Capital Budgeting Decisions, Cash Flow Forecasts, and Management Accountants’ Motivated Reasoning: A Field Study

Marc Wouters; Frank Stadtherr

doi:10.2139/ssrn.3812939

Capital Budgeting Decisions, Cash Flow Forecasts, and Management Accountants’ Motivated Reasoning: A Field Study

Wouters, Marc;Stadtherr, Frank 2021-03-17 00:00:00 Management accountants who are preparing cash flow forecasts for capital budgeting decisions may have preferred conclusions that lead to motivated reasoning. Whereas previous research has mainly demonstrated antecedents of accountants’ motivated reasoning (e.g., client pressure), we look more closely at the phenomenon of accountants’ motivated reasoning itself. We conducted a field study in the management accounting department in product development in a car company. We describe two detailed episodes around the technical design of new cars, preparation of cash flow forecasts, and decisions on capital investment projects. We develop and provide empirical evidence for a theoretical framework that builds on key elements of motivated reasoning: normative ambiguity and justification. The framework includes four ways in which accountants may exploit normative ambiguity for influencing their forecasts, and it contains four ways for accountants to create justification by showing comparisons. Keywords: cash flow forecasts, capital budgeting, motivated reasoning, product development, management accountants’ work Electronic copy available at: https://ssrn.com/abstract=3812939 1. Introduction Forecasts of future financial results play an important role for investors and managers, and forecasting is a central topic in accounting research (e.g., Brüggen et al. 2020; Cassar and Gibson 2008; Kadous et al. 2009). Forecasts may contain intentional and unintentional biases that influence decisions of investors and management (Hirst et al. 2007; Armstrong et al. 2007; Goodman et al. 2014; Hribar and Yang 2016), Whereas most research has focused on biases in disclosed, accruals- based earnings forecasts (e.g., Rogers and Stocken 2005; Veenman and Verwijmeren 2018; Dong et al. 2017), we consider undisclosed, cash flow forecasts that are related to capital budgeting decisions (Brüggen and Luft 2016). Managers often have incentives to provide biased information in the process of preparing such cash flow forecasts, for example because they want to increase the probability of initial acceptance of a capital investment proposal (Turner and Guilding 2012; Fehrenbacher et al. 2020; Brüggen and Luft 2011; Haka 2006). Even though biased cash flow forecasts for capital investment proposals is an economically highly relevant issue, our current understanding is still sparse. We focus on accountants in the process of preparing cash flow forecasts for capital budgeting decisions. Although accountants may also be involved in biasing, we know almost nothing about their role. The profession promotes the image of the neutral, transparent, and fact-based accountant (AICPA 2017; IMA 2019), but research shows many instances of behavior that differs from this image. Accountants and CFOs are not only acting as independent information preparers but sometimes deliberately distort information (Maas and Matějka 2009; Bishop et al. 2017; Feng et al. 2011; Indjejikian and Matějka 2009). Against this background, we investigate how accountants might be biasing cash flow forecasts in capital budgeting processes. Better understanding their behavior is important, because decision-makers rely on input from accountants and biased information may unknowingly influence their decisions. Electronic copy available at: https://ssrn.com/abstract=3812939 We propose that motivated reasoning theory may help to explain accountants’ biasing in this setting of preparing cash flow forecasts for capital budgeting decisions. Motivated reasoning theory (Kunda 1990; Boiney et al. 1997) explains that people with directional preferences ‘‘search for, interpret, and process information in a biased manner and, consequently, are more likely to reach the preferred conclusion’’ (Kadous et al. 2003, 759). Prior accounting research has found extensive support for behavior in line with motivated reasoning theory, including auditors and tax professionals (Anderson et al. 2017; Bradshaw et al. 2016; Kadous et al. 2003, 2008; Koch and Salterio 2017; Kadous et al. 2013; Luft et al. 2016). We expect motivated reasoning to also play a role when accountants are creating cash flow forecasts for capital budgeting decisions. They may not be neutrally evaluating the decision alternatives and letting the forecasted financial outcomes determine their recommendations (AICPA, 2017; IMA, 2019). Instead, accountants—like other stakeholders in capital budgeting processes—may prefer particular investment decisions. These preferences would create a directional goal to prepare a cash flow forecast that supports their preferred alternative. Accountants could engage in motivated reasoning to reach that goal, i.e., to show their preferred alternative in a financially favorable light. That something like that would happen may not be so surprising, but we investigate how it may happen. Much