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Article

Opportunities, Threats, and Strategic Choice: The Modifying Role of Emotion

by
Camilla Aarøen
and
Marcus Selart
*
Department of Strategy and Management, Norwegian School of Economics, Helleveien 30, 5045 Bergen, Norway
*
Author to whom correspondence should be addressed.
Adm. Sci. 2025, 15(9), 331; https://doi.org/10.3390/admsci15090331
Submission received: 30 June 2025 / Revised: 11 August 2025 / Accepted: 19 August 2025 / Published: 25 August 2025
(This article belongs to the Special Issue Navigating the Challenges: Entrepreneurship and Leadership in the Modern Global Business Landscape)
Versions Notes

Abstract

Business models often transform due to adaptation in response to external changes. However, relatively little is known about what causes these types of adaptations. We suggest that threat-rigidity as well as prospect theory have the potential to explain what causes business model adaptation in response to gains and losses. Firm leaders’ inclination to adapt their business model is sensitive to risk that is perceived as a gain or a loss in the macro-economic environment. We apply threat-rigidity and prospect theories to examine the relationship between risk perception and business model adaptation. We also investigate if emotion has explanatory value for how managers adapt to business models. We test our hypotheses in a field experiment involving 95 Scandinavian managers. Here, we relate managers’ inclinations to adapt to different business models under different risk scenarios. The results reveal that, in general, managers are more risk seeking in gain scenarios than in loss scenarios. This finding is in line with the threat-rigidity theory. In addition, emotional style is found to relate more to risk aversion than to risk seeking in the domain of potential gain. We argue that emotional style has explanatory value for how managers adapt to business models, because emotions are key influencers on risk perception.

1. Introduction

A business model constitutes an important tool for avoiding organizational and market pitfalls because it highlights the need to consider a range of strategic decisions. This is because the business model in many ways reflects the strategic choices made. In addition, the business model prompts the upper echelons of an organization to consider the logic and internal consistency of the strategic decisions collectively (Shafer et al., 2005). Understanding what leads firms to take risks and what drives them to adapt their business model is thus an intriguing and meaningful discussion related to business model adaptation.
Risk taking in firms and the adaptation of business models to these has been a topic of significant research interest for decades (Trimpop, 1994), and researchers have studied the subject from a variety of angles. Saebi et al. (2017) show that firms are more likely to adapt their business models in environments of perceived threat than in environments of perceived opportunity. Adapting the business model is a risky venture as there is no way of knowing whether one will succeed. Central themes in the field of strategy pertain to sustaining competitive advantage and creating value in firms and industries (Selart et al., 2006; Bashir & Verma, 2019). The business model has become an essential tool in achieving these factors. A firm’s business model is its strategy of how to create, deliver, and capture value. It is also a description of how processes and infrastructures in the firm are related. Research on business models has flourished in recent years (Linder & Cantrell, 2000; Magretta, 2002; Morris et al., 2005; Mason & Leek, 2008). The business model concept appeals to both researchers and practitioners. The most significant development is the recognition that adapting, shaping, and renewing the business model is paramount for firms to create value on a continuous basis (Shafer et al., 2005; George & Bock, 2011; Casadesus-Masanell & Ricart, 2010; DaSilva & Trkman, 2014; Aspara et al., 2010). Firms that have been successful for some time risk failing if they do not alter the business model to adapt to external changes (Achtenhagen et al., 2013; Bashir & Verma, 2019; Demil & Lecocq, 2010; McGrath, 2010; Teece, 2010). A large group of studies therefore report the effects of changes in the business model. An umbrella term for changes in the business model is business model adaptation, defined as the process by which firms actively align their business model to a changing environment (Foss & Saebi, 2015, 2018; Saebi et al., 2017). However, business models are often challenging to alter. Characteristics within firms can make the business model rigid and inert (Achtenhagen et al., 2013; Andries et al., 2013; Bashir & Verma, 2019; Doz & Kosonen, 2010; McGrath, 2010; Sosna et al., 2010; Stieglitz et al., 2016).
We provide two contributions in this study. First, reviewing the relevant literature on business model adaptation, we investigate the predictive capability of prospect and threat-rigidity theories (Mazzei et al., 2025). Because the theoretical foundation for understanding business model adaptation is relatively weak, we consider the current study a pioneering effort (Foss & Saebi, 2015; Saebi et al., 2017). Second, we examine the extent to which emotionality as a trait has the capacity to influence the relationship between risk perception and business model adaptation—specifically, how emotionality relates to risk aversion and risk seeking in scenarios of potential gains and losses (Weller & Tikir, 2011; Weller & Thulin, 2012).

