Abstract
In this article, we look at how a family firm signal affects decision making of nonprofessional investors. Grounded in prospect theory and supported by empirical evidence from a choice-based experimental study (N = 418) backed by a qualitative study, we demonstrate a behavioral bias induced by the family firm signal. This family firm bias shifts nonprofessional investors’ preferences toward the high-risk alternative in a choice situation. Accordingly, processing the family firm information seems to moderate risk aversion as risk avoidance is decreased in the gain domain, while risk seeking is reinforced in the loss domain due to trust and longevity associations tied to the family firm signal.
Should family firms communicate their family nature to third parties or not? This question is highly relevant for researchers and family entrepreneurs alike, as family firms show a very heterogeneous behavior in dealing with the communication of their family firm status. While some family firms identify themselves as family firms and exhibit the family nature publicly, others remain silent and conceal their family nature (e.g., Micelotta & Raynard, 2011), behaving more like nonfamily firms (Stewart & Hitt, 2012). Understanding the foundations of this decision demands answers to another question: How does the perception of the family nature of the firm impact decisions of major stakeholders?
While family business scholars have started to investigate topics such as identity, image, and reputation (Zellweger, Eddleston, & Kellermanns, 2010; Zellweger, Eddleston, Kellermanns, & Memili, 2012; for timely reviews see Beck, 2016; Binz Astrachan, Botero, Astrachan, & Prügl, 2018; Sageder, Mitter, & Feldbauer-Durstmüller, 2016), “empirical findings on how stakeholders perceive family firms and the effects of a family firm’s image and reputation remain unclear” (Sageder et al., 2016, p. 2). Initial empirical evidence suggests that family firms are seen as more trustworthy and exhibit a higher longevity than nonfamily firms. The trustworthiness of family firms is a key association, which has the strongest empirical support to date (Beck & Kenning, 2015; Beck & Prügl, 2018; Binz, Hair, Pieper, & Baldauf, 2013; Carrigan & Buckley, 2008; Kashmiri & Mahajan, 2014; Krappe, Goutas, & von Schlippe, 2011; Orth & Green, 2009). This is also mirrored in the latest Edelman Trust Barometer: a large majority of people trust family firms more than nonfamily ones (“Edelman Trust Barometer,” 2017). The longevity of family firms is another key association (Krappe et al., 2011; Micelotta & Raynard, 2011) since the owning family acts as a guardian of survivability across generations (Binz Astrachan, & Astrachan, 2015; Poza, 2013), thus focusing on long-term financial goals (Anderson & Reeb, 2003). While we do have some empirical evidence regarding perceptions of family firms by consumers (e.g., Beck & Kenning, 2015; Binz et al., 2013), and employees (e.g., Hauswald, Hack, Kellermanns, & Patzelt, 2016; Kahlert, Botero, & Prügl, 2017), other major stakeholder groups, such as investors, remain largely unexplored.
In particular, theory and empirical evidence as to how and why the family nature of a firm impacts decision making under risk in a choice situation is still missing. As family firms have the potential to project a unique identity (Zellweger et al., 2010, 2012) that is shaped by the owning family, they seem to be perceived differently compared to their nonfamily counterparts, which in turn could result in a preference shift (all other things being equal) when a choice is made between a family firm and a nonfamily counterpart in situations under risk and uncertainty (hereinafter “risky” situations 1 ).
We investigate this issue in the context of investment decisions of nonprofessional investors by running an experimental study backed by a qualitative study. Following the demand for more experimental studies in family business research (Evert, Martin, McLeod, & Payne, 2016), we use a choice-based experimental design (Hsu, Wiklund, & Cotton, 2017). This approach allows us to empirically test the causal effect of a firm’s family nature in the context of risky decisions. Grounded in prospect theory (Kahneman & Tversky, 1979), boundedly rational individuals (Simon, 1972) tend to respond to uncertainty by adopting a variety of judgmental heuristics (e.g., Tversky & Kahneman, 1973). These cognitive shortcuts simplify the valuation process, but also introduce a range of behavioral biases. We aim to explore and test a potential bias induced by a firm’s family nature on risky decisions made by nonprofessional investors.
Our results clearly show evidence for a “family firm bias” in risky decisions: the family firm signal leads to a preference shift toward more risky choices, indicating a moderating effect on risk aversion under gains and losses—in both cases toward more risk seeking. More specifically, the family nature of a firm mitigates risk aversion in the gain domain and reinforces risk seeking in the loss domain. Furthermore, we find support for longevity and trust as being central decision-relevant perceptions tied to the family firm signal.
Thus, our article contributes to the literature primarily in the following ways: (a) We extend existing knowledge by examining preferences in a choice setting driven by a firm’s family nature. Furthermore, the effects of central family firm perceptions such as trust and longevity in the context of risky decisions are clarified. (b) We offer initial insights on the perceptual consequences of the family firm cue in the hitherto overlooked yet relevant external stakeholder group of nonprofessional investors and inform existing research on other stakeholder groups about the preference shift driven by the communication of the firm’s family nature. (c) We extend prospect theory by focusing on the editing phase of decision making, an under-researched area in comparison to the well-investigated evaluation phase of decision making in prospect theory (Levy, 1996, 1997). (d) We use an experimental study, a method largely underrepresented so far in family business research, to increase the variety of methods and thus the robustness of cumulative findings.