potential for motivated reasoning may exist in the setting of preparing cash flow forecasts for capital investment proposals. Motivated reasoning depends on normative ambiguity, in other words, vagueness about what conclusions should or should not be reached. Motivated reasoning also depends on the ability to appear rational and to provide justifications for conclusions. Considering these factors, motivated reasoning would seem feasible and likely to occur. Uncertainty about the future creates ambiguity and much of the input for capital budgeting forecasts is “soft” information (Kadous et al. 2005; Rowe et al. 2012), i.e., it can be changed in one direction or the other, because it is difficult to formally, objectively verify the information Electronic copy available at: https://ssrn.com/abstract=3812939 (Bertomeu and Marinovic 2016). Accountants may also have considerable opportunities to create justifications for their cash flow forecasts, not only because of uncertainty, but also because they have substantial flexibility for how to construct these internal, undisclosed, cash-flow forecasts that are not bound to financial reporting standards (Goretzki et al. 2018). Moreover, forecast biasing is more likely if it is more difficult to detect (Armstrong et al. 2007). The cash flow forecast in our context concerns a separate decision, instead of entity-wide results, making it difficult to later isolate the actual impact of the specific decision, to verify the forecast, and to pinpoint any biases. On the other hand, how motivated reasoning would work is a priori quite puzzling and the focus of this paper. Management accountants need to rely on significant input from others (Goretzki and Messner 2019), also in the context of capital budgeting (Rowe et al. 2012), which reduces their control over the forecast and could limit opportunities for forecast biasing and constructing justifications. Also, information asymmetry between accountants and management may be less than between management and external stakeholders. Managers may be able to critically review the forecasts that support the accountants’ recommendations (Rowe et al., 2012), which may reduce opportunities for forecast biasing that remains unnoticed. Furthermore, for biasing cash-flow based forecasts, the accountants cannot exploit the typical accrual mechanisms (e.g., around revenue recognition, discretionary accruals, or asset valuation) (Liu 2019). Finally, how would accountants engage in motivated reasoning whilst needing to maintain an image of competent and truthful professionals (Goretzki et al. 2018)? Against this background, we investigate how accountants engage in motivated reasoning when preparing cash flow forecasts for capital budgeting decisions. We focus on their means for influencing and justifying their forecasts. We conducted a field study to gain detailed insights into how accountants created estimates of the financial consequences of key decisions regarding the technical design of new products. We could establish that the accountants had directional Electronic copy available at: https://ssrn.com/abstract=3812939 preferences, i.e., their own ideas regarding these decisions. We could also establish how the accountants created cash flow forecasts that supported their preferred conclusions. Particular courses of action they wanted managers to implement were shown in a financially favorable light. We could identify a number of tactics accountants adopted for this purpose, which provide a more nuanced understanding of how motivated reasoning “works” in an organizational context. As a first contribution, we develop a deeper understanding of how accountants may produce biased cash flow forecasts for capital investment proposals. Uncertainty about the future creates normative ambiguity. We develop four mechanisms for actively making use of this ambiguity for influencing cash flow forecasts, and we provide empirical evidence from the field study. These mechanisms are based on claiming specific number inputs, making particular choices for the detailed implementation of a foresting method, determining the scope of the forecast in terms of alternatives and criteria that are being considered, and interacting with information providers depending on whether the information they provide is consistent with the accountant’s preferred conclusion. This result provides a more detailed understanding of the process of motivated reasoning in accounting. It complements research that has focused on antecedents of motivated reasoning in other accounting settings, but not much on how accountants engage in motivated reasoning (Kadous et al. 2003, 2008; Koch and Salterio 2017; Kadous et al. 2013; Austin et al. 2020). This results also contributes to the broader topic of budgeting for planning and resource allocation, which warrants specific research, besides performance evaluation (Becker et al. 2016). Second, we develop a more specific understanding of how accountants can create justification for their biased cash flow forecast by showing comparisons. Motivated reasoning requires the ability to appear rational and provide justification for a decision. It is known