Exploring decision-making: experimental observations on project selection and the impact of justification pressure

Lukas, Christian; Neubert, Max-Frederik; Schöndube, Jens Robert Article — Published Version Exploring decision-making: experimental observations on project selection and the impact of justification pressure Journal of Management and Governance Provided in Cooperation with: Springer Nature Suggested Citation: Lukas, Christian; Neubert, Max-Frederik; Schöndube, Jens Robert (2024) : Exploring decision-making: experimental observations on project selection and the impact of justification pressure, Journal of Management and Governance, ISSN 1572-963X, Springer US, New York, NY, Vol. 29, Iss. 3, pp. 735-775, https://doi.org/10.1007/s10997-024-09717-9 This Version is available at: https://hdl.handle.net/10419/330803 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Vol.:(0123456789) Journal of Management and Governance (2025) 29:735–775 https://doi.org/10.1007/s10997-024-09717-9 Exploring decision‑making: experimental observations onproject selection andtheimpact ofjustification pressure ChristianLukas1 · Max‑FrederikNeubert2· JensRobertSchöndube3 Accepted: 29 July 2024 / Published online: 2 September 2024 © The Author(s) 2024 Abstract In this experimental investigation, we explore the impact of justification on project choices. Introducing a novel element, we implement asymmetric payoff schemes commonly employed in business, signifying distinct payoff distributions for the firm (principal) and the manager (agent). The agent has to choose one project from two options that differ in their risk-return profiles. The outcomes of our experiment substantiate our hypothesis, indicating that a mandate for justification decreases the probability of agents selecting the project with higher risk and return. The degree of this reduction appears to hinge on the nature of justification. Increased profit shares for the agent or a project recommendation from the principal can partially counterbalance the distortion in the project choice. Keywords Agency· Behavioral accounting· Experiment· Incentives· Justification· Project selection JEL Classification C72· C91· D81· M40· M52 1 Introduction It is a standard practice for managers to provide a rationale for their decisions and elucidate the outcomes of their choices or actions. In business, examples of justifications encompass various scenarios such as employees reporting to superiors, top * Christian Lukas [email protected] Jens Robert Schöndube [email protected] 1 Friedrich-Schiller-Universität Jena, Carl-Zeiss-Strasse 3, 07743Jena, Germany 2 Otto-von-Guericke-Universität Magdeburg, Universitätsplatz 2, 39106Magdeburg, Germany 3 Institute ofManagerial Accounting, Leibniz Universität Hannover, Königsworther Platz 1, 30167Hannover, Germany 736 C.Lukas et al. management addressing inquiries during investor calls, or the board commenting on company performance in general meetings. The requirement to justify decisions and outcomes in these and similar situations may lead to personal costs, manifesting as pressure, discomfort, or even severe stress for individuals tasked with defending themselves (Frimanson etal., 2021; Roberts, 2009). For instance, at the upper echelons of management, the CEO bears accountability to the board of directors. Consequently, the CEO must rationalise major decisions, particularly those of a strategic nature that impact the company’s long-term success, and address (negative) past outcomes before the board. However, the pressure to provide justifications is not exclusive to the top tier. Taking, for instance, a project manager who must explain delays or budget overruns in completing a project. The experimental research on justification predominantly utilises settings involving a single-person (Vieider, 2009, 2011), single-person settings with hypothetical second parties (Bauch & Weißenberger, 2020); Fehrenbacher etal., 2020), or twoperson settings with symmetric payoff structures (Pahlke etal., 2012).1 However, in a business context, payoff structures commonly exhibit asymmetry, leading to unequal payoff shares for the decision-maker, i.e., the manager, and the firm. Aligned with the primary goal of this study, we explore whether and in what manner justification influences project decisions within a context representative of control issues in firms. In this scenario, the authority to make project choices is delegated to a manager whose compensation is tied to the project’s success. However, acting as the residual claimant, the firm typically receives a share of the success distinct from the manager’s. The second aim of our study is connected to the conclusions drawn by Vieider (2009) and Pahlke etal. (2012), which propose that justifications heighten the probability of opting for risky projects that could result in losses. Given that analytical evidence (Lukas etal. (2019), or the model in the present paper) suggests a contrary prediction, indicating less inclination towards risk-taking, we examine our asymmetric payoff scheme concerning the decision maker’s propensity. Our third objective explores whether the nature of what the decision-maker has to justify makes a difference. Insights from the performance evaluation literature indicate that the mere expectation of defending one’s actions can be profoundly stressful for individuals tasked with justifying their actions and outcomes (Frimanson etal., 2021; Messner, 2009; Roberts, 2009). The way individuals perceive these personal costs may vary. Nevertheless, their impact probably also hinges on the justification, specifically, whether one has to justify outcomes or decisions. We contribute to the literature by investigating how three distinct justification regimes or types commonly employed in management practices influence the perceived costs of justification. These regimes differ in that the individual is required to justify (1) the decision, (2) the outcome, or (3) the outcome when it falls below a threshold. Another factor affecting the costs of justification may be superiors’ preferred courses of action, such as shareholders’ favoured investment strategy or the level 1 Pollmann etal. (2014) similarly utilised asymmetric payoffs, where the agent’s material payoff depends on the principal’s decision to reward the agent before or after the project choice. 737 Exploring decision‑making: experimental observations on… of risk the firm or division is willing to bear for its returns. Opting for a decision aligned with the preferred strategy is the most easily defensible choice (Tetlock, 1985), whereas varied preferences are likely to escalate the costs associated with justification. While these effects are intuitively understandable, as of our knowledge cutoff date, no specific evidence is derived from a setting with asymmetric payoff schemes as in our study. Hence, our fourth objective is to investigate how the communication of a preference interacts with asymmetric pay and types of justification concerning perceived costs of justification and project choice. To address our research inquiries, we conducted a computerised laboratory experiment involving 360 undergraduate and graduate student participants at Leibniz University Hannover (Germany) in several sessions in 2015 and 2019. We established an agency situation where the agent is accountable for choosing a project. The principal (firm owner) is the residual claimant of the project’s payoff and requires the agent to justify the project choice or performance if it falls below a predetermined