Theoretical Development
Family Business Research and the Perception of Family Firms
As family firms integrate elements from both, the family and the business to various degrees (Sundaramurthy & Kreiner, 2008), they may be particularly well-positioned to create unique identities. This phenomenon has inspired researchers to deepen the understanding of potential advantages family firms can generate by building a strong family firm identity (Zellweger et al., 2010). This involves the family admitting that it is a family firm by asking itself “are we a family firm?” (Zellweger et al., 2010, p. 57), and the effects of the decision made within the family firm to communicate that firm identity and to project a family firm image (Zellweger et al., 2012).
By turning the perspective from an internal, identity-based view to an external, reputation-based view focusing on the stakeholder’s perception, family business research is still in its infancy. A growing number of family firms around the world, however, have started to explicitly promote their nature as a family firm to their stakeholders (see, for example, “Foster Farms – Family Owned Since 1939”; “Glenfiddich – Family Run Since 1887”; or one of the pioneers in capitalizing on its family firm identity: “SC Johnson – A Family Company”). So far, only a few researchers have addressed this new communication paradigm and found initial empirical evidence of its effects (see Sageder et al., 2016, for a timely review). While those studies addressing the external perspective of family firms so far have mainly focused on the associations with and perceptions of family firms (Beck & Kenning, 2015; Beck & Prügl, 2018; Binz Astrachan & Astrachan, 2015; Binz et al., 2013; Gallucci, Santulli, & Calabrò, 2015; Krappe et al., 2011; Orth & Green, 2009), none of them has examined whether it is the family firm signal in general, or those identified perceptions in particular, that influence the decision making of external stakeholders. This constitutes a significant gap in research and at the same time calls for choice-based experiments. It is therefore particularly relevant to examine situations in which decisions are made under risk and uncertainty, as existing studies to date have primarily addressed perceptions of offerings with comparably low levels of risk and uncertainty involved (like fast-moving consumer goods, e.g., in a grocery setting [Orth & Green, 2009]; or perceptions of retail brands [Beck & Kenning, 2015]). Filling this gap facilitates a better understanding of the effect of the family firm signal.
Furthermore, literature on the effects of signaling the family firm identity so far has mainly focused on two primary stakeholder groups: consumers (Beck & Kenning, 2015; Binz et al, 2013; Binz Astrachan & Astrachan, 2015; Carrigan & Buckley, 2008; Orth & Green, 2009) and prospective employees (Botero, 2014; Covin, 1994; Hauswald et al., 2016; Kahlert et al., 2017). Thus, the impact of perceptions of the family firm signal and associations with it on major stakeholders such as nonprofessional investors remain unexplored, thereby constituting a further gap in the literature. That is why little is known as to whether the nature of the firm of an investment target (family vs. nonfamily firm) and related associations linked to risk perceptions such as trustworthiness and long-term orientation impact risky investment decisions.
The trustworthiness of family firms is a key association with family firms—and the one with the strongest empirical support to date (Beck & Kenning, 2015; Binz et al., 2013; Carrigan & Buckley, 2008; Kashmiri & Mahajan, 2014; Krappe et al., 2011; Orth & Green, 2009). 2 Trustworthiness is strongly linked to risk perception, in particular when emphasizing the common understanding across disciplines that trust is especially important in risky situations (Chaudhuri & Holbrook, 2001; Matzler, Grabner-Kräuter, & Bidmon, 2008; Mayer, Davis, & Schoorman, 1995).
The second characteristic ascribed to family firms predominantly found in literature is long-term orientation, formally defined as “the tendency to prioritize the long-range implications and impact of decisions and actions that come to fruition after an extended time period” (Lumpkin, Brigham, & Moss, 2010, p. 241). This holds especially true for family firms since the owning family acts as a guardian of longevity and survivability across generations (Binz Astrachan & Astrachan, 2015; Poza, 2013), emphasizing long-term financial goals (Anderson & Reeb, 2003). This attitude of family firms is also a major association of stakeholders with the family nature of the firm (Krappe et al., 2011, Micelotta & Raynard, 2011). Family firms tend to take fewer risks than nonfamily firms (Naldi, Nordqvist, Sjoberg, & Wiklund, 2007; Zahra, 2005) due to their long-term orientation (Lumpkin et al., 2010). Accordingly, the longevity association tied to family firms can also alter expectations regarding a firm’s future behavior (Ganesan, 1994), and thus stakeholders’ expectations regarding the risk-taking behavior of family firms.
While we know about these central associations that have hitherto been linked to the family firm signal, we do not know how associations of trust and longevity of the investment target impacts decision-making under risk. The objective of this study is to close the gaps outlined above by exploring the causal impacts of explicitly communicating the family nature of a firm on preference formation of nonprofessional investors in a decision situation characterized by risk and uncertainty. Thus we raise the question: How does a firm’s family nature affect risk-related decision-making preferences (in the context of investment decisions of nonprofessional investors)? Building on this and combining quantitative and qualitative data, we start to delve more deeply into the underlying cognitive processes exploring why the family firm signal influences the choice of individuals in the context of risky decisions.
Prospect Theory and Decision Making
We ground our theoretical argumentation in prospect theory (Kahneman & Tversky, 1979), which has already been used to contribute to the development of the theory of the family enterprise (e.g., Chrisman & Patel, 2012; Kotlar, Signori, De Massis, & Vismara, 2017). In their general framework for individuals’ risk-related decision making (Baker & Nofsinger, 2002; Hsu et al., 2017; Kahneman & Tversky, 1979), Kahneman and Tversky (1979, p. 274) argued that “[p]eople normally perceive outcomes as gains and losses, rather than as final states of wealth or welfare. Gains and losses, of course, are defined relative to some neutral reference point.” This phenomenon was labeled as the “framing effect” (Kahneman, 2003; Kahneman & Tversky, 1984; Tversky & Kahneman, 1986). When individuals are exposed to the domain of gains (i.e., positive frame), they feel that they have more to lose and become more sensitive to potential future losses than potential future gains (Sitkin & Pablo, 1992); thus, they behave in a more risk-averse way. In contrast, individuals exposed to the domain of losses (i.e., negative frame) become more sensitive to future gains and thus behave more risk-seeking in order to recoup their losses (Kahneman & Tversky, 1979; Thaler & Johnson, 1990). This phenomenon is mirrored in the S-shaped value function proposed by prospect theory (Kahneman & Tversky, 1979; Tversky, Slovic, & Kahneman, 1990).
