 2018) and the family expects outside directors to be ‘challenging but supportive’ (Roberts et al., 2005). However, it is by professionalizing the family firm and asking for accountability that outside directors will ‘help the family’ (Ng & Roberts, 2007) in realizing their EI and prevent that dysfunctional effects of BB will thwart the EI–EA relationship.

In sum, the presence of outside directors may be a signal for both family and nonfamily employees that the firm is being professionalized and the controlling family is putting everything in place to prevent negative repercussions of potential bifurcation bias. Also, proper signaling can create a virtuous cycle of reduced bounded rationality (Verbeke & Kano, 2012). According to equity theory (Adams, 1965), reduced perceptions of inequality will diminish employee’s destructive behavioral (e.g. reduced effort and commitment) and cognitive changes (e.g. emotional resignation). Therefore, the signaling function of appointing outside directors to the board can help family firms to reduce the negative repercussions of bifurcation bias and consequently reduce the risk that the family firm’s EIs will not be (fully) realized. In sum, we propose the following:

Hypothesis 3 The negative moderating effect of bifurcation bias on the EI–EA relationship will be reduced when the family firm has a board of directors with one or more outside directors.

3 Research method 3.1 Sample

The sampling frame was taken in the 2012–2013 period from a wider study investigating succession and governance issues in private family firms in Flanders, which is the northern region of Belgium. In this paper, a firm is classified as a family firm if (1) at least 50 per cent of the shares are owned by the family, the company is family managed or the family is responsible for the strategic choices or succession

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decisions, or (2) at least 50% of the shares are owned by the family, the company is not family-managed but the CEO perceives the firm as a family firm. A total number of 3600 firms were randomly selected from a family business database, and a survey was mailed to the CEO. A response rate of 12.5% resulted in 452 surveys.

In our final sample we excluded non-family firms (n = 39), and cases with missing values on relevant variables (n = 128). To reduce concerns related to measuring both predictor and criterion in the same survey,¹ we also excluded firms who indicated their intentions have changed over time (n = 46). Furthermore, we excluded firms that employed less than 3 people (n = 16) as our argumentation might not (fully) apply to these very small family firms. This resulted in a final sample of 223 cases.

3.2 Variables and measures

Entrepreneurial intentions We defined EI as the firm’s willingness to create new value within an existing organization. This is expressed by the firm’s intention to engage in innovative, proactive and risky actions. In line with prior research, we operationalized the EI construct using an adaptation of the nine-item Miller/Covin and Slevin (1989) Entrepreneurial Orientation scale. Since the Miller/Covin and Slevin (1989) scale incorporates items that reflect both dispositions and behaviors (Covin & Lumpkin, 2011), we transformed all items to intentions. For example, original items of the Miller/Covin and Slevin (1989) scale like “In dealing with its competitors, my firm typically initiates actions to which competitors then respond”, were transformed to “In general, my firm has the intention to initiate actions to which competitors then respond”. The nine-item scale had a Cronbach’s alpha value of 0.84 suggesting high internal consistency and reliability. Also, we included a question in our survey to determine whether the firm’s EI were stable over time. We deleted those cases who indicated that their intentions changed during the last few years (46 cases).

Entrepreneurial Actions. Miller and Friesen’s (1982) seven-item index was used to measure the firm’s EA. Here, the focus is on entrepreneurial actions that were effectively carried out by the firm rather than their mere intentions (Zahra & Covin, 1995). The Cronbach’s alpha for the EA scale was 0.89.

Bifurcation bias As previously discussed, bifurcation bias can be defined as the de facto asymmetric treatment of family vs. nonfamily assets (Verbeke & Kano, 2012).

As there is no generally accepted scale to measure bifurcation bias (Madison et al., 2018) and recent calls have been made for an accurate operationalization and quantification (Jennings et al., 2018), we developed a 7-item scale based on how bifurcation bias is conceptualized in the literature. Namely, we assessed whether ‘heritage assets’ (Kano & Verbeke, 2018) tend to be favored, in general, for both human assets (e.g. personnel selection, compensation, promotion) and non-human assets (e.g. the family character of the business). More specifically, we use the following 7 items to

1 To further alleviate this concern, we also did a robustness test (see result section) where EA was measured with secondary data (in line with Kreiser et al., 2019) enabling us the induce a time lag between the measurement of EI and EA.