level. The principal-agent pairs engage in interactions across multiple decision rounds. In each round, the agent chooses between two available projects: a standard project and a visionary project with a higher mean return and variance. We represent these projects as basic lotteries with two distinct but equally probable outcomes (low/high). Notably, the ex-ante efficient (visionary) high-risk/high-return project may result in a loss for the principal. The experiment involves three manipulations: first, we modify the agent’s payoffs from the available projects (adjusting his/her variable pay); second, we change the requirement to justify decision-making or performance; and third, we alter the principal’s opportunity to communicate project preferences (recommendations) to the agent. We formulate our hypotheses using a straightforward analytical model connected to Lukas etal. (2019). This model is situated within the framework of management control alternatives proposed by Merchant and Van der Stede (2007). It integrates results control (outcome-contingent pay), action control (justification), and personnel control (project recommendation clarifies the firm’s expectation). The experimental results essentially support our hypotheses. Here are our key findings: (i) the presence of a justification requirement decreases the probability of agents selecting more lucrative and riskier projects, (ii) principals’ initial recommendations and increased variable pay for such projects counteract the impact of justification, resulting in more frequent selection of such projects, and (iii) decision justification elicits the highest compliance with recommendations for such projects. We make two contributions to the experimental literature on justification effects. The first contribution relates to the (a)symmetry of the payoff scheme relevant in a setting with a justification requirement. The second contribution pertains to the event triggering the justification. Concerning the first contribution, we incorporate an asymmetric payoff scheme in a simplified manager-firm scenario. Contrary to studies utilising symmetric payoffs (Pahlke etal., 2012) or single-person settings (Vieider, 2009), our findings show that justification decreases the probability of participants opting for high-risk/high-return choices. Our study also diverges from Pollmann et al. (2014) who employ a reward-based justification approach where the decision-maker does not actively justify the decision or outcome. Instead, the 738 C.Lukas et al. principal rewards either the decision or the realised outcome, resulting in decreased risk tolerance. In contrast, our research explores the interactive effects of justification and asymmetric payoff schemes on project choices. As part of that investigation, we can demonstrate that justification does not consistently align with the principal’s best interests. Considering the evidence that decision-making on behalf of others is typically linked with a decrease in loss aversion (e.g., Chakravarty etal., 2011; Polman, 2012; Andersson etal., 2016), one might anticipate the riskiest decisions in our experimental conditions with justification. However, our observations reveal a more or less opposite outcome. Concerning the second contribution, we explore the effects of different “triggering events” of justifications on decisions. It is an empirical question if it makes a difference for decision-making whether the decision itself, low outcomes or losses following a decision trigger the justification requirement. Prior literature focuses on decision justification (Vieider, 2011; Fehrenbacher etal., 2020). We investigate low outcome justification and loss justification in addition to decision justification. Given our results, the various justification regimes appear to influence project selection and compliance with project recommendations from supervisors in distinct manners. Our experiment finds that for higher risk strategies, decision justification results in higher compliance rates than justifications for outcomes. This result may have implications for business as it suggests that firms could best align management decisions with the firms’ preferred strategy by requiring a justification for decisions rather than outcomes of decisions. Consequently, our findings contribute to comprehending diverse (justification-related) management controls, as outlined by Merchant and Otley (2007). The rest of this paper is organised as follows. The subsequent section provides an overview of related literature. Section3 establishes the theoretical framework and introduces a straightforward model that can derive testable hypotheses. Section 4 outlines the experiment, and Sect.5 presents its results. The concluding discussion in Sect.6 evaluates the findings of our study. 2 Related literature The body of literature on justification pressure and justification effects is expanding, as summarised by Patil etal. (2014).2 Our study aligns with the experimental literature exploring justification for choices involving risky alternatives or projects. In particular, it is related to the work of Vieider (2009), Pahlke etal. (2012), and Pahlke et al. (2015). Vieider (2009) observes in a single-person setting that loss aversion diminishes if the decision-maker is required to explain their choice after the fact. In essence, justification raises the probability of making risky decisions that could result in losses. Similar findings are reported in Pahlke etal. (2012) and Pahlke etal. (2015), who employ a two-person setting with symmetric payoffs and 2 There is a relation between justification and accountability. Lerner and Tetlock (1999) refer to accountability as the expectation that individuals may be obliged to justify their actions to others. 739 Exploring decision‑making: experimental observations on… show that justification reduces loss aversion while leaving other elements of risk attitude unaffected. Symmetric payoffs imply that the person who decides and the other passive recipient receive the same yield for each potential choice. The asymmetric payoff structure in our paper enables us to also examine how variations in the agent’s pay for performance affect risky project choices under justification pressure. By incorporating various types of justification, our study is related to the literature analysing if and how different justification arrangements interact and whether they affect decision-making differently. Siegel-Jacobs and Yates (1996) experimentally investigated how different justification types, outcome justification and decision justification, affect judgements about individual attitudes of other persons. The results suggest that decision justification incentivises people to consider relevant information in more detail, while outcome justification only produces additional noise in the individuals’ judgements. Justification arrangements are also particularly relevant in project management. Mac Donald etal. (2020) conducted interviews with project managers and revealed that these managers undergo various effects of justification. In response to the need for justification, project managers cultivate skills to facilitate decisions, anticipate problems, and manage multiple priorities. Mir and Rezania (2023) analysed data from a survey of project managers. They show that project managers’ justification of the project management decision process moderates the effect of the managers’ interactive use of project management control systems on project performance via team learning. While