The seminal work of Kahneman and Tversky (1979) was developed for simple prospects with monetary outcomes and stated probabilities, but it can be extended to more involved choices (Levy, 1992). Prospect theory distinguishes two phases in the choice process: an early phase of editing, and a subsequent phase of evaluation (Kahneman & Tversky, 1979). Kahneman and Tversky (1979) originally restricted themselves to choice problems where it is reasonable to assume “that the original formulation of the prospects leaves no room for further editing” (p. 275). In its current form, prospect theory focuses on the evaluation of prospects and not on the editing of choices (Levy, 1992, 1997). Our study differs, however, in purposely not excluding the editing phase as we offer and manipulate information on the nature of the firm, that is, we add information to the formulation of the offered prospects (regarding the investment target).
Decision Making and the Family Firm Bias
Research offers a broad range of biases when it comes to opening the black box of an individual’s psychology while making risky decisions. Some biases are directly derived from prospect theory, some not (Baker & Nofsinger, 2002). For example, scholars have shown the well-known representativeness bias—the reliance on stereotypes (Lakonishok, Shleifer, & Vishny, 1994; Shefrin, 2001; Tversky & Kahneman, 1974); the overconfidence bias—also known as the “ego trap” consisting of systemic overrating of own skills and knowledge (Belsky & Gilovich, 1999); the endowment effect—explaining individuals’ tendency to ascribe higher values to objects they already acquired (Kahneman, Knetsch, & Thaler, 1990, 1991; Thaler, 1980); the status quo bias—the individual’s preference for the current state (Samuelson & Zeckhauser, 1988); or the familiarity bias (French & Poterba, 1991), which can be described as the individual’s tendency to prefer things they are familiar with (Baker & Nofsinger, 2002). This familiarity bias offers a behavioral-based rationale as to why, for example, individuals tend to invest in companies of their home countries. They obviously are more familiar with them and thus feel closer to them (Ackert, Church, Tompkins, & Zhang, 2005) which in turn increases levels of trustworthiness, generates the experience of liking (Alter & Oppenheimer, 2008), and ultimately reduces perceived risk (Slovic, Fischhoff, & Lichtenstein, 1980).
We argue that decision making of stakeholders in a choice situation involving risk (where none of the alternatives are known ex ante) will show a similar bias (what we call the family firm bias) induced by the signaling of the family nature of the firm. We hypothesize that, due to central associations of trust and longevity surfacing in risky situations, a firm’s family nature can induce a bias toward behaving less risk-avoiding in the gain domain and more risk-seeking in the loss domain.
The gain domain
From the perspective of prospect theory, we expect that nonprofessional investors in the gain domain want to avoid any losses by behaving in a risk-averse way (Kahneman & Tversky, 1984). Accordingly, we expect that the decision process for a nonprofessional investor in the gain domain is triggered by screening the alternatives for a risk-return profile that primarily focuses on low risk (accepting low returns) in order to avoid losses in a particular period (depending on the investment horizon). Yet how might information on the nature of the investment target alter this nonprofessional investor’s risk preference? As previously mentioned, the available alternatives and related information are translated into prospects during the editing phase (Kahneman & Tversky, 1979). In the case of an investment decision, there are two types of information available: objective information regarding the past performance of the asset (e.g., price development of a stock in a given period of time), and additional information on the investment target (e.g., company data, for example the nature of the firm).
The nonprofessional investor first decodes the objective risk (based on the past stock performance) of both alternatives and identifies the alternative which is less risky due to it having less volatility. By decoding the second information type, the nature of the firm, a behavioral bias comes into play: As soon as the high-risk alternative signals a family firm background, the family firm status will shift the preferences of the risk-averse investors toward the high-risk alternative. But why do we expect that? Empirical evidence shows that one of the central perceptions regarding a firm’s family nature is that family firms are perceived as being more trustworthy than their nonfamily counterparts (e.g., Beck & Kenning, 2015; Binz et al., 2013; Carrigan & Buckley, 2008; Kashmiri & Mahajan, 2014; Orth & Green, 2009). We argue that the trustworthiness of family firms biases the investor’s decision toward family firms as trust (for a comprehensive definition of trust, see Castaldo, Premazzi, & Zerbini, 2010 3 ) increases the willingness to accept higher levels of risk (Chaudhuri & Holbrook, 2001), which are ascribed to one of the investment alternatives.
The well-established fact that family firms are associated with longevity and stability (among others: Krappe et al., 2011; Lumpkin et al., 2010; Zellweger, 2007) adds to the strength of the family firm bias in the gain domain. Accordingly, the signaling of stability will lead the nonprofessional investor to accept higher levels of objective risk because the family firm background ensures that the company is interested in longevity and long-term financial success and will not take unnecessary risks (Lumpkin et al., 2010) and thus behaves in a more risk-averse way (Miller, Le Breton-Miller, & Lester., 2011; Schulze, Lubatkin, Dino, & Buchholtz, 2001). This perfectly matches the risk-averse preferences of nonprofessional investors in the gain domain. The ascribed longevity will thus alleviate the concerns related to the greater volatility of the high-risk alternative.