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measure bifurcation bias: (1) in delegating responsibilities and selecting new managers, being a family member is a big advantage, (2) family members deserve other remuneration than non-family members, (3) providing jobs for the family is one of the main goals of the firm, (4) successors need to be chosen from the family, (5) creating/saving employment for the family is a main objective, (6) independence from outsiders in management is an important objective for the firm, (7) continuity of the family character is an important objective for the firm. The respondents were asked to indicate to what extent they agree with each item on a 7-point Likert scale (1 = totally disagree, 7 = totally agree). To assess the structure of our data and determine the suitability for factor analysis, the Bartlett’s test of sphericity (BTS) and the Kaiser–Meyer–Olkin (KMO) measure of sampling adequacy were performed. Low p values (e.g., p < 0.05) indicate that an identity matrix does not exist, and that the data are appropriate to perform a factor analysis. The KMO statistic quantifies the intercorrelations among inventory items, wherein values approaching 1.0 indicate high data appropriateness for factor analysis (Hair, 1998). For our data, the BTS was significant (p < 0.000), and the KMO approached 1.0 (0.714). After performing an exploratory factor analysis, where all items loaded on one single factor, the questions were summed into one single index. The Cronbach’s alpha reliability coefficient of our measure of bifurcation bias was found to be 0.69, which is an acceptable level for reliability² (Cho & Kim, 2015). Next, we validated our self-constructed measure for bifurcation bias using convergent validity (i.e. the extent to which alternative measures of the same concept are in agreement). Since there are no existing measures for bifurcation bias available (Madison et al., 2018), Kano and Verbeke (2018) recently suggested some options to proxy bifurcation bias. Given that the proportion of family leadership has been suggested as a proxy for bifurcation bias (Kano &

Verbeke, 2018), we look at the correlation between our scale for bifurcation bias and the percentage of family leadership (proportion (%) family managers in the management team). Here, we find a positive correlation (0.1640, p < 0.05), which is in line with our expectations as bifurcation biased family firms tend to favor more family involvement in management positions.

Control variables To ensure proper model specification, we included several con- trol variables in our model. In particular, we included numerous firm-level variables such as firm size (Casillas & Moreno, 2010), measured as the natural logarithm of the number of full-time employees in 2012; firm age (Arend, 2014), measured as the natural logarithm of the years since the firm was founded; firm industry (Casil- las & Moreno, 2010), measured through four dummy variables that allow for five major business lines to be differentiated: manufacturing, construction, wholesale, retail, and services; solvency (Wiklund & Shepherd, 2005), measured as the firm’s capital structure in 2012 (i.e. total equity divided by total assets); past performance, measured as the 3-year ROA average (2010–2012), because it seems that past performance affects a firm’s actual entrepreneurial behavior (Cruz & Nordqvist, 2012;

Tsai, 2001); liquidity, measured as the 3-year average of the firm’s current ratio

2 According to Cho & Kim (2015), no strict cut-off value should be applied but in general Cronbach alpha’s around or above 0.7 are seen as acceptable levels of alpha.

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(2010–2012). We control for liquidity because high liquidity firms are more inclined to put their EI into practice (Wiklund & Shepherd, 2005). All control variables were drawn from the Belfirst database of Bureau Van Dijk.

Outside directors (OD) was measured using a dummy variable which equals “1”

if the family firm has a board of directors with at least one outside nonfamily director (i.e. a nonfamily director who is not involved in day-to-day operations), and “0”

otherwise.

3.3 Common method bias

Since some of the variables in our model stem from the same data source, any observed covariance may be the result of common method bias (Podsakoff, 2003, 2012). Following the vast literature on the subject, common method bias can be alle- viated both procedurally or statistically (for a comprehensive review see Podsakoff et al., 2003). From a procedural perspective, great care was taken in the design of the survey. Before sending out the survey, we simplified complex statements, eliminated any ambiguous and unfamiliar questions and performed a pre-test of the questionnaire with experts in the field. Based on the comments of this pre-test, any remain- ing ambiguities in scale items were corrected. Furthermore, we concealed interest in criterion and predictor variables in the cover story of the questionnaire and sepa- rated the measurement of predictor and criterion variables in the survey.