Leong (1991) considers justification of the decision process of project management and justification of project outcomes as accountability arrangements that are running parallel, Rezania et al. (2019) observe differences in the strength of both justification types among organisations. Finally, our investigation aligns with studies exploring both justification’s positive and negative effects on decision-making. Various experimental studies at the individual level have extensively documented the positive impacts of justification. For instance, justification pressure has been shown to decrease preference reversals (Vieider, 2011), mitigate overconfidence or order effects (Ashton, 1990; Jermias, 2006), diminish the influence of positive affective reactions (Fehrenbacher etal., 2020), and address inaccurate judgements and favouritism (Ashton, 1992; Bauch & Weißenberger, 2020). Additionally, studies by Webb (2002) and Arnold (2015) investigated the impact of perceived pressure to justify decisions and financial pressure on budgeting decisions. They observe that such pressure tends to reduce slack and enhance cooperation in decision-making processes. The experimental results of Ashton (1990) indicate that performance pressure may harm or improve performance depending on the existence of a decision aid. The availability of a decision aid can hurt performance as it changes the nature of the decision maker’s task. As we add the principal’s recommendation for the project choice to our experiment, we also consider some “decision aid” in our investigation. However, the decision aid is not based on a statistical regression like in Ashton (1990) but it is provided by the superior who has own interests. Thus, we complement the above findings by analysing how a decision aid from a superior who induces the justification pressure affects the decision maker’s choice. 740 C.Lukas et al. 3 Formulation ofmodel andhypothesis development Theoretical framework In business, numerous firms are overseen by managers rather than their owners, granting these managers significant decision-making authority. Firms employ various management controls to ensure that managers’ actions align with the owners’ objectives. Merchant and Van der Stede (2007) state that these controls can be categorised into results, action, personnel, and cultural controls. The interplay of these controls is essential, and when firms utilise different controls while considering potential interactions, they employ management control as a system (Grabner & Moers, 2013). Results controls are widespread and commonly manifest as outcome-contingent pay for management. Action controls include delegating decision rights or mandating justifications for decisions and outcomes. Personnel controls are means designed to communicate the firm’s expectations or what it “wants,” while cultural controls involve elements such as shaping the organisation’s identity. In our model, a firm engages a manager and presents a contract featuring outcome-contingent pay. The manager faces a decision between two distinct projects. The selection of a project, coupled with a random state of nature, determines the project’s outcome and, consequently, the payoffs for both the manager and the firm. Our model incorporates and integrates three types of controls: variable pay contingent on the project’s outcome, serving as a results control; justification, functioning as an action control; and a project recommendation, operating as a personnel control. In formulating the management control system, the model firm considers the interaction between these controls. The firm’s primary aim is to prompt the management to make the desired project choice at the lowest possible cost. The underlying framework of the model draws on agency theory, where we designate the firm as the principal and the manager as the agent. The setting We examine a principal-agent scenario in which the principal delegates the decision regarding a project to an agent. It is assumed that both contracting parties are risk-neutral. The outcome, denoted as xi of Project i=A,B , is contingent on the realised state of nature. There are two equally likely states of nature denoted as: {state 1, state 2}. If state 1 occurs, the outcome of Project i is Li , while in state 2 it is Hi , with Hi>Li . We categorise Project A as the “standard project” and Project B as the “visionary project.” In the event of state 1 realisation, the gross outcome of the visionary project for the principal is less than that of the standard project, LB<LA . Additionally, we assume that Project B has a higher expected outcome and higher outcome variance. Hence, with HB > HA . The agent’s compensation, denoted as wi and contingent on the selection of Project i, corresponds to a bonus contract, a common practice in many firms. Specifically, the compensation function wi=(f,si) comprises a fixed payment f and a bonus (rate) si . E ( xA ) =0.5 ( HA+LA ) <0.5 ( HB+LB ) =E ( xB ) Var (x A )=0.25(H A −L A )2<0.25(H B −L B )2=Var(x B ) , 741 Exploring decision‑making: experimental observations on… The fixed payment f remains unaffected by the project choice or outcome. Alongside the fixed payment, the agent is entitled to a bonus of si monetary units per unit of the project outcome, provided the outcome is positive. This arrangement implies the use of results controls by the principal. The bonus rate si can be interpreted as the agent’s share of the (positive) outcome of Project i. We assume LB < LA=0 and Hi > 0 for i=A,B to streamline the analysis. In formal terms, the compensation contract for the agent comprises a menu of two compensation functions {wA,wB} from which the agent chooses one by implementing Project i. Therefore, the agent’s expected compensation, given project choice i is expressed as: We assume that the agent is protected by limited liability, necessitating that si≥0 and f≥0 must be satisfied. Justification pressure The agent is tasked with the responsibility of project selection. In response, the principal requires a justification from the agent, contingent on poor project performance or for the chosen project. The justification serves as an action control implemented by the principal. In practice terms, the justification provides the agent with a chance to clarify deviations from anticipated results, elaborate on factors that impeded the project’s proper implementation, or provide evidence supporting the view that a particular project choice was optimal based on pre-project analysis. While this may lead to positive outcomes for both the agent and the company, the process undeniably induces stress and discomfort for the agent (Messner, 2009; Frimanson etal., 2021). Stress is notably probable when an explanation is required following subpar performance. Nevertheless, even if the justification pertains to the decision rather than its outcome, there is a basis to assume that the ultimate result influences how the agent’s decision is assessed by superiors or shareholders (Lipe, 1993). When the agent decides while the outcome remains uncertain, the agent will consider the potential for suboptimal project performance. We denote the psychological stress and effort associated with justification as justification pressure JPi for Project i. JPi signifies the expected costs of justification, and is expressed as follows: The function JC(xi),xi=Li,Hi , represents the justification costs when the outcome xi occurs after choosing Project i. The indicator variable 𝜄 x i∈{0, 1} is zero if the principal does not request a justification for outcome xi ; if a justification is required, 𝜄 x i =1 . Intuition suggests that JC(xi) is a monotone decreasing function - the higher (1) E(wi)=f+0.5siHi. (2) JP i =0.5 [ 𝜄 Hi JC(H i )+𝜄 Li JC(L i ) ]. 