Accordingly, we argue that the strong evidence of a trust advantage of family firms over their nonfamily counterparts together with the signal of longevity and long-term stability leads to the willingness of nonprofessional investors located in the gain domain to accept far higher levels of objective risk than expected. The reason for this is that the family firm signal is interpreted as an insurance against these risks (Sraer & Thesmar, 2007). In other words: In the gain domain, longevity and trust perceptions add up to a strong behavioral bias toward the riskier alternative when it signals a family firm background (all other things being equal). We therefore hypothesize that:
The Loss Domain
On the contrary and again in line with prospect theory, we expect that nonprofessional investors in the loss domain want to recoup their losses and accordingly tend to accept higher levels of risk (Kahneman & Tversky, 1984). We expect that decision process for a nonprofessional investor in the loss domain to be triggered by screening the alternatives for a risk-return profile that primarily focuses on high return (accepting high risks) in order to recoup losses in a particular period (depending on the investment horizon).
Again, as in the gain scenario, the nonprofessional investor first assesses the alternatives based on its past performance. Thereby, the nonprofessional investor first identifies the alternative, which generated higher returns in the past. This alternative shows comparably higher volatility and is obviously the high-risk alternative. We argue that the preferences will—like in the gain domain—also shift further toward the high-risk alternative as soon as the investor decodes the second type of information regarding the investment target (e.g., the firm’s family nature). Family firms invoke higher levels of trustworthiness, thus biasing the investor’s decision toward family firms because this trust signal reinforces the investor’s expectancy of the firm’s future beneficial behavior (Chaudhuri & Holbrook, 2001). This expectancy further stimulates the investor’s risk-seeking behavior.
In other words, when the high-risk alternative signals the family firm nature, investors are more prone to choose this alternative, but this behavioral bias will be less pronounced than in the gain domain. This is because we expect that only the association of trust accounts for the shift toward a higher willingness to accept the high-risk alternative. Associations of longevity and stability triggered by the family firm cue, however, will most likely shift preferences away from the high-risk alternative as associations of longevity (i.e., being a long-term oriented and stable entity) will not fit the investor’s risk-seeking preferences. Accordingly, we argue that these two central associations triggered by the firm’s family nature have mixed effects on the decision of nonprofessional investors in the loss domain: while the longevity signal reduces the willingness to accept the higher-risk option more often than expected, the trust signal strongly increases this willingness due to the strong effect of trustworthiness on risk acceptance (Chaudhuri & Holbrook, 2001). Overall, we assume that still more nonprofessional investors than expected will choose the risky alternative when the firm’s family nature is signaled in the loss domain, while this effect will be less pronounced than in the gain domain due to the mixed effect of the family firm signal in the loss domain. Formally:
Method
Investigating the influence of psychologic variables affecting the behavior of individuals under risk and uncertainty is challenging because most research designs lack causality (Hsu et al., 2017) and alternative explanations for the findings always restrict their validity. For this reason, we choose an experimental approach. An experimental research design has the power to establish causal relationships and therefore represents the most adequate method for our research question. Inter-individual differences between participants will be averaged out through randomized assignment of respondents to different experimental conditions (see Colquitt, 2008), thereby minimizing alternative explanations of the findings. This implies that “any outcome differences that are observed between those groups at the end of a study are likely to be due to treatment, not to differences between the groups that already existed at the start of the study” (Shadish, Cook, & Campbell, 2002, p. 13). Furthermore, by applying a choice setting and not a single stimuli assessment, we can clearly highlight the power of the family firm information. The reliance on hypothetical choices might raise questions regarding the validity of the method and the generalizability of the results. Nevertheless, hypothetical choices are very well established in the context of prospect theory as by default, the method of hypothetical choices emerges as the simplest procedure allowing a large number of theoretical questions to be investigated.
Sample and Procedure
The participants in our online experiment were individuals selected from Clickworker (the German equivalent of Amazon MTurk) who participated in our online experiment in exchange of a small amount of money. In total, 428 people participated in the online experiment. As 10 participants failed the manipulation checks, we omitted them from further analysis (Hsu et al., 2017). A detailed description of the manipulation checks can be found below. Our final sample consisted of 418 participants, all based in Germany, Austria, or Switzerland. Their mean age was 36.90 years, 43.5% of the participants were female, and 37.6% stated that they had experience with investments in the stock market. Furthermore, 23.4% of the participants indicated that they had a family firm background. 4
Our experiment was a 2 (gain framing vs. loss framing) × 2 (family firm as high-risk alternative vs. nonfamily firm as high-risk alternative) between-subject design in a choice setting.
The detailed procedure of the experiment was structured as follows (see also Figure 1): First, participants were randomly assigned to one of two cover stories, manipulating a gain or loss scenario and thus their individual risk attitude (individuals in loss situations are more risk-seeking and vice versa). To manipulate the gain scenario, we decided to frame a situation where the participant is beneficiary of an inheritance and has, in line with prospect theory logic, a reference point of an inheritance of €50,000€ (unexpected gain with the magnitude of one gross annual income of €50,000€) locating the individual in the gain domain:
You work for a company and you receive a gross annual income of €50,000.
Your grandfather has unexpectedly passed away and has named you in his will. You have received an
In the following, you will be presented Procedure of experiment.
The cover story of the loss scenario, however, was framed as the individual having debts of €50,000 to the bank caused by unexpected fire damage (unexpected loss with the magnitude of one gross annual income of €50,000), locating the individual in the loss domain
5
:
You work for a company and you receive a gross annual income of €50,000.