that showing comparisons supports justification (Kadous et al. 2013; Rowe et al. 2012; Huikku and Lukka 2016) and we build on this. We develop four different ways in which comparisons can be Electronic copy available at: https://ssrn.com/abstract=3812939 shown and provide empirical evidence from the field study. These ways are based on making connections between different parts of the forecast calculation, and on linking the forecast to hard numbers and sources outside the forecast calculation, to calculation methods used in other forecasts, and to the broader context. The remainder of the paper is structured as follows. Section 2 reviews literature to motivate the research question. Section 3 motivates and describes the research method. The findings from the study at the case company are presented in Section 4. We discuss these findings to develop a theoretical framework in Section 5, and Section 6 concludes the paper. 2. Background and research questions Biased cash flow forecast for capital investment projects Cash flow forecasts for capital investment projects may include unintentional errors. Technical issues, such inadequate forecasting techniques or missing data, could be causing these (Turner and Guilding 2012). Psychological biases may also be at work, such as over-optimism (Haka 2006) or escalation of commitment during later project stages when cash flow forecasts are being updated (Brüggen and Luft 2016). However, intentional errors may also play a role. Managers who provide input to cash flow forecasts may be inclined to misrepresent their private information. If they benefit from having a capital investment accepted, they may provide information that increases the likelihood of project acceptance, especially when they are competing for limited investment resources (Brüggen and Luft 2011). They might overstate sales numbers or sales revenues and understate required investment amounts or operating expenses. Psychological mechanisms leading to biases may also be at work on the side of the proposal reviewers and decision- makers, such as biases by affective reactions to a proposing manager (Kida et al. 2001; Fehrenbacher et al. 2020). Reversely, it may also in the interest of the manager to estimate less favorable cash flows, so targets for the investment proposal become more easily achievable, but this is less likely in a capital budgeting setting compared to operating budgets, because of the risk that the project may be completely rejected (Brüggen and Luft 2016). Electronic copy available at: https://ssrn.com/abstract=3812939 Managers are typically the agents who provide biased cash flows in the research, and the role of accountants is usually not addressed (Haka 2006). Perhaps accountants are assumed to be independent advisors (AICPA 2017; IMA 2019), who should make sure that decisions are based on neutral and verifiable forecasts: “whenever computation shows that an investment decision would delay or jeopardize the fulfillment of their (cash) objectives, the management accountants are not slow to wield their veto” (Lambert and Pezet 2011, 20). However, research in other contexts has shown many instances of behavior that differs from this image of neutral information providers, such as CFOs manipulating earnings (Bishop et al. 2017; Feng et al. 2011; Indjejikian and Matějka 2009) and management accountants selectively distributing or withholding information (Goretzki et al. 2018; Mahlendorf et al. 2018; Puyou 2018), misreporting data (Fauré and Rouleau 2011; Maas and Matějka 2009) or creating budgetary slack (Davis et al. 2006; Indjejikian and Matějka 2006). Therefore, the role of accountants biasing cash flow forecasts for capital budgeting decisions also seems theoretically worthwhile to investigate. Instead of management accountants having no prior opinion about a particular capital investment decision—they wait and see what the cash flow forecasts suggests should be done— management accountants may have their own preferences, i.e., they could favor a particular capital investment already before creating a cash flow forecast. Management accountants as business partners are expected to work closely with local management and to be involved in decision- making. Understanding the preferences of powerful managers, management accountants may consider it beneficial for themselves to support those preferences with their forecasts, similar to auditors and tax accountants responding to client preferences (Kadous et al. 2003, 2008; Koch and Salterio 2017; Hatfield et al. 2011; Austin et al. 2020; Cloyd and Spilker 1999). Furthermore, management accountants’ identity as business partners creates self-expectations (Wolf et al. 2020; Morales and Lambert 2013). Seeing themselves as analysists who need to understand the business Electronic copy available at: https://ssrn.com/abstract=3812939 from a financial perspective, they may believe that particular investment decisions would benefit the organization and, therefore, consider it acceptable to bias their cash flow forecast to promote those investment decisions. Pro-organizational motives can drive such behavior (Mahlendorf et al. 2018). In their study, accountants