From a statistical standpoint, Siemsen et al. (2010) found that interaction effects cannot be artificially created through Common Method Variance (CMV). Since our final model includes a 3-way interaction effect, it is doubtful that the specific rela- tionships in our model are part of the individual respondents’ cognitive maps and theories-in-use which substantially eases common method concerns (Chang et al., 2010). Moreover, as CMV is found to deflate regression estimates of interaction effects (Siemsen et al., 2010), the highly significant interaction effects in our model should be considered as strong evidence that the effect exists. To further ease our concerns for CMV, we performed three ex-post tests. First, we performed a Har- man’s single-factor test (Harman, 1967), resulting in five factors with eigenvalues greater than one. Moreover, none of the single factors explained over 33% of variance in the data which is a finding that reduces common method concerns. Second, we estimated an unmeasured latent method factor model on the three latent variables of our research model for which CMV could be a potential problem (EI, EA and bifurcation bias) (Podsakoff, 2003). The results of this test showed a common variance of 21% (0.46²). Third, we used a common marker variable technique (Lin- dell & Whitney, 2001). In doing so, we used Covin et al.’ s (2006) five-item scale to capture ‘participative decision making’ (PDM), defined as the degree to which the firm’s main strategic and operating decisions are made through consensus seek- ing versus individualistic or autocratic processes by the formally responsible executive (Covin et al., 2006). PDM items were not (for bifurcation bias) or only weakly (for EI and EA) correlated with our main variables and are expected to share potential common method variance (Podsakoff, 2012). This analysis shows a common

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variance of only 11% (0.33 × 0.33). In sum, both ex-ante measures and ex-post tests allow us to conclude that CMV is unlikely to account for the results in this paper.

3.4 Non‑response bias

Since prior literature advocates that late respondents are comparable to non-respondents (Kanuk & Berenson, 1975), the late respondents were treated as representative of non-respondents and were compared with the early respondents. We used t-tests to analyze potential differences between early respondents and late respondents who answered the survey in the second round after sending a reminder. These t-tests indicated no statistically significant differences between the late and early respondents, alleviating our concern for potential non-response bias.

4 Analysis and results

Descriptive statistics and correlations are presented in Table 1. The average family firm in our sample employs 25 people and is 28 years old. Also, 16.14% of the family firms in our sample have a board with outside directors. The mean value for a firm’s EI, on a scale from 9 (low EI) to 63 (high EI) was found to be 34.46 with a standard deviation of 9.26. For EA, the mean value was 25.15 on a scale from 7 (low EA) to 49 (high EA), with a standard deviation of 9.12. Furthermore, the average firm in our sample has a moderate score for BB. On a scale from 7 (low BB) to 49 (high BB), a mean value of 27.07 was found with a standard deviation of 7.03.

The correlations clearly show that EI and EA are highly correlated (r = 0.74, p < 0.01), which gives a first indication for a positive relationship between EI and EA. The other correlations were moderate and did not indicate multicollinearity problems in our sample. Before running all robust linear regression analyses, we also tested the measurement model (see Fig. 1), which included the latent variables used in this study (EI, EA and BB).

Given our sample size (n = 223), this model achieved an acceptable fit (χ² [227] = 730.24, p = 0.000, SRMR = 0.08, RMSEA = 0.099), indicating that the facto- rial structure of these variables is satisfactory (Hair et al., 1998; Hu & Bentler, 1995).

To test our hypotheses, we used robust linear regression analysis (see Table 2) and use robust standard errors to correct for potential heteroscedasticity. Model 1 offers a test of the control variables only. Model 2 includes the direct effect of EI, bifurcation bias, and outside directors on EA. Results indicate that EI has a direct positive and statistically significant effect on EA (β = 0.706, p < 0.01), which supports our first hypothesis. Bifurcation bias (BB) and the presence of outside directors do not have a direct significant effect on a firm’s EA. Hypothesis 2 argues that bifurcation bias will moderate the relationship between a family firm’s EI and their EA, in such a way that a family firm’s EI will have a less positive effect on their EA when the level of bifurcation bias increases. To test this interaction effect, we mean-centered the interaction terms to minimize multicollinearity problems (Aiken et al., 1991).