As a result of an unexpected accident last month, your house was damaged by fire. The fire damage was estimated at
In the following, you will be presented
Consequently, two investment alternatives were presented to all participants by providing them with two 3-year stock performance charts of the two companies 6 . Both presented stocks have the same purchasing price of €50.00, to keep the affordable stock volume constant. In order to guarantee a high-risk alternative with high returns and a low-risk alternative with low returns we conducted a pretest (N = 20) ex ante with a student sample (Mage = 24.9 years, 55% female) showing similar characteristics as our final sample in terms of experience with investments as well as family firm background. We created five different charts showing different 3-year stock performances regarding volatility and returns. We asked the participants of the pretest to assess the risk-return profile of the presented stocks to identify one low-risk stock performance chart with low returns and one high-risk stock performance chart with high returns for the main experiment.
After having been exposed to the cover story, the participants were shown two investment alternatives, one of them previously categorized as being a high-risk alternative, the other as being a low-risk alternative. We manipulated the investment alternatives according to the brief company description provided beneath the performance charts (see Figure 2 for the detailed manipulations). The between-subject treatment was that in Scenario 1: the high-risk alternative was labeled as “Specht AG,” a German family firm, and the low-risk alternative as “ORVATO AG,” a German nonfamily firm, with all shares in free float, or vice versa (in Scenario 2 the risky alternative as nonfamily firm and the low-risk alternative as family firm). The overall experimental logic of the between-subject treatment is visually presented in Figure 2.
Overview of between-subject treatment. Choice architecture (gain & loss domain).
Measures
Immediately after providing the stimuli, we captured the investment preference of the participants by first asking them how likely it was that they would invest their money in option A/option B on a 7-point Likert scale (1 = very unlikely, 7 = very likely). Afterwards, the participants made a final decision on the company in which they wanted to invest their money. After having been exposed to the investment alternatives and to the investment decision, participants in the study completed a short questionnaire. We captured perceived longevity of the companies by two items on a 7-point Likert scale ranging from (1) strongly disagree to (7) strongly agree (Moulard, Raggio, & Folse, 2016): “company A/company B has been around for a while” and “company A/company B has a long history” (Cronbach’s alpha [Cα] = .93). Further, we also measured trust in the respective companies by using three items (Moulard et al., 2016): “I trust company A/company B” “I could rely on company A/company B” “company A/company B is an honest company” (1/7, Cα = .92). Since we assume that the general risk propensity of individuals affects their investment behavior, we included the risk averseness scale (Burton, Lichtenstein, Netemeyer, & Garretson, 1998) as a control variable, which was measured based on a four-item scale at the beginning of the questionnaire: “I don’t like to take risks,” “Compared to most people I know, I like to live on the edge,” “I have no desire to take unnecessary chances on things,” “Compared to most people I know, I like to gamble on things” (1/7, Cα = .74). Because trust, longevity, and risk averseness scales include a variety of diverse items, we ran a confirmatory factor analysis (CFA) to identify the psychometric properties of the respective scales. Importantly, we found model fit indices (comparative fit index = .96; normed fit index = .95; adjusted goodness of fit index = .90), which indicated a good model fit (Baumgartner & Homburg, 1996; Hu & Bentler, 1999). Additionally, we assessed composite reliability measures and found that the trust (CR = .89) and longevity scales (CR = .87) were well above the threshold of 0.7. Only risk averseness scored slightly lower (CR = .56), but we still evaluated it as adequate due to its acceptable Cronbach’s alpha value (Cα = .74).
Manipulation Checks
As an important standard procedure in any experimental research (Koschate-Fischer & Schandelmeier, 2014), we also included several manipulation checks to guarantee that our presented stimuli were processed as intended (e.g., Hsu et al., 2017; Warren & Campbell, 2014). This is important, as only if the manipulation is correctly decoded by the respondents, an effectiveness of the variation of the independent variable can be tested (Koschate-Fischer & Schandelmeier, 2014). Our first manipulation check was a risk assessment of the investment decision. Directly after exposing the participants to their investment decision, we asked them: “How risky do you think your investment decision was?” (1 = not risky at all, 7 = very risky). Analyses showed that participants who invested their money in the high-risk alternative rated their investment decision as significantly riskier (M = 4.31, SD = 1.22) than those participants who invested in the low-risk alternative (M = 3.51, SD = 1.25, t = 5.87, p < .001). This result indicated that the stock performance manipulation we provided was processed as intended. Further examining the robustness of our experimental design, we asked the participants to select which type of firm they were exposed to within their choice scenario. We provided a multiple selection option including “family firm,” “corporation,” “charitable foundation,” and “registered association.” The participants who selected “charitable foundation” or “registered association” were removed (N = 10) because they apparently were not fully engaged in the experimental scenario (“engagement check”; see Hsu et al., 2017) or did not understand the experimental setting as intended.
Findings
We present the findings of our experiment according to the gain and loss domain respectively. First, we analyze the gain domain and explore the distribution of the stock allocation of participants who have been exposed to the gain domain. Second, we test whether the distribution pattern of the loss domain differs from the gain domain, thereby testing our Hypotheses 1 and 2 subsequently. To further deepen the understanding of our main experiment’s results, we included qualitative data exploring why individuals choose which alternative.