2. Theoretical Background

2.1. Business Models and Business Model Adaptation

The rapid growth in the number of articles written about the business model concept demonstrates its importance as a relatively new unit of analysis, distinct from the product, firm, industry, or network (Zott et al., 2011; Spieth et al., 2016). As business models have gained popularity as a topic for research, the focus in the literature has shifted from examining the static business model to exploring how business models change, evolve, and innovate over time (Foss & Saebi, 2015, 2018; Saebi et al., 2017). Changes in the business model can occur as business model learning, innovation, renewal, replication, erosion, life cycle, transformation, creation, and transformation (Bashir & Verma, 2019; S. Cavalcante et al., 2011; Teece, 2010, 2018; Casadesus-Masanell & Zhu, 2013; S. A. Cavalcante, 2014; Massa et al., 2017). Saebi et al. (2017) classify all these dynamics as business model adaptation, defined as “the process by which management actively aligns the firm’s business model to a changing environment, for example, changes in the preferences of customers, supplier bargaining power, technological changes, competition, etc.” (De Reuver et al., 2009; Wirtz et al., 2010, 2015; Sosna et al., 2010; Dunford et al., 2010; Bohnsack et al., 2014; Santos et al., 2015). Drivers of business model adaptation are strictly external and include external stakeholders, changes in the competitive environment, and new opportunities brought about by new information and communication technologies (Saebi et al., 2017; Foss & Saebi, 2018). These drivers influence business model adaptation (Voelpel et al., 2004; Pateli & Giaglis, 2005; Ferreira et al., 2013; Miller et al., 2014; To et al., 2019; Sarta et al., 2020).
Other research shows that rigid business models are related to firms’ willingness to experiment (Andries et al., 2013; Sosna et al., 2010; McGrath, 2010; Cozzolino et al., 2018; Liu & Yu, 2021) and firms’ ability to develop organizational and leadership capabilities (Achtenhagen et al., 2013; Bashir & Verma, 2019; Doz & Kosonen, 2010). Furthermore, path dependencies, which contribute to stability and operational efficiency, can cause business models to become inert over time (Saebi et al., 2017; Foss & Saebi, 2018). Adapting the business model can be a high-risk strategy (Pateli & Giaglis, 2005). When the outcome is uncertain and business models may become inert, leaders and firms need strong incentives to adapt their business model.