and CFOs with stronger organizational identification were more willing to not disclose negative company information if they believed this would benefit their organization, and this behavior was not associated with career self-interest. We do not focus in this study on the question why the management accountants would have particular preferences, but if they do, we investigate how these preferences may be biasing their cash flow forecasts for capital budgeting decisions. Motivated reasoning of accountants We analyze the accountants’ biasing of cash flow forecasts though the lens of motivated reasoning theory. The reasoning of people is sometimes not driven by accuracy goals but by motivational goals. Motivated reasoning concerns an individual’s cognitive processes for intentionally pursuing the goal of reaching a desired conclusion (Kunda 1990; Boiney et al. 1997). If from the beginning, an individual prefers a particular conclusion, such a directional motivation biases the judgment process. People selectively retrieve information in their own memory and creatively combine knowledge. They are more skeptical to the quality of information provided to them when it is inconsistent with their preferences. They process and present information to reach the preferred conclusion. For example, “people who want to believe that they will be academically successful may recall more of their past academic successes than of their failures. They may also use their world knowledge to construct new theories about how their particular personality traits may predispose them to academic success” (Kunda 1990, 483). Motivated reasoning depends on normative ambiguity (Kadous et al. 2003, 2008). Lacking clear guidance, for example from objective benchmarks, hard evidence, or strict rules, it becomes Electronic copy available at: https://ssrn.com/abstract=3812939 unclear what conclusions should or should not be reached. Furthermore, motivated reasoning depends on the ability to provide justification. People want to appear rational and they try to construct a plausible justification for the desired conclusion (Kunda 1990). Biasing in motivated reasoning is limited by “reasonableness constraints” and “while motivated decision makers will bias their judgments to favor the desired outcome, they will try to avoid biasing them more than necessary” (Boiney et al. 1997, 5). In other words, normative ambiguity and justifiability require sufficient degrees of freedom: there must be a range of conclusions that can be drawn and flexibility for constructing the reasoning towards those conclusions. We belief it is worthwhile to investigate accountants’ motivated reasoning in the specific situation of when they are creating forecasts for capital budgeting decisions. First, because this setting is empirically relevant. The way in which capital investment decisions are taken is clearly of economic importance and so we need to understand what kind of information accountants provide for these decisions. Research so far provides only limited insights into actual behavior of accountants in this setting. Although prior research suggests various biases that emerge with accountants in different roles (Maas and Matějka 2009; Bishop et al. 2017; Feng et al. 2011; Indjejikian and Matějka 2009), we lack empirical support for such behavior when it comes to preparing information for capital investment decisions. Second, this setting is theoretically relevant. Prior research provides an understanding of factors that are likely to enhance motivated reasoning in accounting, but we know far less about the phenomenon of motivated reasoning itself. Factors that have shown to be conducive to motivated reasoning include the nature of auditor focus and the strength of accuracy goals (Austin et al. 2020), the presence of quality assessment and the strength or directional goal commitment (Kadous et al. 2003), the level of practice risk (Kadous et al. 2008), independence threats and litigation risk (Blay 2005), advice justifiability and social ties with advice providers (Kadous et al. Electronic copy available at: https://ssrn.com/abstract=3812939 2013), client pressure and client affinity (Koch and Salterio 2017), or the implementation of an audit judgment rule (Kang et al. 2020). However, little is known about how accountants engage in motivated reasoning. How does motivated reasoning in an accounting context “work”? What do accountants actually do for modifying and justifying their recommendations? What are the accounting mechanisms that play a role in motivated reasoning? Our setting of creating cash flow forecasts for capital investment decisions is theoretically interesting for studying motivated reasoning itself. There is much potential for motivated reasoning but a priory, it is puzzling how accountants would be able to bias and justify their cash flow forecasts. It is a suitable setting for studying how motivated reasoning in accounting could “work.” How does motivated reasoning in accounting “work”? We would expect behavior in line with motivated reasoning to occur in the setting of accountants preparing cash flow forecasts for capital investment projects. Normative