742 C.Lukas et al. the realised outcome xi the less stressful it is to justify the result or decision.3 Establishing the following relations then is straightforward: If the principal requires a justification for a loss, 𝜄LB =1 ; if a low outcome necessitates justification, 𝜄LA = 𝜄 LB =1 ; all other indicator variables are set to zero. Given Eqs.(3) and (4), it is then easy to establish the following relationship: If the agent is required to justify a loss or a low outcome, the anticipated justification costs for choosing option B are consistently higher than for option A. Furthermore, suppose JC(xi) is sufficiently strictly convex, indicating that justifying the potential loss from Project B is sufficiently stressful. In that case, the inequality in (5) remains valid even when the agent has to justify the project choice regardless of the outcome. If the principal can indicate a preference for a particular project using personnel controls, this will probably impact justification costs. For instance, if the principal prefers ventures with higher risk, such as Project B in our model, justifying the potential loss from Project B is likely less burdensome. In a broader sense, when the principal signals a preference for Project i, justifying the selection of i for a specific outcome is expected to be less stressful than insituations without the signaled preference. Conversely, indicating a preference for Project j should raise the costs of justifying i. To integrate the impact of the recommendation, we modify Eq.(2) as follows: Here, RECj indicates the principal’s preference or recommendation for Project j, communicated to the agent. The variable 𝜌ji reflects the effect of the recommendation. We assume 0 <𝜌 AA = 𝜌 BB < 1 . If the selected Project i aligns with the recommended Project j, meaning i=j , it results in a vertical downward shift of the initial justification cost function. Conversely, if the agent does not choose the recommended Project j, justification costs increase, 𝜌AB = 𝜌 BA > 1 . Following this reasoning, the subsequent relations are derived: (3) JC(LB)>JC(LA), (4) JC(HB) < JC(HA). (5) JPB > JPA. (6) JP i(RECj)=0.5 [ 𝜄H i 𝜌jiJC(Hi)+𝜄L i 𝜌jiJC(Li) ], 3 An alternative rationale for a decreasing justification cost function stems from the principle of loss aversion. It is widely acknowledged that individuals tend to loss aversion, wherein negative (monetary) values carry greater weight than positive values of equal magnitude (Brink & Rankin, 2013; Sawers etal., 2011; Tversky & Kahneman, 1991) Consequently, agents, perceiving a loss to be more impactful on the principal than the corresponding gain, face higher justification costs when justifying a loss than a gain of equivalent magnitude. Given that LB < LA=0 , where the low outcome under Project B signifies a loss, the justification cost function JC(xi) would exhibit a convex decreasing trend caused by loss aversion. 749 Exploring decision‑making: experimental observations on… of the determinants of justification pressure. Figure1 provides a summary of the experiment. In each regime, we implement the justification manipulation through a computer chat. The agent is mandated to enter the justification, and the principal has the opportunity to respond. We (correctly) anticipate that agent participants experience sufficient discomfort when they justify themselves. Our second manipulation seeks to understand the effects of communication of owners’ preferences (recommendations) for project decisions. This is executed by enabling upfront communication of the principal’s preference in treatments REC, LOS-REC, DEC-REC, and LOW-REC. Technically, in every decision round, the principal’s project preference is presented on the agent’s computer screen before the agent makes a project decision. The agent has the liberty to either adhere to or disregard the recommendation. No additional communication, such as through chat, is allowed. Our third manipulation involves varying the principal and agent payoff shares if Project B is chosen (Table3 in the appendix). The escalating outcome share for the agent reflects the increase in variable pay that might be necessary to incentivise projects with higher expected returns (and return variance). This variation allows us to portray Project B as relatively more attractive to the agent than Project A regarding expected compensation. The manipulation of outcome shares enhances the internal validity of the experiment. Implementing the three manipulations, the initial substantial part of our analysis concentrates on examining how these manipulations influence the agents’ frequencies of selecting the project with higher risk and return. Correspondingly, the number of agents’ selections of Project B (ChoiceB) is the primary variable of interest. The second significant aspect of our investigation centers on the psychological effects of the manipulations, specifically, justification pressure ( JPi in the model). Fig. 1 Overview of the experiment’s setup and treatments (bold solid arrows = treatments LOW, RECLOW; bold narrowly dashed arrows = treatments DEC, REC-DEC; bold widely dashed arrow = treatments LOS, REC-LOS) 750 C.Lukas et al. In line with the definition of Lerner and Tetlock (1999), we posit that only agents in justification conditions experience justification costs. To assess its perception and extent, we examine data from a computerised post-experimental questionnaire that mandates agents in justification conditions to report their experience with the justification requirements. As measurements after each decision likely influence behavior in subsequent rounds, we collected the data after completing the experiment. The questionnaire comprises seven questions where subjects rate their experience with justification on a 9-point scale. We calculate Cronbach’s alpha coefficient to evaluate whether the items are conceptually related. With a value of 0.84, the internal consistency of our questionnaire’s scale is considered good. Following standard practice, we operationalise the construct with the justification pressure variable JP, representing the average scores agents achieve on the corresponding items. To ensure the internal validity of our experiment, we employ standardised questionnaires that are acknowledged for their reliability and validity. Additionally, we utilise various tests and regression models to verify the robustness of the results. Several controls complement the data of our main variables. Alongside sociodemographic information (Age, Sex), we gather data on the subjects’ risk attitude (WillRisk) through a pre-experimental questionnaire. The questionnaire is constructed based on the German Socio-Economic Panel (SOEP). Furthermore, we consider inequality aversion since other-regarding preferences could impact decision-making in our experiment. We ascertain the variable for inequality aversion (InequalityF) using a test from Fortin etal. (2007), that categorises participants into three classes of inequality aversion (low, medium, and high). Other influences that merit consideration stem from the repeated interaction of the participants (multiple decision sets, fixed matching). The first such influence is reputation building. For instance, as the principal observes the agent’s track record of decisions, it could be possible that the agent aims to shape