Inspection of the variance inflation factors (VIFs) showed that multicollinearity was

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not a concern. All VIF coefficients were lower than 3 (Netter et al., 1990). To test hypothesis 2, the following regression formula was used: EA = α + β₁ EI + β₂ BB + β₃ EI*BB + β₄ OD + δ Controls + ε. As seen in Model 3, the interaction coeffi- cient (EI*BB) was negative and statistically significant (β = − 0.009, p < 0.10). Thus, hypothesis 2 seems to find some weak support. It can be seen in Fig. 2 that in firms with a high degree of bifurcation bias, the positive relationship between EI and EA is less intense than in firms with lower levels of bifurcation bias. Nevertheless, these results require further examination by including the 3-way interaction between EI, BB, and OD in our model.

Model 4 presents the test of hypothesis 3. Here, the following formula was used: EA = α + β₁ EI + β₂ BB + β₃ OD+ β₄ EI*BB + β₅ EI*OD+ β₆ BB*OD + β

7 EI*BB*OD + δ Controls + ε. The results of Model 4 indicate that the three-way interaction is positive and statistically significant (β = 0.061, p < 0.01), which supports hypothesis 3. This result supports our logic that the negative moderating effect of bifurcation bias on the EI–EA relationship will be reduced when the family firm has a board of directors with at least one outside nonfamily director. Even more, Fig. 3 shows the slopes for the EI–EA relationship for different combinations of BB and OD. Here, we see that the EI–EA relationship is stronger for family firms that combine high levels of bifurcation bias with the appointment of nonfamily directors compared to family firms that combine high levels of bifurcation bias with a board consisting of only family members. This paradoxical finding provides empirical support for the theoretical notion that family firms can economize on their BB (Kano &

Verbeke, 2018; Verbeke & Kano, 2012; Verbeke et al., 2020). Thus, supporting our central reasoning that appointing outside directors to the board serves as a proper signaling and professionalization mechanism that prevents BB to negatively affect the EI–EA relationship.

The lower-level interaction coefficients in Model 4 should be interpreted with caution as they represent conditional effects and should not be interpreted as main effects. For example, the coefficient for BB in Model 4 of Table 2 is positive and significant (β = 0.125, p < 0.05). Thus, the positive and significant regression coefficient for BB in Model 4 (β = 0.125, p < 0.05) means that BB has a positive effect on EA only when both EI and OD are zero. From a theoretical point of view, this is not a realistic scenario as entrepreneurship is an intentional process and EA is the result of a planned behavior, meaning that it is not something that ‘just happens’

without intentions (Krueger et al., 2000). Also from a statistical point of view, there are no cases in our sample where EI is zero. In sum, the coefficient for BB in Model 4 cannot be interpreted as the direct effect of BB on EA as the effect of BB on EA is conditional on EI and OD. All other lower-level interaction coefficients in Model 4 should be interpreted in the same manner.

4.1 Robustness analyses

We checked the robustness of our results by using a different proxy for our dependent variable (EA). In line with Kreiser et al. (2019) we believe that secondary data can be used to proxy firm-level entrepreneurial activity. More specifically, financial

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Table 1 Descriptives and correlations N = 223 EA entrepreneurial action, EI entrepreneurial intention, BB bifurcation bias, OD outside director * , **, ***significance at 0.10 0.05 and 0.01, respectively MeanStdev12345678910111213 1. EA25.159.121 2. EI34.469.260.74***1 3. BB27.077.030.17**0.17**1 4. OD0.160.370.17**0.23***− 0.061 5. Ln firm size2.910.780.100.12*− 0.030.111 6. Return on assets5.317.36− 0.08− 0.090.030.020.041 7. Current ratio2.553.05− 0.11*− 0.110.03− 0.05− 0.070.17***1 8. Manufacturing0.290.45− 0.16**0.24***− 0.060.070.18***− 0.070.031 9. Construction0.240.42− 0.13*− 0.15**− 0.00− 0.10− 0.040.070.03− 0.35***1 10. Wholesale0.140.350.060.02− 0.10− 0.000.060.06− 0.06− 0.25***− 0.22***1 11. Retail0.120.330.04− 0.010.050.02− 0.18***0.010.05− 0.24***− 0.21***− 0.15**1 12. Ln firm age3.220.480.020.04− 0.000.020.21***− 0.20***0.020.19***− 0.13*0.13*− 0.081 13 Solvency45.3423.50− 0.10− 0.050.11*− 0.08− 0.010.25***0.59***0.060.00− 0.030.080.21***1