2.2. Risk Perception as a Driver of Business Model Adaptation

Adapting the business model is often a risky venture, and the likelihood of succeeding in doing so is low (Pateli & Giaglis, 2005). It is therefore not surprising that business models often do not change once put in place. Findings from several contributions suggest that this inertia can be blamed on firms’ unwillingness to experiment (Andries et al., 2013), firms’ lack of ability to develop leadership and organizational skills (Doz & Kosonen, 2010; Achtenhagen et al., 2013; Bashir & Verma, 2019), and path dependencies in firms (Foss & Saebi, 2015; Saebi et al., 2017; Foss & Saebi, 2018). In the face of this low likelihood of success and firm characteristics that prevent adaptation, what factors can prompt firms to adapt their business models?
Prospect theory predicts that firms and individuals will take more risks when faced with potential loss than when faced with perceived opportunity (Kahneman & Tversky, 1979; Tversky & Kahneman, 1992). Changing an existing business model tends to be both uncertain and costly with respect to the outcome. Business model adaptation thus constitutes a form of risky behaviour especially in times of crisis. Prospect theory suggests that firms facing difficult situations are more likely to respond with business model adaptation. Firms facing better conditions are more risk-averse since they have more to lose than to gain and are thus more likely to prefer the status quo (e.g., Saebi et al., 2017).
The theory addresses the relationship between risk attitude and a firm’s current position (Bromiley et al., 2001; Tsai & Luan, 2016), positing that all evaluations are relative to where the firm finds itself at any moment (Jegers, 1991; Shimizu, 2007; Bromiley, 2010; Kahneman, 2012; Barberis, 2013). Thus, when making decisions under uncertainty, members of higher echelons tend to prioritize avoiding losses over acquiring equivalent gains. However, their risk preferences shift based on the framing of the situation. They are risk-averse when faced with gains but risk-seeking when facing losses. The observation is that higher echelon members asymmetrically feel losses greater than that of an equivalent gain. Prospect theory centralizes around the idea that these members estimate their utility from gains and losses relative to a certain neutral reference point regarding their current individual situation.
Many researchers view the theory as the most accurate description of how people evaluate risk, though some research indicates that there are relatively few well-known applications of it (Barberis, 2013). However, contributors have found some support for prospect theory arguments across different disciplines and cultures, as well as on both individual and organizational levels (Fiegenbaum, 1990; Kuvaas & Selart, 2004; Dham & al-Nowaihi, 2007; Kairies-Schwarz et al., 2017; Krčál et al., 2016; Palmer et al., 1995; Nordmo & Selart, 2015; Tsai & Luan, 2016; Saebi et al., 2017; Foss & Saebi, 2018).
H1a. 
Managers are more likely to engage in business model adaptation in environments of perceived threat or potential loss than in environments of perceived opportunity or potential gain.
Habits inclined to avoid change and uncertainty play pivotal roles in peoples’ everyday lives. Hence, successful behaviors that produce satisfactory results easily become the default (Rosman et al., 1994). The liability to engage in learned and habitual behavior is not seldom strengthened in times of distress when people actively try to understand and critically analyze occurring events (Weick, 1988, 1990). In order to explain this tendency, Staw et al. (1981) developed the threat-rigidity theory. This theory suggests decreased attention and communication followed by tighter control of decision-making as a result of threatening situations. According to the theory, there is a tendency to refuse adaptation to evolving conditions, thereby supporting the status quo (Samuelson & Zeckhauser, 1988; Fernandez & Rodrik, 1991; Kahneman et al., 1991; Ritov & Baron, 1992; Masatlioglu & Ok, 2005).
Threat responses often result in people simplify information processing, leading to a narrowing of decision-making procedures (Ashkanasy et al., 2003). The result of this is that cognitive flexibility deteriorates as individuals engage in well-learned courses of action (Hodgkinson & Wright, 2002). Under duress, decision-makers frequently rely on limited comparison rather than on rational methods in ways that might prove to be counterproductive (Plotnick & Turoff, 2014; Le Mens et al., 2015; Shimizu & Hitt, 2005).
Nevertheless, a rigid response is not generally to be regarded as a lack of action, but rather as defaulting to prior methods and practices. Threat-rigidity is not directly the product of poor strategic thinking, a lack of screening, resistance to change, or paralysis by analysis. Instead, it constitutes a specific behavioral response when under pressure to engage in familiar courses of action (Mazzei et al., 2025).
Threat-rigidity theory suggests that people will exhibit rigidity, or an inability to act, when faced with economic adversity. Individuals, groups, and organizations are believed to revert to familiar responses when faced with threats, even in situations when this might be considered questionable (Connolly & Shi, 2022). According to the theory, people confronted with poor economic performance or threats in their environments will tend to act conservatively, looking inward and reacting by relying on existing routines (Shimizu, 2007; Mazzei et al., 2025). In their threat-rigidity theory article, Staw et al. (1981) hypothesized that threats might lead organizations to, among other things, rely on prior knowledge, centralize authority, and increase efficiency, which results in constricted control, conservation of resources, and a restriction on information processing. Firms thus give priority to prior knowledge over new information and thereby reduce and restrict communication (Hodgkinson & Wright, 2002).
When faced with perceived opportunity, firms are expected to have the ability and motivation to take more risks. Perceptions of opportunity are associated with higher levels of control, which should motivate firms to “initiate actions that might otherwise be perceived as too risky” (Chattopadhyay & Huber, 2001; Kreiser et al., 2020; Jeong et al., 2023). Researchers have found support for the validity of the threat-rigidity theory in the context of loss (Chattopadhyay & Huber, 2001; Meschi & Métais, 2015; McManus & Sharfman, 2017; Shi et al., 2018), but less proof exists of its legitimacy in the context of gain. Research finds that firms are more likely to pay higher premiums if acquisitions are framed as opportunities (McManus & Sharfman, 2017), but other than this, little proof exists.
H1b. 
Managers are more likely to engage in business model adaptation in environments of perceived opportunity or potential gain than in environments of perceived threat or potential loss.