ambiguity surrounding the preparation of the cash flow forecast is high and considerable opportunities for constructing a justification exist. Uncertainty about the future makes it difficult to know which investment decision would be best. Moreover, the forecast is not disclosed and not bound to financial reporting standards, so there is considerable flexibility regarding the technical method for preparing the forecast. Furthermore, post-decisional verification is more difficult compared to earnings forecast, because it may be harder to isolate the actual outcomes in the organization’s overall results and to demonstrate forecast inaccuracy. However, it is theoretically not evident how motivated reasoning would work in this setting. First, we know less about motivated reasoning in a business context (Boiney et al. 1997), which differs from the situation in which an individual has a personal preference and is motivated to arrive at that preferred conclusion through biased, individual retrieving and processing of personal information. An organizational context involves multiple stakeholders, who also have their own Electronic copy available at: https://ssrn.com/abstract=3812939 goals and private information sources. Furthermore, routines partly formalize decision-making processes and limit flexibility for information preparers. Moreover, there is accountability of information preparers and decision makers to various audiences, and accountants need to fulfil particular roles. Accountants would want to be seen as acting according to professional expectations of objectivity and neutrality, which may limit motivated reasoning. Even more so, because they need to consider that other stakeholders in the organization also have information and may be able to critically review the forecast (Kadous et al. 2005). Accountants in an organizational context also depend on other stakeholders to provide information, which accountants may not be able to change when they are preparing their forecast. These organizational circumstances limit the degrees of freedom for influencing and justifying the forecast. Second, it is theoretically not evident how motivated reasoning would work because the setting of preparing cash flow forecasts for decisions on capital investment projects differs from preparing accrual-based earnings forecasts. Biasing of earnings forecasts may work through known accrual mechanisms (“tricks” around discretionary accruals, asset valuation, and revenue recognition, for example), which are not applicable to the same degree in cash flow forecasts. This technical circumstance makes it even more intriguing how management accountants’ motivated reasoning would lead to influencing their forecasts. In sum, the research question for this study is how accountants exhibit motivated reasoning when preparing cash flow forecasts for capital investment projects. 3. Research method A field study on cost management in product development The initial, broader focus of the research was cost management in product development, in particular through methods such as product modularity that go beyond target costing (Davila and Wouters 2004). We wanted to know how technical approaches such as modular design and product Electronic copy available at: https://ssrn.com/abstract=3812939 platforms were used for cost management purposes, how these approaches were implemented, how accounting departments and accountants were implicated, how tradeoffs were quantified, and which further issues played a role. Although modularity is a much-researched topic (Campagnolo and Camuffo 2009; Fixson 2007, 2005; Jiao et al. 2007), little is known about modularity for the specific purpose of cost management in product development (Labro 2004; Anderson and Dekker 2009; Jørgensen and Messner 2009, 2010). The exploratory nature of the research motivated conducting an in-depth field study in a single case company. Field research involves in-depth study of real-world accounting phenomena through direct contact with the organizational participants (Merchant and Van der Stede 2006) and provides the opportunity to grasp an accounting phenomenon in a broader context, to understand why it exists, how it works, and what its effects are (Hopwood 2007; Malsch and Salterio 2016). Recent interview-based field studies (Bills et al. 2018, 2020; Free and Murphy 2015; Free et al. 2021) and in-depth case studies demonstrate this potential (Ahrens and Chapman 2004; Free 2007; Fiolleau et al. 2013; Goretzki et al. 2017; Pfister and Lukka 2019; Väisänen et al. 2020; Wouters and Roijmans 2011). Field research starting with a theoretical focus offers can offer opportunities for surprising insights that trigger a process of going back and forth between thinking about theoretically relevant questions and explanations and collecting further information in the field (Ahrens and Chapman 2006). An abductive research process (Lukka and Modell 2010) capitalizes on the possibility to look at field data through a theoretical lens that changes over time, which helps to develop a better theoretical understanding of the accounting phenomenon and also guides and potentially redirects the course