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ratios best capture a behavioral, corporate strategy perspective of entrepreneurship, as they reflect what a firm actually did with its resources (Kreiser et al., 2019) rather

Fig. 1 Conceptual model Table 2 Robust linear

regression analysis Model 1 Model 2 Model 3 Model 4

β β β β

Controls

Manufacturing 4.558** 0.904 0.961 1.600

Construction 0.339 0.715 0.899 1.290

Wholesale 3.721* 2.236 2.183 2.231

Retail 3.891* 2.471 2.465 3.491**

Firm size 0.951 0.257 0.148 0.358

Return on assets − 0.075 − 0.010 − 0.016 − 0.014

Current ratio − 0.209 0.034 0.003 − 0.020

Firm age − 0.763 − 0.051 − 0.035 − 0.440

Solvency − 0.020 − 0.030 − 0.027 − 0.027

Variables

EI 0.706*** 0.713*** 0.676***

BB 0.085 0.092 0.125**

OD 0.100 0.143 − 3.083**

EI*BB − 0.009* − 0.010**

EI*OD 0.396***

BB*OD − 0.693***

EI*BB*OD 0.061***

R² 0.073 0.562 0.567 0.590

F 2.28** 31.59*** 29.07** 25.16***

N 223 223 223 223

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than their EI or disposition towards entrepreneurship. Kreiser et al.’s (2019) behavioral approach of entrepreneurship aligns with our theoretical definition of EA. There- fore we used secondary data, namely ‘Firm Growth’ (measured as the compound annual growth rate for the firm’s total assets between 2012 and 2015) as a proxy for EA since firm-level entrepreneurial activity has been found to play an important role in the growth rate of family firms (Stenholm et al., 2016). As there is ideally a time lag between the measurement of EI and EA, this approach also reduces potential concerns related to measuring both predictor (EI) and criterion (EA) at the same time. EA and firm growth seem to be positively correlated (0.11, p < 0.10), which is in line with prior research and a first indication that firm growth can be used as a proxy for EA. As EA normally precedes firm growth, we take into account a time lag between the moment of our data collection (2012–2013 period) and the measurement of firm growth (2012–2015 period). We ran the same robust linear regression analyses as outlined above but now we use firm growth as a dependent variable, which alleviates potential worries for CMV.

We find support for our first hypothesis (i.e. the same as Model 2 in Table 2 but with Firm Growth as dependent variable) as the coefficient for EI is positive and significant (β = 0.0016, p < 0.10). Also, the findings related to our second hypothesis are confirmed as the coefficient for EI*BB is negative and significant (β = -0.0002, p < 0.05) (i.e. the same as Model 3 in Table 2 but with Firm Growth as dependent variable). Although the EI*BB*OD coefficient in the alternative Model 4 is positive, it is not significant which is not in line with our main findings in Model 4 of Table 2.

In general, these post hoc analyses with firm growth as a dependent variable show that our main findings are robust, suggesting that EI is an important driver of EA but bifurcation bias can hamper the transmission of EI into EA. The reason why we do not find support for our third hypothesis in this robustness check, can be explained

Fig. 2 Interaction between entrepreneurial intentions (EI) and bifurcation bias (BB)

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by the fact that firm growth is not a perfect proxy for EA. Also, the R² values of the robustness tests appear to be lower than the reported R² values in Table 2, which might be an indication that firm growth is not a perfect proxy for EA. The results of the robustness test with Firm Growth as dependent variable are available upon request.