2.3. Emotionality as a Modifier of the Relationship Between Risk Perception and Business Model Adaptation

Several studies reveal that emotion plays an important role in social and economic decision-making (e.g., Elster, 1998a, 1998b; Loewenstein, 1996, 2000; Naqvi et al., 2006; Slovic et al., 2007; Heilman et al., 2010). People evaluate objective features of a risky situation in a subjective way and emotions are to, a high extent, part of this process (Bechara et al., 1997; Chandler et al., 2009; Coricelli et al., 2007; Damasio, 2005; Holt & Laury, 2002; Lerner & Keltner, 2000, 2001). As a result, people’s cognitive evaluation of risks are not always in alignment with their emotional reactions to those risks. People fear recognizable things at the cognitive level but do not fear things that are objectively threatening. To put it differently, the determinants of fear are most often different from the determinants of cognitive evaluations of riskiness (Loewenstein, 2000).
Emotions significantly influence risk-taking behavior, impacting both the willingness to take risks and the specific decisions made under uncertainty (Loewenstein et al., 2001; Nabi, 2003; Ditto et al., 2006; van Dijk & Zeelenberg, 2006; Lerner & Tiedens, 2006). Factors like specific appraisals of situations, feelings as information, mood maintanance, context, and individual differences all play an influencing part in the outcomes.
High emotionality is characterized by a tendency to worry about minor matters, feeling empathetic towards others, and a propensity to share concerns (Lee & Ashton, 2013). Individual differences associated with sentimentalities, such as experiences of anxiety, sentimentality, and empathy versus fearlessness, detachment, and independence, are assigned to the emotionality trait (Weller & Tikir, 2011; De Vries et al., 2009).
Emotionality influences risk-taking behavior and is associated with higher risk perceptions (Weller & Tikir, 2011). It refers to the degree to which someone experiences and expresses emotions. It encompasses both the internal feelings and the observable behaviors associated with emotions. It is a trait that can vary significantly between individuals, influencing how they react to various situations and stimuli. The emotionality trait has some similarities to the neuroticism trait, in which people who score high (i.e., highly neurotic people) show a tendency to be anxious, compulsive, defensive, and thin-skinned (McCrae & Costa, 1987). The trait also relates to bad self-esteem and low self-efficacy (Judge et al., 2002, 2013).
Several studies have found relationships among emotionality, risk taking, and risk perception, which implies that emotional individuals are less inclined to take risks. For example, emotionally stable individuals perceive risks as lower than emotionally unstable individuals (Fyhri & Backer-Grøndahl, 2012). Emotionality is associated with a lower propensity to take risks in both the potential loss and the potential gain domains (Weller & Thulin, 2012).
In general, emotionality appears to be connected with risk aversion and a higher-than-average risk perception. We assume that firms with risk-averse managers will be hesitant to implement changes in the domain of potential loss. However, higher perceptions of risk may make firms including managers with a high emotionality score less inclined to adapt business models when faced with opportunities as well. In summary, we predict that managers with a high emotionality score will act rigidly in both potential gain and potential loss domains. This prediction correlates with prospect theory in domains of potential loss and threat-rigidity theory in domains of potential gain. In line with these assumptions and arguments, we propose the following hypotheses:
H2a. 
The higher the emotionality score, the less likely the leader is to propose business model adaptation in the domain of potential gain.
H2b. 
The higher the emotionality score, the less likely the leader is to propose business model adaptation in the domain of potential loss.

3. Methods

3.1. Sample and Data Collection

We distributed the survey to 385 randomly selected Scandinavian middle managers, top managers, and chief executive officers in various sectors. Of these, 134 participated, and after removing outliers and unfinished responses, 95 useful responses remained (26% women and 74% men). A significant percentage (46%) were aged 45–54 years; most others were in the age groups 35–44 years (21%) and 55–64 years (23%). Almost all the responses (94%) were from individuals in the private sector. The industries addressed included wholesale/retail trade, manufacture, maintenance, and construction. We did not collect information about nationalities, but all the participants spoke Norwegian and worked in Norwegian firms, so it is realistic to assume that most of the participants had Norwegian origins. The experiment consisted of two parts, the personality test and the field experiment, counterbalanced such that half the participants started with the personality test and the other half started with the field experiment.

3.2. The Personality Test

We administered the 60-item Hexaco personality test online to the participants (Ashton & Lee, 2007). It included items such as “I would be quite bored by a visit to an art gallery”; “When working on something, I don’t pay much attention to small details”; and “When it comes to physical danger, I am very fearful”. All items used the following 5-point Likert scale: 1 = “Strongly agree”, 2 = “Agree”, 3 = “Neutral (Neither agree nor disagree)”, 4 = “Disagree”, and 5 = “Strongly disagree” (Ashton & Lee, 2007).