Decision Making With Ex Ante Lower Levels of Acceptance of Risk: The Gain Domain
We find strong support for our first hypothesis (H1) as the data clearly demonstrate that the family firm information increases the likelihood of the high-risk alternative being chosen. More precisely, when the high-risk alternative is framed as a nonfamily firm and the low-risk alternative as a family firm (Scenario 2), 12.0% of the participants invest their money in the high-risk alternative (88.0% in the low-risk alternative). However, by switching the family firm information toward the high-risk alternative (Scenario 1), the willingness to take risks changes: 35.1% of the participants invest their money in the high-risk alternative, constituting a difference of 23.1% for preference of the high-risk alternative. The preference for the risky alternative almost tripled due to the family nature of the firm. Accordingly, chi-square statistics revealed a highly significant relationship between the family firm information and the tendency of individuals to choose the high-risk alternative or not, χ2 (1) = 61.36, p < .001. Based on the odds ratio (
We also captured the choice preferences by using an additional continuous variable measuring the likelihood to invest in the high-risk alternative. Again, the findings confirm the biasing effect of the signaling of a firm’s family firm nature. Findings reveal a significant difference between the likelihood to invest in the high-risk alternative when the company signals a family firm background (M = 4.07, SD = 1.67) and their likelihood to invest in the higher-risk alternative when it signals a nonfamily firm background (M = 3.23, SD = 1.72, t = 3.58, p < .001; see Figure 3).
Control variables
Additionally, we further controlled whether personal risk propensity, gender, prior investing experience, and family firm background may affect the findings. We used effect coding 7 to run a factorial ANOVA with investment manipulation (family firm as high-risk alternative vs. nonfamily firm as high-risk alternative), risk propensity (risk-averse vs. risk-seeking), gender (female vs. male), family firm background (background vs. no background), and prior investing experience (experience vs. no experience) as fixed factors, and likelihood to invest in the high-risk alternative as dependent variable.
While the results show no significant interaction effects of the investment manipulation with gender, prior investing experience or family firm background (p values > .05), a significant interaction effect of investment manipulation with risk propensity on the likelihood to invest in the high-risk alternative, F(2, 202) = 6.41, p < .01 can be observed. This indicates that risk-averse and risk-seeking individuals react differently to the family firm signal. More precisely, in the case of a risk-seeking person, the likelihood to invest in the high-risk alternative does not change significantly depending on whether the risky investing target is a family firm (M = 4.03; SD = 1.63) or a nonfamily firm (M = 3.87; SD = 1.63, p > .05). On the other hand, risk-averse individuals significantly change their preference for investing in the high-risk alternative depending on whether a family firm (M = 4.12; SD = 1.75) or a nonfamily firm nature (M = 2.68; SD = 1.61, p < .001) is signaled.
Decision Making With Ex Ante Higher Levels of Acceptance of Risk: The Loss Domain
Contrary to the gain domain, when the high-risk alternative is framed as nonfamily firm and the low-risk alternative as family firm, 26.2% invest their money in the high-risk alternative (compared to 12.0% in the gain domain), which is in line with the loss aversion argument of prospect theory. However, and analogously to the gain scenario, by switching the family firm signal toward the high-risk alternative the preferences for the high-risk alternative change to 38.5% (+12.3%). Accordingly, chi-square statistics reveal a significant relationship between the family firm information and the tendency of nonprofessional investors to choose the high-risk alternative or not, χ2 (1) = 26.21, p < .001. Based on the odds ratio, the odds of their investing in a high-risk alternative is 1.76 times higher if the company signals its nature as a family firm, showing the existence of a family firm bias in the loss domain. However, compared to the gain domain, the effect is less pronounced, lending support to Hypothesis 2.
In line with our previous analysis within the gain domain, we additionally captured the investment preference by measuring the likelihood to invest in the high-risk alternative. Similarly to our previous findings concerning the gain domain, we revealed a significantly higher difference between the likelihood to invest in the higher-risk alternative over the lower-risk option when the high-risk company signals being a family firm (M = 3.98, SD = 1.85) compared to investing in the high-risk alternative when it signals a nonfamily firm (M = 3.41, SD = 1.83, t = 2.21, p < .05).
Control Variables. Again, and in line with the analysis of the gain domain, we tested for confounding effects. Results of the ANOVAs demonstrated no significant interaction effects of risk manipulation (family firm as high-risk vs. nonfamily firm as high risk) with personal risk propensity, gender, prior investing experience, and family firm background on the likelihood of individuals investing in the high-risk alternative (all p values > .05).
In both scenarios (gain and loss domain), we asked the participants to make an ex post risk assessment of their decision by asking them “How risky do you think your investment decision was?” Findings show that participants who invested in the high-risk alternative assessed the risk of the nonfamily firm risk scenario (M = 4.27, SD = 1.27) as being as risky as the family firm risk scenario (M = 4.15, SD = 1.18, p > .05). This implies that the decision for the high-risk alternative is perceived as a high-risk decision, regardless of whether the investment target was framed as a family firm or not. The family firm signal therefore does not influence risk awareness in general, but the individual willingness to accept risk. But why does this happen? How do individuals cognitively edit the different prospects in the choice situation? What are associations related to the underlying cognitive process?
Editing of Prospects and the Family Firm Bias: The Role of Longevity and Trust
We used two independent methods to gain insights into the cognitive process underlying the “family firm bias” discovered in our main experiment. First, we gathered further quantitative insights from our main experiment. Second, we performed a qualitative exploration to deepen our understanding of the editing of the choices and to discover initial insights in the cognitive process in relation to the choice taken. 8
Insights from the quantitative study
Statistics of Findings.