5 Discussion and conclusion

In entrepreneurship literature, there is an implicit assumption that EI will lead to EA (Fayolle & Liñán, 2014; Fayolle et al., 2014; Liñán & Fayolle, 2015). Although this is often the case, the assumption that intentions are sufficient to bring about the desired outcome has been widely challenged in the psychological literature (Godin et al., 2005; Sniehotta et al., 2005) and recently in the entrepreneurship literature (Kautonen et al., 2015; Van Gelderen et al., 2015). While family firms continuously need to balance family- and business goals, this paper differentiates and links EI and EA in a family business context. The results of this study offer support for the hypotheses that EI is indeed an important determinant of EA in family firms, but the positive effect decreases when the level of bifurcation bias increases. These findings are encouraging in a number of respects.

First, we provide empirical support for the entrepreneurial intention-behavior relationship by focusing on EI and EA in existing organizations where the scant prior literature used a more narrow definition of entrepreneurial activities, i.e.

new venture creation. When it comes to entrepreneurship in existing organiza- tions, family firms are the dominant organizational form worldwide (Gómez-Mejía et al., 2007) but their distinctive attributes are often overlooked in entrepreneurship

Fig. 3 Interaction between entrepreneurial intentions (EI), bifurcation bias (BB), and outside directors (OD)

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literature. We have discussed why distinctive family firm attributes need special attention when linking intentions to actions since several factors may obstruct or hinder smooth implementation of these intentions leading to an ‘intention-behavior gap’, (Godin et al., 2005; Sniehotta et al., 2005). This argument is closely linked to the ongoing debate in the entrepreneurial orientation (EO) literature around the controversial question whether EO, a firm-level construct of entrepreneurship, must be seen as a dispositional or a behavioral construct (Covin & Lumpkin, 2011; Covin

& Wales, 2011). Without choosing either side in this discussion, this paper throws in to this matter by expanding it to an intention-behavior debate. Namely, we show that both conceptualizations of EO are highly related since a disposition or intention towards entrepreneurial behavior often leads to the preferred behavior. Nevertheless, we show that certain variables, like bifurcation bias, may cause this relationship to be less straightforward than expected as family firms can ‘get derailed’ (Gollwit- zer & Sheeran, 2006) while pursuing their entrepreneurial intentions resulting in an intention-behavior gap. In other words, family firms may plan to act entrepreneuri- ally (EI), but in the end these plans may not be (fully) realized due to their bifurcation bias. For example, investments in R&D and innovation often require a long and resource consuming process in order to be successful. So, having the intention to invest in R&D is only a first step to goal realization. While bifurcation bias is known to incite perceptions of organizational injustice (Madison et al., 2018) and thus undermines employee’s effort and commitment towards these investments, it can lead to an intention-behavior gap. While most EO researchers are interested in the effect of EO on firm performance, we believe our results may help them to gain a better understanding of the inconclusive EO-performance results (Rauch et al., 2009) by taking into account the proven gap between EI and EA.

Second, our study contributes to the heterogeneity debate in family business literature (Chua et al., 2012; Verbeke et al., 2020). We empirically show that individual differences exist between family firms, not only in terms of observed bifurcation bias, but also in the extent to which this bifurcation bias affects the entrepreneurial process. Apart from Madison et al.’s (2018) self-created dummy variable, there are no other existing measures of bifurcation bias that take into account family firm heterogeneity. Therefore, we introduced a measure of bifurcation bias that allowed us to construct a continuous variable, which is in line with recent conceptualizations of bifurcation bias (Verbeke et al., 2020). Building on this heterogeneity perspective, our results support the notion that bifurcation bias can entail harmful inefficiencies that obstruct the entrepreneurial process. On the other hand, we show that bifurcation bias is not always a bad thing and family firms are able to ‘economize’ on their bifurcation bias as suggested by several authors (Kano & Verbeke, 2018; Verbeke &

Kano, 2012; Verbeke et al., 2020).