3.3. The Field Experiment

The field experiment was conducted as an online study. This implies that all the stimulus material was administered electronically to participants. Since there is not yet a validated measurement scale available to measure business model adaptation, our measurement was closely aligned to the core features of the business model (Clauss, 2016): (1) the value proposition, (2) choice of target customer, (3) the structure of the value delivery, and (4) the value capture mechanisms.
The value proposition defines a portfolio of solutions (products and/or services) for customers (Morris et al., 2005; Johnson et al., 2008). Thus, to measure a change in this dimension, we asked whether respondents had introduced new products/services or reduced number of products/services. Further, business models can be adapted by changing the target customer, such as by offering the same service to an entirely new segment of customers (thereby creating a new market). Thus, to measure a change in the target segment, we asked whether respondents had increased sales effort to new customers or increased sales effort to customers abroad. The structure of value delivery defines how and by what means firms create value along the value chain using suppliers and external collaboration partners (Achtenhagen et al., 2013). Thus, we asked respondents whether they had established closer links with partners, used new suppliers, or engaged in reorganization. Value capture defines how value propositions are converted into revenues (Teece, 2010); thus, we asked respondents whether they had reduced or increased prices because of the crisis.
For the business model adaptation experiment, participants answered questions adapted and adjusted from Saebi et al.’s (2017) article on business model adaptation and risk domains, a 2010 survey of Norwegian firms after the financial crisis. Because “business model adaptation” is still a new term and measurement methods are not yet established, we deemed it appropriate to base the experiment on this survey.
Participants first chose at least two relevant external changes that their firm had experienced or was currently experiencing from the following list: changes in customer preferences, changes in supplier power, changes in technology, and changes in the competitive environment. We chose these external changes in line with the drivers of business model adaptation presented in Section 2 and Saebi et al.’s (2017) study. For each of the chosen external changes, participants viewed four replicates of scenarios of risk, developed using prospect theory, and were asked to make decisions based on these. The scenario with the least risk had a certain gain of 50 monetary units if no change was made and an uncertain expected gain of 80 monetary units if any changes were made. The riskiest scenario had an inevitable loss of 50 monetary units if no changes were made and an uncertain expected loss of 125 monetary units if changes were made. The scenarios were designed to measure participants’ reactions to scenarios of potential high gain, low gain, low loss, and high loss. There were two replicates for each of the domain’s potential gain and potential loss. The dependent measure was constructed based on the average changes made in each of the domains, high gain, low gain, high loss, and low loss, measured as the number of business model adaptation changes
The experiment was designed such that the more risk the participants wanted to take, the more business model adaptation changes they could choose. Making choices in both domains ensured that we collected sufficient data in the gain context, which was lacking in Saebi et al.’s (2017) study. To simplify the experiment, we reduced Saebi et al.’s nine business model adaptation options to seven. One option was to do nothing, and the remaining six were practical and general options that could be applied to the chosen external changes (change number of products or services, change prize of products or services, increase sales efforts toward new customers or customers abroad, adjust relationships toward suppliers and/or partners, reorganize the organization).
The experiment also contained several control variables and descriptive variables, which the participants were assured would not be used to track individual responses. This identifying information was deleted when it was no longer needed. The included variables were Gender (1: Male, 2: Female), Age (1: 24 or younger, 2: 25–34, 3: 35–44, 4: 45–54, 5: 55–64, 6: 65–74), Number of years in the position, Hierarchical position (1: CEO, 2: Top manager, 3: Middle manager, 4: Department Manager, 5: Other), Sector (1: Private, 2: Public).
After some pilot testing among students and associates, the design of the experiment was improved to simplify the process for participants and increase response rate and response accuracy. Some wording was clarified, and better instructions for the experiment were added. The personality test took 10–12 min to complete, and the experiment took an additional 5–8 min. The whole process took 17–20 min. The median time it took for the 94 individuals in the sample to finish the survey and experiment was 19.23 min.

4. Results

4.1. Repeated Measures Analysis of Variance to Test H1

In a one-way repeated measure analysis of variance, participants were exposed to the same experimental conditions (gains/losses), and the dependent variable had the characteristics of a continuous variable (business model adaptation). Thus, the data in the business model adaptation variables were considered sufficiently continuous, and we regard the necessary conditions to be met.
We compared participants’ business model adaptation—that is, the average changes made in the two domains—to determine whether significant differences were present between how many business model adaptation changes the participants chose in the domains. We measured differences between the main domains, potential gain, and potential loss (potential gain, M = 4.76, SD = 1.76, N = 95; potential loss, M = 3.45, SD = 2.13, N = 95). We observed a significant effect for the domains (Wilks’ lambda = 0.72, F = 36.78, p < 0.001). The effect provides support for H1b: business model adaptation was significantly higher in the domain of potential gain than in the domain of potential loss. As a consequence, it does not provide support for H1a.

4.2. Regression Analysis to Test H2

Before performing the regression analysis, we considered some prerequisites. We deemed the sample size of 95 adequate, as it is within the minimum number of accepted cases when considering the number of independent variables (Tabachnick & Fidell, 1989). We next examined the correlation between the independent variables and observed some correlation between the personality trait variables’ extraversion and emotionality (p = 0.4%). We tested for multicollinearity and found that all tolerance levels were far higher than 0.20 and all variance inflation factors were lower than 5, which indicates no multicollinearity (Christophersen, 2006). As stated in Section 2.1, we removed some outliers found in the initial data screening. We next screened for multivariate outliers by examining the Mahalanobis distance scores, and Cook’s distance scores indicated none. Residuals and scatterplots indicated that the linearity assumptions were supported, and we deemed the assumption of homoscedasticity satisfied using the same method. We observed a moderate deviation from normality, but we deemed it not severe enough to deny the assumption of normality for the variables (Christophersen, 2006).