the principal’s perception of her/him. However, we do not believe that agents have the incentive to manage their reputation. If the agent is unaware of the principal’s preference, it is unclear which image is “right.” Even if the principal’s recommendation (preference) is known, it is uncertain whether an image as a “B-decision maker” or “A-decision maker” enhances the agent’s utility. As a second factor, we examine the participants’ potential for cooperative or retaliatory behavior in chat communication treatments. Accordingly, we analyse chat contents to differentiate between undesired communication and behaviors influenced by justification, actual recommendations, or the pay scheme. An instance of undesired collusion is a principal communicating her/his preference to the agent through the justification chat in treatments with justification but without a recommendation. Another example involves principals and agents revealing their names via chat and agreeing to share the payoff after the experiment. Identifying suspicious chats for a principal-agent pair in a given round excludes all observations after the 751 Exploring decision‑making: experimental observations on… particular round.6 In total, 76 out of 1080 agent-round observations are eliminated due to collusive behavior. The majority of eliminations are identified under decision justification (36), followed by low outcome justification (21) and loss justification (19). We attribute the variation between the treatments to the fact that there is the most frequent opportunity for collusion under decision justification, followed by low outcome justification and loss justification. No disputes or acts of retaliation are identified. The experiment was conducted in the Leibniz Laboratory of Experimental Economics at Leibniz University Hannover. The software hroot (Bock et al., 2014) was employed for organisation and administration processes. The experiment was programmed using the software zTree (Fischbacher, 2007). 360 undergraduate and graduate students from various fields participated, resulting in an overall sample of 180 principal-agent pairs. The proportion of female subjects in the experiment is 40.56%. The gender distributions of principals and agents are similar within each treatment and do not vary significantly between treatments. The highest proportion of female agents is found in the loss justification condition (50%). On average, the students are 23.84 years of age and attend courses in the 5th semester. Regarding content, 49.72% of the participants are enrolled in STEM courses, while the rest are distributed between economics and management, teaching, and some other fields. Experimental sessions lasted approximately 60min, and earnings averaged 11.23 Euro.7 After arriving in the lab, participants received written instructions containing all relevant details about the experiment. A video film was played in which an experimenter (who was not present during the experiment) read the complete instructions, and explanatory screenshots for the upcoming experiment were presented. This was followed by participants reading the written instructions at their assigned seats. Any clarifying questions were addressed at the participants’ seats. Prior to the commencement of the actual experiment, participants were required to answer several control questions to ensure a thorough understanding of the experimental situation. Our experiment participants received compensation through an initial endowment of 30 Taler (3 Taler = 1 Euro) and the payoff from a specific decision round. Instead of all rounds, paying for only one round was done to avoid wealth effects (Charness etal., 2016). The round relevant to the payoff was publicly and randomly selected after the experiment. The initial endowment for each participant guaranteed that the sum of the initial endowment and the payoff in the relevant decision round could not be negative, meaning no participant could incur a monetary loss in the experiment. 6 To maintain neutrality, the process of analysing chat contents and filtering observations was conducted by multiple third parties. These individuals were unfamiliar with the hypotheses and were uninterested in the experiment’s outcomes. 7 Additional details regarding the dataset can be provided upon request. 752 C.Lukas et al. 5 Results oftheexperiment Concerning Hypothesis 1, we examine how decision-making is influenced by justification. Figure2 illustrates the Project-B choices of agents without recommendation.8 Agents choose the risky Project B less frequently when justification is present. This suggests that the manipulation in the experiment (justification yes/no) was effective. To determine whether these differences are statistically significant, we conducted a t-test. The Shapiro-Wilk test confirms normality for the corresponding variables. We find the difference between agents operating under any justification (without a recommendation) and the baseline statistically significant (t-test: t=2.511 , df =522 , p=0.012 ). This result provides initial support for Hypothesis 1. Asserting that the best decision aligns with the principal’s interest, not necessarily maximising expected value, we control for the principals’ preferences when assessing the impact of justification. Although our model predicts that principals prefer Project B, it is reasonable to assume that there might be principals favoring the less risky investment (Project A). Again, focusing only on treatments without a recommendation, Fig.3 illustrates the agents’ decisions. It appears that justification reduces agents’ selections of Project B even when principals prefer the risky Project B (t-test: t=2.097 , df =265 , p=0.037 ). When considering principal-agent pairs where the principals prefer Project A, the effect of justification on Project B choices shows a similar trend but does not reach a Fig. 2 Descriptive statistics for Project B choices (ChoiceB) of agents under no (JustDum=0) and any justification (JustDum=1), excluding treatments with recommendations. The bar chart displays the mean rates at which agents choose Project B in various decision sets. The percentage values above each bar indicate the average selection rates for Project B across all decision sets. The number of observations is presented at the bottom of each bar. Error bars are included to represent the 95% confidence intervals 8 A comprehensive compilation of round-by-round results is available in the appendix, specifically in Tables4 and 5. 