Third, by introducing the board of directors into the EI–EA relationship, we add insights to the ongoing debate on potential economizing mechanisms for bifurcation bias. Verbeke and Kano (2012) were the first to suggest that family firms are able to economize on their bifurcation bias and could safeguard themselves against dysfunctional effects. Majocchi et al. (2018) argued that opening the board to outside directors could be a successful way to reduce the dysfunctional effects of bifurcation bias. Our study adds to this dialogue by empirically showing how the appointment

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of outside directors helps to reduce the EI–EA gap for bifurcation biased family firms. Moreover, our findings demonstrate that the relationship between EI and EA is even stronger for highly bifurcation biased family firms than for family firms with lower levels of BB when they have outsiders on their board of directors. Thus, our results show that BB can be seen as a bivalent attribute of family firms—a unique, inherent feature of an organization that is the source of both advantages and disad- vantages (Tagiuri & Davis, 1996). Which side of the discussion prevails, depends on the installment of corrective governance mechanisms like the appointment of outside directors.

Future research can further investigate the entrepreneurial process in private family firms by investigating how other family-related variables might intervene in the EI–EA relationship. For instance, the extent and outcomes of bifurcation bias may be contingent on specific family-related variables, such as values (García-Álvarez

& López-Sintas, 2001), ownership and management structures (sibling partnerships, cousin consortium, controlling owner, etc.) or identity concerns (Milton, 2008). As suggested by Kano and Verbeke (2018), other economizing mechanisms such as for example ‘operational meritocracy’ (the extent to which operational responsibilities in the family firm are entrusted to the most competent managers regardless of their relation to the family (Leleux & Glemser, 2011) may also prevent the dysfunctional effects of BB to intervene in the entrepreneurial process of family firms. Also, family business scholars widely acknowledge the presence of noneconomic objectives in family firms. In this line of thought, the socioemotional wealth (SEW) perspective, referring to the non-financial aspects of the firm that meet the family’s affective needs (Gómez-Meija et al., 2007) has become an important paradigm in the family business field to understand and explain family firm behavior (Berrone et al., 2012;

Gómez-Mejía et al., 2007, 2011, 2014; Kellermanns et al., 2012; Schepers et al., 2014). Future research might delve into the linkages between SEW and bifurcation bias in order to increase our understanding of the EI–EA relationship in private family firms. Recent studies (Kano & Verbeke, 2018; Samara et al., 2019) have empha- sized the link between SEW and bifurcation bias and invited future research to further elaborate on this link as the direction of this link is not always self-evident.

We believe that future research on SEW and bifurcation bias could help sharpen and refine their (mutual) effects on the EI–EA relationship. Also, further refining the operationalization and quantification of bifurcation bias will be a challenge for future research. Our self-constructed scale for bifurcation bias is a first attempt to operationalize this phenomenon.

Despite we carefully designed our study, this research still suffers from some limitations which open opportunities for future research. First of all, our study is based on a cross-sectional design which means we only look at a snapshot to explore the EI–EA relationship in private family firms. Since entrepreneurship is clearly an intentional process of planned behavior, intentions have to precede actions.

We aim to alleviate this concern by deleting those cases who indicated that their intentions changed during the last few years. Consequently, our sampling frame only includes family firms whose EI have been stable over time which decreases our concerns related to the cross-sectional design of our study. Therefore, future research can investigate the EI–EA relationship by introducing a time lag between

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the measurement of EI and EA because this may reduce potential social desirability bias. Next, this study takes an important step towards an increased understanding of a family firm’s EI and their realized EA. While there is a relationship between EI and EA, our results suggest that this relationship is more complex since bifurcation bias can result in perceived organizational injustice which reduces employee’s effort and commitment towards implementing their EI. Nevertheless, our study did not investigate the ‘successfulness’ of the firm’s EA. Therefore, future research may investigate the extent to which EA mediates the relationship between a family firm’s EI and its actual financial performance.

Finally, our study has an important practical implication. Namely, entrepreneurs and consultants will benefit from a better understanding of how intentions are transformed into actions. Even more important, our intention-based model yields useful practical applications in private family firms since we have shown that a family firm context may impede the transmission of EI into EA. Since family firms are susceptible to making affect-driven decisions (Kano & Verbeke, 2018), consultants must be aware of these expressions of bounded rationality when they are involved in the entrepreneurial process of private family firms. If left unremedied, bifurcation biased decisions will lead to lower levels of entrepreneurship and ultimately to a decline in performance (Rauch et al., 2009). Opening the board to outside directors can help bifurcation biased family firms to overcome these problems.

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