4.3. Predicting Business Model Adaptation in the Domain of Potential Gain

Table 1 shows the predictive effect of the control variables (age, gender, managerial level, and number of years in the position) and the emotionality trait on business model adaptation in the domain of potential gain. The dependent variable in this regression is total gain—that is, the average changes made per external change in the domain of potential gain. Step 1 of the analysis shows that one of the control variables, gender, contributed significantly to the variance in the dependent variable, and therefore we retained it in hierarchical regression analysis. The other three control variables displayed no significant correlation, so we excluded them from the ensuing analysis to prevent a reduction in the significance of the regression model due to a decline in the degrees of freedom by including a higher number of nonrelevant, independent variables.
In step 2 of the analysis, we found that emotionality contributed significantly to the regression model, accounting for a further 4% of the variation in business model adaptation in the domain of potential gain. The effect was significant at the 1% level (p = 0.010). Gender and emotionality were significant predictors of business model adaptation in the domain of potential gain. In total, the variables accounted for 10.5% of the variance. These results suggest support for H2a, that high emotionality makes business model adaptation less likely in the domain of potential gain. We also observed a positive, significant relationship between business model adaptation and gender (p = 0.018), which indicates that the female participants were more inclined to choose business model adaptation changes than male participants.

4.4. Predicting Business Model Adaptation in the Domain of Potential Loss

Table 2 displays the predictive effect of the control variables (age, gender, managerial level, and number of years in the position) and emotionality on business model adaptation in the domain of potential loss. The dependent variable in this regression is total loss, that is, the average changes made per external change in the domain of potential loss. The analysis shows that the control variables made no significant contribution, so to avoid a reduction in significance, we excluded them from the hierarchical analysis. The next step reveals that emotionality did not contribute any significant variance in the dependent variable. The results of the analysis do not provide support for the proposed hypothesis (H2b) in the loss context.

5. Discussion

5.1. Theoretical Implications

As discussed in Section 2, research supports both prospect and threat-rigidity theories (Tsai & Luan, 2016). Saebi et al.’s (2017) study shows support for prospect theory, indicating that Scandinavian firms took more risks in the domain of potential loss than in the domain of potential gain following the financial crisis. Other findings supporting prospect theory show that it explains tax evasion (Dham & al-Nowaihi, 2007), and a sample of 3300 firms in 85 industries provides evidence that prospect theory can explain the tradeoff between risk and return (Fiegenbaum, 1990). An analysis of health insurance choice and risk preference shows that most people’s decisions reflected prospect theory rather than utility theory (Kairies-Schwarz et al., 2017). Threat-rigidity theory also finds support in research. For example, studies show that threats leading to a reduction in control encourage more internally directed actions, in line with threat-rigidity theory (Chattopadhyay & Huber, 2001), and researchers studying acquisitions have found similar results (Meschi & Métais, 2015). McManus and Sharfman (2017) show that when acquisitions were framed as threats, firms paid lower premiums, that is, they chose a less risky strategy. Tsai and Luan (2016) find that firm performance, risk-taking capabilities, and their interaction positively correlate with risk taking, again supporting the threat-rigidity argument. As these and the examples in Section 1 indicate, research shows equivocal results when attempting to predict firm risk behaviour. Our study provides additional proof of threat-rigidity theory, suggesting that managers are more concerned with the possibility of downside outcomes than upside outcomes as risk. According to threat-rigidity theory, one important reason for this perception is that external threats induce adversity, which in turn sets organizational change in motion. Such change is biased by the organization’s responses as structured by group interaction, formal information systems, core cultural rules, past organizational experiences, as well as the adaptation of other organizations’ solutions (Ocasio, 1993).
At the individual level, personality theories containing both light (e.g., conscientiousness) and dark (e.g., narcissism) traits might inform our understanding of how individuals respond differently to the same threat (Mazzei et al., 2025). For instance, certain personality traits such as emotionality might alleviate or aggravate the likelihood of rigid responses to a threat. Similarly, stress theories bestow insight into how individuals evaluate threats and suggest that the decision-making process for answering to threats may rely on obtainable response options (Mazzei et al., 2025). Many organization-level characteristics are also likely to play important parts if rigidity is present and whether the associated behaviors are appropriate (Hambrick & Mason, 1984; Hambrick & Fukutomi, 1991) As noted by Meyer (1982), rigidity varies with firm attributes (e.g., age, size, and complexity), but many of these variations remain to be investigated by future research (see Mazzei et al., 2025). For instance, is there an age threshold (e.g., established firm versus a start-up) for when firms have enough historical behavior to become more apt at responding to threats with rigidity? Are greater firms more likely to exhibit rigidity effects than small firms, due to more complex information systems and resource structures? Does the likelihood of rigidity increase in public firms, as opposed to private ones, due to increased scrutiny from shareholders and analysts (He & Tian, 2013; Shi et al., 2018)? According to Mazzei et al. (2025) all these questions are imperative and thus need to be answered by the research community.
Another important contribution of this study pertains to the impact of emotionality on the relationship between risk perception and business model adaptation. The observed significant, negative correlation between emotionality and business model adaptation in the gain context contributes to the understanding of the trait’s impact on risk taking and indicates potentially large significant effects of personality traits on business model adaptation. Although we observed no significant findings on emotionality’s impact in the loss context, the findings in the gain context accentuate the importance of this personality trait when potentially large payoffs can be achieved if risky decisions are made (see also Mazzei et al., 2025). The risk aversion displayed by participants with high emotionality scores in our experiment is in line with many previous contributions. Other studies show that emotionality is associated with less risk taking in the domains of both potential gain and potential loss (Weller & Tikir, 2011; Weller & Thulin, 2012). Studies using the Hexaco model show mostly significant results when testing for correlations between emotionality and risk taking. As touched on in Section 3.3, the emotionality trait has slightly more complex properties than neuroticism. Individual differences such as anxiety, sentimentality, and empathy versus fearlessness, detachment, and independence are assigned to the emotionality trait in the model (De Vries et al., 2009; Weller & Thulin, 2012). Including these traits can help explain why emotional individuals score lower on risk-taking behaviour such as business model adaptation.
Furthermore, risk taking and, by extension, business model adaptation are influenced by individuals’ risk perception and propensity to take risks. Sjöberg and af Wåhlberg (2002), referred to in Fyhri and Backer-Grøndahl (2012), find that emotionally unstable people perceive risk to be higher than emotionally stable individuals. Moreover, Oehler et al. (2018), using the five-factor model of personality, find that neuroticism is related to high risk aversion in undergraduate business students. Weller and Thulin (2012) also link emotionality to accentuated perceptions of risk. The rigid approach to risk taking and business model adaptation displayed by the field experiment participants with high emotionality scores is therefore in line with much of the previous literature on the topic. In summary, in contrast with previous research in which evidence of the effect of emotionality and its corresponding five-factor model trait is somewhat equivocal, this study contributes further proof of the negative relationship between managers’ level of emotionality and their propensity to adapt business models and take risks.