753 Exploring decision‑making: experimental observations on… significant level (t-test: t=−1.367 , df =255 , p=0.173 ). In a preliminary summary, it seems that justification generally results in less risky choices by agents. When principals prefer higher-risk (investment) strategies without the agents being aware of this preference, the justification works against the interests of the principals. However, when principals prefer lower-risk strategies, justification does not appear to work against their interests. To better understand the agents’ decisions, we performed logistic regression analyses9 for the agents’ choices of Project B and Table2 presents the results. Concerning Hypothesis 1, models (1) and (2) are pertinent, focusing on agents who must justify decisions or outcomes without controlling for the type of justification. While model (1) includes the variables of interest, model (2) also incorporates various control measures. The odds ratios of JustDum indicate a reduction in the likelihood of a Project B choice due to justification. For example, the ratio of 0.542 suggests that under justification (compared to the baseline), we anticipate finding only 0.542 agents selecting Project B for every agent choosing Project A. This magnitude remains Fig. 3 Descriptive statistics for Project B selections of agents operating under no justification or any justification (no distinction between types), controlling for uncommunicated project preferences of principals. Bar charts, differentiated by lighter (no justification) and darker (with justification) gray, illustrate the impact on Project Bchoices (ChoiceB) by agents in treatments without (JustDum = 0) and with (JustDum = 1) justification. The two left bar charts depict the impact of justification on Project B choices (ChoiceB) agents make when their principals prefer Project B (PrefB). The two bar charts on the right show the effect on agents’ Project B choices (ChoiceB) when principals prefer Project A (PrefA). Treatments with recommendations, where principal preferences are communicated, are excluded. The percentage numbers above each bar represent the mean rates of selecting Project B across all decision sets, with the number of observations displayed at the bottom of each bar. Error bars are included to represent the 95% confidence intervals 9 Non-linear regression analysis is frequently used in psychology research when the dependent variable is binary (Gomila, 2021). Multiple linear regression models confirm our results. See also Wooldridge (2002) for insights into appropriate models. 754 C.Lukas et al. Table 2 RE logit models on the agents’ Project B-choices Random effects logit models on the agent’s Project Bchoices including data from all decision sets and treatments. The dependent variable ChoiceB is a binary variable signaling for each round if the agent selects Project B. JustDum is a binary variable signaling whether an agent must justify the decision or outcome (independent of the type ChoiceB (1) (2) JustDum 0.542 0.624 (0.206) (0.222) Rec A 0.424* 0.537 (0.201) (0.252) B 26.052*** 28.910*** (20.727) (21.275) JustDum#Rec 1 A 0.647 0.421 (0.403) (0.259) 1 B 0.341 0.233* (0.312) (0.200) E[ShareB] 40% 1.994** 2.001** (0.647) (0.650) 45% 2.057** 2.032** (0.593) (0.587) 60% 7.391*** 7.438*** (3.044) (3.056) 80% 57.813*** 60.535*** (50.735) (50.850) Var[ShareB] 0.975*** 0.974*** (0.006) (0.006) WillRisk 1.123 (0.081) InequalityF Medium 0.171*** (0.113) High 0.201*** (0.078) Age 0.982 (0.023) Male 1.596* (0.435) Semester 1.093** (0.044) Observations (n) 1.004 1.004 Wald chi2 79.11 101.37 Prob > chi2 <0.001 <0.001 755 Exploring decision‑making: experimental observations on… consistent when controlling for individual characteristics such as risk attitude and sociodemographic details. Both models narrowly miss the significance threshold for the main effect, however, the interaction between JustDum and Rec reaches statistical significance in Model (2). It seems that justification significantly affects project decisions when recommendations have been made beforehand. Figure4 provides an overview of these effects. We notice that the decrease in predicted probabilities is more pronounced after a recommendation (triangle and square ends) than without a recommendation (circle ends). By calculating the margins, we discover that justification reduces the predicted probability of a Project B choice after a Project B recommendation of justification). Rec is a categorical variable indicating whether the agent receives a recommendation for Project A or B. Its base level is “no recommendation.” E[ShareB] is a categorical variable indicating the agent’s expected profit share (in percent) in state 2 when selecting Project B. Its base level is “30%.” Var[ShareB] indicates the variance of the agent’s payoff share (in absolute numbers) when selecting Project B. Control variables are the subjects’ age (Age), sex (Male), study progress (Semester), inequality aversion (InequalityF), andrisk attitude (WillRisk). Coefficients are presented in exponentiated form, i.e., as odds ratios. Standard errors (in parentheses) are clustered at the individual level. The constants are included in the models but not reported * p < 0.1 ** p < 0.05 *** p < 0.01 Table 2 (continued) Fig. 4 Marginsplot of predicted probabilities for Project B choices by agents under no justification or any justification (no distinction between types), including Project B and Project A recommendations. The line with triangles (squares) depicts predicted probabilities after Project B (Project A) recommendations. The line with circles illustrates probabilities for agents receiving no recommendations. The percentage numbers next to each line represent the corresponding predicted probabilities. The analysis is based on 1.004 observations, and error bars are included to represent 95% confidence intervals 756 C.Lukas et al. by 15.36% ( z=−2.590 , p=0.010 ) and by 21, 52% ( z=−2.670 , p=0.008 ) after a ProjectA recommendation. Based on the results from our regression models and tests, we identify justification as a crucial factor in the agents’ decision-making. In line with the prediction of the theoretical model, justification seems to diminish the attractiveness of projects with high risks and returns. This effect appears to persist even in the presence of recommendations. Thus, for Hypothesis 1, we state: Result 1 Experimental evidence supports Hypothesis 1, justification leads to a less frequent choice of the high-risk/high-return project (Project B). Shifting our focus to Hypothesis 2, Fig.5 illustrates descriptive data. Depending on the presence or absence of a justification requirement, it aligns the Project B choices of agents in treatments without recommendations with the Project B choices of agents in treatments with Project B recommendations. Our theoretical framework posits that when the principal makes a Project B recommendation, it signals a willingness to bear losses to the agent. This, in turn, results in lower justification pressure and has the potential to counteract the negative effect of justification on Project B choices, as predicted in Hypothesis 1 and documented above. Fig. 5 Descriptive statistics for Project B choices (ChoiceB) of agents with and without Project B recommendations. Bar charts, differentiated by light (without recommendations) and dark (with recommendations) gray, depict the impact of Project B recommendations (RecDum=0 vs. Rec=B). The percentage values above each bar represent the overall rates at which agents choose Project B across all decision sets, with the number of observations listed at the bottom of each bar. Error bars represent the 95% confidence intervals. The two bar charts demonstrate the effect of Project B recommendations when agents are not required to justify decisions or outcomes (JustDum=0). At the same time, the two bar charts on the right show the effect under justification (JustDum=1). The plots do not include the decisions of agents receiving a recommendation to choose the less risky Project A 757 Exploring decision‑making: experimental observations on… Even though recommendations are not legally binding, it is evident that agents tend to follow them. We define this tendency as compliance. For instance, around 95% of participants chose Project B after receiving the corresponding recommendation in the REC treatment, whereas only 58% opted for Project B in the BL (see RecDum=0 vs. Rec=B comparison when JustDum=0). These differences are statistically significant (t-tests: t=−6.287 , df =245 , p<0.001 (JustDum=0); t=−8.022 , df =506 , p<0.001 (JustDum=1)). Table 2 presents additional evidence regarding the influence of recommendations: the coefficients associated with Project B recommendations outweigh those of all other decision drivers.10 For Hypothesis 2, we thus state: Result 2 Experimental evidence supports Hypothesis 2, indicating that recommendations from principals favoring Project B increase the possibility of agents choosing the high-risk/high-return project (Project B). Concerning Hypothesis 3, increasing the agent’s outcome share, i.e., rewarding the high outcome with a larger bonus, is expected to boost the probability of Project B choices. According to our model, a higher bonus compensates for the agent’s justification pressure and the discomfort potentially causing losses on the principal. This mechanism could shift the balance in favor of Project B. Observations from the experiment align with this idea, as evident from Table2, where the expected bonuses for Project B statistically significantly motivate agent Fig. 6 Descriptive statistics for perceived justification pressure (JP) among all agents under various types of justification without recommendations: decision justification (DEC), low outcome justification (LOW), and loss justification (LOS). The numbers above each bar represent the mean justification pressure scores, with the number of observations indicated at the bottom of each bar. Error bars are included to depict the 95% confidence intervals 10 Additionally, Table5 and Fig.8 in the appendix reveal that recommendations favoring Project A from some principals are also effective. 758 C.Lukas et al. participants to choose Project B (indicated by coefficients of E[ShareB]). The logistic regressions in the baseline (BL) further demonstrate significant coefficients. This suggests that Project B choices (and the apparent “willingness” to cause losses for the principal) are potentially influenced by the agent’s profit share, even in the absence of justification pressure and recommendations (Table6 in the appendix). We conclude as follows: Result 3 Experimental evidence supports Hypothesis 3, suggesting that a higher profit share for the agent mitigates the impact of justification and increases the likelihood of opting for the high-risk/high-return project (Project B). To test Hypothesis 4(a), we utilise our measure of justification pressure to compare the scores of agents operating under different types of justification. We predict the highest justification pressure score under decision justification for either project [see (14)-(15)]. The results for every type of justification are presented in Fig.6. The justification pressure scores are roughly on the same level for each of the three justification regimes. We find greater variance in perceived justification pressure under decision justification compared to the low outcome and loss justification. Given that the Shapiro-Wilk test does not confirm normality for each variable, non-parametric testing using the two-sided Wilcoxon-Mann–Whitney test for independent samples (WMW) is employed. The results of this test confirm that the differences do not achieve statistically significant levels (WMW: DEC vs. LOW: z=−0.923 , p=0.356 ; DEC vs. LOS: z=−0.309 , p=0.758 ; LOW vs. LOS: Fig. 7 Descriptive statistics for project choices of agents receiving specific recommendations under various types of justification: no justification (REC), decision justification (DEC-REC), low outcome justification (LOW-REC), and loss justification (LOS-REC). The percentage numbers above each bar indicate the mean compliance rates of agents with the principals’ project recommendations across all decision sets. The number of observations is presented at the bottom of each bar, with error bars representing 95% confidence intervals 765 Exploring decision‑making: experimental observations on… Instructions to the experiment 766 C.Lukas et al. Payoff distribution See Appendix Table3. Table 3 Payoff distribution (in experimental currency Taler) between the principal and the agent for each project, state of nature and decision set The distribution is equal in all treatments. Subjects are only able to observe the payoff distribution of the current decision round, not the distribution of previous or subsequent decision sets (i.e., the first payoff distribution is only observable in round one, the second distribution only in round two, etc.). Expected values and variances are also not displayed in the experiment 767 Exploring decision‑making: experimental observations on… Project choices and preferences, treatments without recommendations See Appendix Table4. Table 4 Agents’ project choices and principals’ project preferences for each decision set in treatments without recommendations BL refers to baseline, DEC to decision justification, LOW to low outcome justification, and LOS to loss justification. Only agents were responsible for project selections. We also recorded the principals’ preferences in each decision set in treatments without recommendations. The upper numbers in each cell display the absolute frequencies, the lower numbers illustrate the relative rates in percent. Note that some decisions of agents are eliminated due to subjects communicating preferences over the justification chat or engaging in other kinds of illegal collusion (see footnote 6). Thus, numbers within some treatments can change between decision sets Agents’ choices BL LOS DEC LOW Project A Project B Project A Project B Project A Project B Project A Project B Set 1 15 13 21 9 7 12 12 11 53.57% 46.43% 70.00% 30.00% 36.84% 63.16% 52.17% 47.83% Set 2 13 15 16 14 7 9 9 9 46.43% 53.57% 53.33% 46.67% 43.75% 56.25% 50.00% 50.00% Set 3 7 21 13 15 1 10 6 13 25.00% 75.00% 46.43% 53.57% 09.09% 90.91% 31.58% 68.42% Set 4 14 14 18 8 5 4 14 5 50.00% 50.00% 69.23% 30.77% 55.56% 44.44% 73.68% 26.32% Set 5 11 17 17 7 6 7 12 8 39.29% 60.71% 70.83% 29.17% 46.15% 53.85% 60.00% 40.00% Set 6 10 18 12 11 5 5 9 9 35.71% 64.29% 52.17% 47.83% 50.00% 50.00% 50.00% 50.00% Principals’ preferences BL LOS DEC LOW Project A Project B Project A Project B Project A Project B Project A Project B Set 1 7 21 10 20 8 11 10 13 25.00% 75.00% 33.33% 66.67% 42.11% 57.89% 43.48% 56.52% Set 2 12 16 13 17 4 15 11 12 42.86% 57.14% 43.33% 56.67% 21.05% 78.95% 60.87% 39.13% Set 3 10 18 14 16 5 14 14 9 35.71% 64.29% 46.67% 53.33% 26.32% 73.68% 60.87% 39.13% Set 4 13 15 14 16 10 9 13 10 46.43% 53.57% 46.67% 53.33% 52.63% 47.37% 56.52% 43.48% Set 5 17 11 15 15 11 8 14 9 60.71% 39.29% 50.00% 50.00% 57.89% 42.11% 60.87% 39.13% Set 6 20 8 15 15 12 7 20 3 71.43% 28.57% 50.00% 50.00% 63.16% 36.84% 86.96% 13.04% 768 C.Lukas et al. Project choices and preferences, treatments with recommendations See Appendix Fig.8 and Table5. Fig. 8 Distribution of project recommendations (Rec) of principals in all treatments. The percentage numbers above each bar represent the mean rates at which principals recommended Project B across all decision sets. At the bottom of each bar stands the number of observations. The error bars represent 95% confidence intervals 769 Exploring decision‑making: experimental observations on… Variables in tests and regressions (1) Variable Description JustDum Dummy variable = 1 if subject operates in an justification treatment (no distinction between the types of justification) Age Years of age Table 5 Agents’ project choices and principals’ project preferences for each decision set in treatments with recommendations REC refers to the treatment with recommendations, DEC-REC to decision justification with