*significance level below .05 **significance level below .01 ***significance level below .001
Insights from the qualitative exploration
The objective of our qualitative study was to better understand why individuals show a bias toward family firms, how they decode the family firm information during the editing phase of prospect theory, and to substantiate which associations play a central role in that process. The participants (n = 23) were selected according to the major sample characteristics of our main experiment (see “Method” section for details). All participants were exposed to the cover story of the gain domain (i.e., inheritance of €50,000) before being presented with two investment alternatives (with the family firm signal being linked to the high-risk alternative). We asked the participants to document their mental associations with the presented alternatives (i.e., a written think-aloud protocol; Someren, Barnard, & Sandberg, 1994). By analyzing the qualitative data, we identified the association patterns related to the choice of the investment alternatives. Our data support our theoretical explanation that most nonprofessional investors (78.3%) first decode the presented chart showing the past stock performance (assessing the objective level of risk). Afterwards participants decode and interpret additional information regarding the company type. For participants who selected the high-risk alternative framed as a family firm, the qualitative data strongly supports our quantitative findings, since longevity as well as trust can be identified as major associations underlying the participants’ decisions. Based on research indicating that earlier/faster retrieval of mental associations is more relevant for decisions than later retrieval (Galdi, Arcuri, & Gawronski, 2008), we clustered the identified associations with family firms chronologically into primary and secondary associations. Interestingly, and contrary to our expectations, we identified longevity as a primary association and trust as a secondary association. For example, Interviewee 5 states: “The family manager is more interested in long-term success than nonfamily managers who may have other interests.” Similar statements were made by many other participants such as, for instance, Interviewee 8: “The family has sufficient entrepreneurial experience und will therefore move in the right direction in order to preserve the company for a long time.” Regarding trust, we find a more nuanced picture incorporating different facets of trust, 9 such as the expectation of the firm’s behavior in the future, as Interviewee 1 argues: “The great-great-grandson will supposedly avoid making overly risky maneuvers and will try to prevent failure using all means available” or confidence in the commitment of the owning manager, as Interviewee 7 clearly formulates: “The fact that Specht AG is a 5th generation family business inspires confidence in the company’s management.” Analysis of our qualitative data generally provided strong support for longevity and trust as being the main cognitive processes behind the family firm signal in decision making under risk. 10
Discussion
Our objective was to investigate the effect of the signaling of the family nature of the firm on risky decisions. Grounded in prospect theory and using a choice-based experimental design, we were able to empirically demonstrate the existence of a family-firm bias of individuals in the context of investment decisions of nonprofessional investors.
Our results clearly show that the family nature of the firm leads to a preference shift of individuals toward more risky choices, indicating a moderation effect in the risk aversion under gains and losses—in both cases toward more risk seeking. In more detail, the family nature of a firm mitigates the risk aversion in the gain domain and reinforces the risk seeking in the loss domain (but to a lesser extent). Thus, our experimental study confirms our theoretical reasoning that the communication of a firm’s family nature has a preference-changing characteristic, as it affects the individual’s willingness to accept risk in an uncertain environment.
Our findings extend existing knowledge of the perception of a firm’s family nature by examining how nonprofessional investors react to the family firm signal. The family firm bias suggests that potential investors actually have a much more positive view of family firms than previously thought (e.g., based on the agency literature on principal-principal conflicts, the expropriation of minority shareholders, or catering to family and friends at the possible expense of the business; see for example Bertrand & Schoar, 2006; Miller, Le Breton-Miller, & Lester, 2013; Schulze et al., 2001; Schulze, Lubatkin, & Dino, 2003). Apparently, nonprofessional investors do not see these as major issues.
Our study thereby adds to the growing literature that explores the consequences of signaling the family firm identity (Zellweger et al., 2010, 2012). It further draws attention on marketing and branding research in the family firm context. By forcing the participants of our experimental study to choose between two alternatives instead of using a single stimuli assessment, we were able to clearly carve out the effect of the family firm information in a decision process. Our results extend previous findings regarding positive and negative perceptions of family firms by evidently demonstrating the power of signaling the family nature of a firm in terms of changing preferences, referred to here as the family firm bias. Our study provides initial evidence that the family firm signal can apparently serve as a mental shortcut to overcome uncertainty and drive individuals to accept more risk. In such a risky choice situation (family vs. nonfamily firm), two perceptions tied to a firm’s family nature seem to be particularly important: perceptions of trust and longevity, the latter being the predominant association with family firms in investment decisions. This holds important implications for the theory of the family enterprise as these two major associations seem to be decisive in situations with uncertain outcomes.
Our work also contributes to prospect theory in two major ways. First, family business scholars have started to use prospect theory (see Kotlar et al., 2017) and the derived behavioral agency model (Chrisman & Patel, 2012; Leitterstorf & Rau, 2014) to explain the behavior of family firms by applying derivatives of prospect theory such as dynamic reference points to explain IPO underpricing (Kotlar et al., 2017) or myopic loss aversion to explain variations in R&D investments (Chrisman & Patel, 2012). Instead of explaining the behavior of family firms, our study uses prospect theory to explain the behavior of external stakeholders (nonprofessional investors) in relation to family firms, thereby unveiling a behavioral bias, which extends the already revealed biases derived from prospect theory. As our study shifts the scope of application of prospect theory from a company-internal to a company-external perspective, it lays the foundation for future research to combine both perspectives and examine the idiosyncratic behavior of family firms and related stakeholders simultaneously to create a holistic understanding of the family firm and its environment.