5.2. Practical Implications

In addition to the theoretical implications of our study, the results provide informational value for practitioners. The study provides further understanding of the business model adaptation concept and its applicability, as well as how personality traits can predict inclination to adapt the business model in different risk domains. The results are of particular value to firms aiming to create sustained competitive advantage and continuously capture and develop value in their environments, most notably due to the main finding of the study on the impact of emotionality on business model adaptation.
Moreover, the results of this study indicate that there are valid applications of personality tests. Managers without the skill or willingness to adapt a business model can act as barriers to change in firms (Massa & Tucci, 2013), and one reason they may do so is a high emotionality score. Entrepreneurs and recruiters can use this knowledge to review emotionality scores of applicants in jobs in which risk taking and an inclination to adapt the business model when necessary are of importance. Knowledge about the impact of personality traits is also useful when electing members for top management teams, as it is often this team that determines if and when a business model is ultimately changed (Teece, 2018). Top management teams are considered essential to eliminating barriers to change (Anyanwu, 2016). As a focus on business model adaptation is crucial for continuous performance growth and sustainable competitive advantage, considering applicant personality traits when making hiring decisions can be an important source of competitive advantage for firms.
Firms aiming to enhance their performance may therefore benefit from managers and top management team members with lower scores on the emotionality trait, as they are more likely inclined to adapt the business model. Consulting less emotional managers with an inclination to adapt and innovate the business model may also be an important tool for firms aiming to use business model adaptation as a competitive advantage. Consulting managers and top management teams with appropriate personality traits to be prepared for and respond to competitor business model adaptation may therefore be crucial for firms’ long-term survival.
As high emotionality in managers leads to them being less willing to adapt the business model when there is a potential for future gain, highly emotional managers may have a negative effect on firms trying to achieve sustained competitive advantage. Therefore, the main practical implication of this study is that a deliberate recruitment strategy of managers in firms, in which personality traits of managers also facilitate business model adaptation, is important if firms want to use business model adaptation as a competitive tool.