recommendations, LOW-REC to low outcome justification with recommendations, and LOS-REC to loss justification with recommendations. Only agents were responsible for project selections. For treatments with recommendations principals had the possibility to make a recommendation prior to the project selection. The upper numbers in each cell display the absolute quantities, the lower numbers illustrate the relative rates in percent Agents’ choices REC LOS-REC DEC-REC LOW-REC Project A Project B Project A Project B Project A Project B Project A Project B Set 1 10 18 14 9 5 10 9 5 35.71% 64.29% 60.87% 39.13% 33.33% 66.67% 64.29% 35.71% Set 2 8 20 9 14 2 13 7 7 28.57% 71.43% 39.13% 60.87% 13.33% 86.67% 50.00% 50.00% Set 3 6 22 8 15 4 11 5 9 21.43% 78.57% 34.78% 65.22% 26.67% 73.33% 35.71% 64.29% Set 4 12 16 13 10 6 9 9 5 42.86% 57.14% 56.52% 43.48% 40.00% 60.00% 64.29% 35.71% Set 5 8 20 15 8 6 9 10 4 28.57% 71.43% 65.22% 34.78% 40.00% 60.00% 71.43% 28.57% Set 6 11 17 13 10 3 12 9 5 39.29% 60.71% 56.52% 43.48% 20.00% 80.00% 64.29% 35.71% Principals’ preferences REC LOS-REC DEC-REC LOW-REC Project A Project B Project A Project B Project A Project B Project A Project B Set 1 12 16 13 10 7 8 7 7 42.86% 57.14% 56.52% 43.48% 46.67% 53.33% 50.00% 50.00% Set 2 9 19 10 13 5 10 5 9 32.14% 67.86% 43.48% 56.52% 33.33% 66.67% 35.71% 64.29% Set 3 11 17 11 12 4 11 7 7 39.29% 60.71% 47.83% 52.17% 26.67% 73.33% 50.00% 50.0% Set 4 21 7 15 8 6 9 10 4 75.00% 25.00% 65.22% 34.78% 40.00% 60.0% 71.43% 28.57% Set 5 17 11 14 9 7 8 9 5 60.71% 39.29% 60.87% 39.13% 46.67% 53.33% 64.29% 35.71% Set 6 19 9 17 6 4 11 9 5 67.86% 32.14% 73.91% 26.09% 26.67% 73.33% 64.29% 35.71% 770 C.Lukas et al. Variable Description ChoiceB Dummy variable = 1 if the agent chooses Project B in a specific decision round of the experiment FreqRevealedPrinRiskAve Number of times the principal revealed risk aversion, i.e., preferences for avoiding the risky project, via chat (variable is used to exclude cases from statistics, tests, and regressions) FreqRevealedPrinRiskTol Number of times the principal revealed risk tolerance, i.e., preferences for an investment in the risky project, via chat (variable is used to exclude cases from statistics, tests, and regressions) ID1 Individual identification number of subject ID2 Team identification number of subject InequalityF Level of inequality aversion indicated by a measure from Fortin etal., 2007 (0 = low aversion; 1 = medium aversion; 2 = high aversion) JP Mean self assessment score in questions 1, 3, 5, 6, 8, and 14 of the post-experimental questionnaire concerning the perception of justification pressure (see post-experimental questionnaire); items 10 to 13 were excluded from our constructs as these questions capture changes in justification pressure over the course of the experiment, rather than the perception itself; moreover, item 15 was eliminated because it showed only small correlations with the other items and its exclusion increased Cronbach’s alpha Male Dummy variable = 1 if subject = male Variables in tests and regressions (2) Variable Description E[ShareB] Categorical variable for the agent’s expected payoff share (in percent) of total firm profit in Taler (experimental currency) if s/he chooses Project B and state 2 realises (30%; 40%; 45%; 60%; 80%) PreReli Measure indicating whether subjects perceive that they have given correct information in the pre-experimental questionnaire Rec Categorical variable for recommendation in a decision round (0 = no recommendation; A = Project A-recommendation; B = Project B-recommendation) Reli Measure indicating whether subjects perceive that they have given correct information in the post-experimental questionnaire RecDum Dummy variable = 1 if subject receives a Project recommendation (no distinction between the type of recommendation) RevPrinRiskAve Dummy variable = 1 if the principal communicated risk aversion, i.e., preferences for avoiding the risky project, in the previous decision round via chat (variable is used to exclude cases from statistics, tests, and regressions) RevPrinRiskTol Dummy variable = 1 if the principal communicated risk tolerance, i.e., preferences for an investment in the risky project, in the previous decision round via chat (variable is used to exclude cases from statistics, tests, and regressions) Round Decision set of the experiment Semester Current length of study measured in number of semesters/terms State State of nature (bad = 1; good = 2) Subject Categorical variable for role of the subject (1 = agent; 2 = principal) Treatment Condition the subject is part of (1 = BL; 2 = LOS; 3 = REC; 4 = LOS-REC; 5 = DEC; 6 = DEC-REC; 7 = LOW; 8 = LOW-REC) Var[ShareB] Variance of the subject’s payoff share from Project B WillRisk Measure for willingness to take risks from SOEP (see questionnaire risk attitude) 771 Exploring decision‑making: experimental observations on… See Appendix Table6. Table 6 RE logit models on the agents’ Project B-choices in BL Random effects logit models on the agents’ Project B-choices including data from the baseline (BL), i.e., no justification and no recommendations. The dependent variable ChoiceB is a binary variable signaling for each round if the agent selects Project B. E[ShareB] is a categorical variable indicating the agent’s expected profit share (in percent) in state 2 when selecting Project B. Its base level is “30%.” Var[ShareB] indicates the variance of the agent’s payoff share (in absolute numbers) when selecting Project B. Control variables are the subjects’ age (Age), sex (Male), study progress (Semester), inequality aversion (InequalityF), and risk attitude (WillRisk). Coefficients are presented in exponentiated form, i.e., as odds ratios. Standard errors (in parentheses) are clustered at the individual level. The constants are included in the models but not reported * p < 0.1 ** p < 0.05 *** p < 0.01 ChoiceB (1) (2) E[ShareB] 40% 1.969 1.971 (1.607) (1.608) 45% 1.773 1.773 (1.207) (1.206) 60% 7.810* 7.830* (8.819) (8.863) 80% 29.968 30.128 (74.218) (74.792) Var[ShareB] 0.982 0.982 (0.016) (0.016) WillRisk 1.200 (0.198) InequalityF Medium 0.453 (0.507) High 0.332 (0.316) Age 0.929 (0.073) Male 1.690 (1.109) Semester 1.303* (0.147) Observations (n) 168 168 Wald chi2 6.89 14.69 Prob > chi2 0.2287 0.1973 772 C.Lukas et al. Acknowledgements We acknowledge helpful discussions and comments from Timothy W. Shields at the 12th Workshop on Accounting and Economics in Tilburg, 2016, Catherine Roux at the 17th GEABA Symposium in Basel, 2016, and Robert Grasser at the 2019 ENEAR meeting in Maastricht. We also would like to thank Sergeja Slapnicar, Barbara Schöndube-Pirchegger, Carina Keldenich and Christine Lücke for comments and ideas. We are also grateful to Kay Blaufus, Martin Fochmann, and Nadja Fochmann for their support in conducting the experiment. Funding Open Access funding enabled and organized by Projekt DEAL. The dean’s office of the Faculty of Economics and Management of the Leibniz Universität Hannover supported the research and partially financed the experiment with 2.000 euros. There was no further involvement of the faculty or other third parties. Declarations Conflict of interest The authors have no relevant financial or non-financial interests to disclose. Ethical approval Approval to conduct experiments with human participants in the Leibniz Laboratory of Experimental Economics (its-Pool) has been granted by the dean’s office of the Faculty of Economics and Management of the Leibniz Universität Hannover. 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