Second, our findings contribute to the basic framework of prospect theory (Kahneman & Tversky, 1979) by focusing on the editing phase of decision making, an under-researched area in comparison to the strong body of empirical evidence existent on the evaluation phase of decision making in prospect theory (Levy, 1996, 1997). Drawing on our findings, we discovered how the editing of the information on the object (in our case, the perception of the nature of the investment target) can indeed bias the choice. As can be derived from our exploration of the underlying cognitive process, it seems that a certain order of editing takes place: First the risk level of the options is assessed, and then the additional information on the object is edited. By adding more information to the object (such as a firm’s family nature), the subjects’ editing can be influenced. Theoretical work on prospect theory’s editing phase emphasizes the framing of prospects as a decisive factor (Tversky & Kahneman, 1986), that is, whether prospects are presented as losses or gains changes the preferences of individuals. We found, however, that preferences can be moderated by the framing of the context of the alternatives. This offers new potentials for applying prospect theory in different situations. Since the organizational type (family firm) biases the editing of prospects, other informational cues might also be influential encouraging further research on the editing of choices.
Limitations and Future Research
The use of experiments offers strong advantages as experimental groups averages out any individual differences (see Colquitt [2008] for a complete review), thereby creating “two or more groups of units that are probabilistically similar to each other on the average” (Shadish et al., 2002, p. 13). Thus, this research design empirically controls the influence of alternative factors by randomizing these variables (Aronson, Ellsworth, Carlsmith, & Gonzales, 1990). As our experimental setting benefits from strong internal validity, future research could test our findings with a stronger focus on external validity, for example, carrying out a field experiment with real money and real choices (if ethical concerns can be resolved properly). Nevertheless, as experimental research in the context of prospect theory shows, field experiments tend to confirm lab findings (Barberis, 2013).
One important direction for further research is to better understand why the family firm bias exists in different decision contexts. While we assume that the general bias will be context-independent, the reasons for the bias might differ across contexts. For example, the perception of trust might be more important in one context, the perception of longevity in another. Additionally, the reasons might vary across different stakeholder groups. An employee, for instance, might prefer family firms due to longevity-evoked perceptions of stability, while a novelty-seeking customer might do exactly the opposite for the same reason. In this study, we discover initial evidence for two explanations of the family firm bias: perceptions of longevity and trust induced by the family firm signal. Future experimental research could manipulate these two potential process variables, for example using the cover story to emphasize the longevity of the company or its trustworthiness (either as a whole construct or by signaling individual dimensions such as benevolence, competence, and integrity; Mayer et al., 1995) in order to empirically test and isolate the effect of these suggested variables in a controlled environment. Furthermore, future research should explore additional rationales of the family firm bias. For instance, family firms are known for having more conservative financial policies, being better financial stewards of their organizations than nonfamily firms (Neckebrouck, Schulze, & Zellweger, 2018). While we did not find evidence of it in our data, financially conservative attributions are potentially impactful in the context of financial decisions under risk. This is an area which calls for further research. Moreover, other associations such as authenticity (Lude & Prügl, 2018) or perceptions of “doing good” in terms of corporate social responsibility (Schellong, Kraiczy, Malär, & Hack, forthcoming) may also drive the explored family firm bias, offering ample scope for future research.
Beyond further investigating the perceptual effects of family firms on the hitherto overlooked stakeholder group of investors, further research should particularly explore the family firm bias in that area in greater detail in order to fully address the heterogeneity of family firms (Chrisman, Chua, Le Breton-Miller, Miller, & Steier, 2018; De Massis, Wang, & Chua, 2018). Interesting paths to follow in order to advance our understanding would be: (a) the context of professional investors (in comparison to the nonprofessional investors in this study), (b) the role of prior knowledge of the investment target (in comparison to the ex-ante unknown target in this study), (c) varying investment horizons (in our study, the horizon was held constantly at a minimum of 2 years), (d) varying levels of uncertainty and risk, and (e) different types and characteristics of family firms, for example, regarding generational stages and transition phases, family and ownership dispersion, company size, internationalization, or the role of nonfamily employees, to name just a few.
Practical Implications
Our findings hold important implications for owners and managers of family firms and for individuals facing a decision situation under risk and uncertainty. Owners and managers of family firms seem to be in a better position—all other things being equal—if they choose to prominently inform stakeholders about their firm’s family nature. The family firm signal seems to be able to attract the attention of more stakeholders and thus potentially leads to value gains for the family firm. Although we did not directly examine the family firm bias for different stakeholder groups, we assume that the family firm effect is effective in very different situations and may also apply to very different stakeholders. Given that all types of stakeholders regularly face decision situations under risk and uncertainty (e.g., customer buying situations), the family firm cue can influence preferences, even when the family firm alternative appears to be the higher-risk choice (e.g., higher prices and less knowledge concerning the quality of the offer). In general, individuals seem to be willing to accept higher levels of risk when informed about the firm’s family nature. Accordingly, awareness of this psychological bias could be helpful in guiding future decisions on the use of family-based branding strategies, especially in situations where a stakeholder’s greater willingness to accept risks is to be achieved, for example when new products are being introduced.
Footnotes
Acknowledgments
This article is the result of an exciting journey, which started somewhere in the mountains of Austria. We are grateful for the challenges and suggestions provided by two anonymous reviewers and for the thoughtful comments and remarkable guidance of our action editor, Thomas Zellweger. We would like to thank the organizers and participants of TOFE 2017 for a great time in Sankt Gallen with inspiring discussions and eye-opening comments. We are grateful for insightful feedback on earlier versions of this manuscript provided by Josip Kotlar, Jess Chua, and Hanqing “Chevy” Fang. We would also like to thank Lisa Oslebo for her ideas and support in the early stages of that study. Last but not least, this journey would have been impossible without the support of the entire team at FIF@Zeppelin University.
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding
The author(s) received no financial support for the research, authorship, and/or publication of this article.
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