5.3. Limitations

Our data are based on single respondents in each firm, collected at one point in time, using one common method of data collection. Each of these weaknesses could be the source of potential biases. We hence applied ex ante remedies to control potential biases through the design of the study’s procedures (Podsakoff et al., 2003; Chang et al., 2010). For example, we took some measures before the data collection to prevent common method bias, most notably counterbalancing the order of the emotionality test and the business model adaptation experiment. Furthermore, the experiment presented the risk scenarios randomly, not in order of risk. Finally, we assured participants that their answers were anonymous, which should have reduced their evaluation apprehension (Podsakoff et al., 2003).
We also used ex post remedies to address common method bias in the statistical analysis of the data. One of the most popular ways to test for common method variance (CMV) is Harman’s test, which measures how much of the variance one variable is accountable for. Running this test on the data returned a score of 23%, which signified an acceptably low CMV. Many researchers recommend statistical methods of testing for CMV that are more sophisticated (Podsakoff et al., 2003), but Harman’s test is widely considered a sufficient indication that CMV is not a serious concern.
In addition, we used questions similar to Saebi et al.’s (2017), which provides our study with a higher test–retest reliability than if the questions had been developed from scratch. In other words, the field experiment was based on existing research on business model adaptation and its drivers (Saebi et al., 2017; Foss & Saebi, 2018),
A potential weakness related to validity is that we applied a rather limited sample (134 Scandinavian managers) drawn from a particular geographical area. Yet, we confirmed that the sample was representative of the population to which we wished to generalize the findings. In addition, it was within the minimum number of accepted cases when considering the number of independent variables (Tabachnick & Fidell, 1989)

6. Conclusions

We found that, in general, managers participating in a field experiment are more risk seeking in the gain scenarios than in the loss scenarios. Furthermore, our data show that managerial emotionality relates more to risk aversion than to risk seeking in the domain of potential gains. Both these results are in line with threat-rigidity theory, which stipulates that threats from the environment will lead to inward-looking conservative behaviour among managers and to a reliance on routines. High emotionality tends to increase conservatism in risky situations such as business model adaptations. Therefore, a practical implication of our results is that firms might consider refraining from hiring people with high scores on the emotionality trait into their top management teams.

Author Contributions

Conceptualization, C.A. and M.S.; methodology, C.A. and M.S.; software, C.A.; validation, C.A. and M.S.; formal analysis, C.A.; investigation, C.A.; resources, C.A.; data curation, C.A.; writing—original draft preparation, C.A. and M.S.; writing—review and editing, M.S.; visualization, C.A. and M.S.; supervision, M.S.; project administration, M.S. All authors have read and agreed to the published version of the manuscript.

Funding

The research received no external funding.

Institutional Review Board Statement

Not applicable, since analyses only were made on data that could not be traced to the specific subjects involved in the study.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Please take contact with the first author for possible access to the data.

Conflicts of Interest

The authors declare no conflict of interest.

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Table 1. Summary of hierarchical regression analysis for variables predicting business model adaptation in the domain of potential gains.
Table 1. Summary of hierarchical regression analysis for variables predicting business model adaptation in the domain of potential gains.
VariableStep 1Step 2
Age0.08
Gender0.20 **0.24 **
Manager level0.09
Years in position−0.16
Emotionality −0.026 ***
R20.070.11
Adjusted R20.020.09
ΔR2 0.04
ΔF1.663.84 *
N = 100, * p > 0.10, ** p < 0.05, *** p < 0.01.
Table 2. Summary of hierarchical regression analysis for variables predicting business model adaptation in the domain of potential losses.
Table 2. Summary of hierarchical regression analysis for variables predicting business model adaptation in the domain of potential losses.
VariableStep 1Step 2
Age−0.03
Gender0.002
Manager level−0.06
Years in position−0.09
Emotionality 0.04
R20.040.00
Adjusted R20.00−0.01
ΔR2 −0.04
ΔF1.05−0.20
N = 100, Note: Standard regression coefficients are shown.
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Aarøen, C.; Selart, M. Opportunities, Threats, and Strategic Choice: The Modifying Role of Emotion. Adm. Sci. 2025, 15, 331. https://doi.org/10.3390/admsci15090331

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Aarøen C, Selart M. Opportunities, Threats, and Strategic Choice: The Modifying Role of Emotion. Administrative Sciences. 2025; 15(9):331. https://doi.org/10.3390/admsci15090331

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Aarøen, Camilla, and Marcus Selart. 2025. "Opportunities, Threats, and Strategic Choice: The Modifying Role of Emotion" Administrative Sciences 15, no. 9: 331. https://doi.org/10.3390/admsci15090331

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Aarøen, C., & Selart, M. (2025). Opportunities, Threats, and Strategic Choice: The Modifying Role of Emotion. Administrative Sciences, 15(9), 331. https://doi.org/10.3390/admsci15090331

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