of the empirical field study. Furthermore, we intended to conduct the study in a car company, because of the strategic importance of cost management during product development in that industry (Anderson 1995; Ansari et al. 2006; Ibusuki and Kaminski 2007; Mahmoud-Jouini and Lenfle 2010). We aimed to Electronic copy available at: https://ssrn.com/abstract=3812939 conduct an interventionist field study (Suomala et al. 2014; Lyly-Yrjänäinen et al. 2017; Baard and Dumay 2021), because this could provide access to the organization at an unparalleled level (Jönsson and Lukka 2006), which we considered important because of the required in-depth understanding of complex product development processes and cost management methods that are used in car companies. We expected such an understanding to be very difficult to obtain by “only” visiting the company. Interventionist research involves the researcher being part of the action, longitudinally, as an asset for collecting detailed information, including information that may be difficult to specify from the outside and which may not always be shared with outside researchers (even if they would know which information they could request). Interventionist research varies with respect to the strength of the intervention. We only had a modest intervention (Jönsson 1996) in mind, interacting closely with organizational members to gather information, to contribute ideas, and to work on activities as these emerged. Thus, the collaboration was based in a genuine interest of the researchers to be working on issues the company considered helpful, yet the collaboration was also a means to another end: to establish a particularly good access and to collect information that would unlikely be available for outside researchers. Case company access and data collection We approached a car company known for a key project in the area of modularity and platforms for the purpose of cost management, making it a so-called extreme case (Cooper and Morgan 2008) that is useful to develop and test new theoretical insights. AutoCompany (a disguised name used in this paper) provided the opportunity to do the intended kind of interventionist study, involving two researchers who are also the authors of this paper. They were employees of the same university. The senior researcher visited the company but was basically offsite and coached the The crucial role of product development for cost management is notwithstanding the impact that short-term decisions later in the product life cycle may have on earnings in the car industry (Brüggen et al. 2011). Electronic copy available at: https://ssrn.com/abstract=3812939 research process. The junior researcher was mostly onsite, working in a management accounting department of AutoCompany that focused solely on product development activities and comprised around two hundred management accountants. The top manager of this department reported directly to AutoCompany’s CFO. Both researchers met roughly every six months with this top manager to discuss progress and the further direction of the research project. The university received funding from AutoCompany to be able to employ the junior researcher. Neither of the researchers received any personal financial compensation from the case company. At AutoCompany, the researcher was working as a colleague, involved in the daily business of the management accounting department. He interacted with many different organizational members in the course of working together (see Table 1), which provided an opportunity for collecting data, such as by making notes on conversations and observations, asking specific questions, exchanging emails, receiving company presentations and other documents, discussing background information regarding presentations and other documents, and having access to information systems. Thus, interviews solely for research purposes played a minor role in this study. We will present a few quotes, but our findings are mainly based on observations and artefacts, i.e., the cash flow forecasts. We have been able to collect much background information about how these artefacts had been shaped. Working together with people in the organization provided a natural way to build a much deeper understanding of what happened for the creation of the cash flow forecasts, before they were being formally presented in meetings. [Insert TABLE 1] Over time, the research topic became more focused. In the second half of the research project, the researcher was actively involved in producing analyses for several decisions concerning AutoCompany’s future modular strategy. Several themes resonated with us, such as the enormous uncertainty surrounding these analyses and anyway the impossibility to model all relevant Electronic copy available at: https://ssrn.com/abstract=3812939 considerations. We also noticed the very different preferences people had, not based on the financial numbers, and how the accountants fought to get attention for their viewpoints and analyses. We selected two episodes at AutoCompany as the empirical sources. The researcher played an active role as a management accountant in these episodes, but we investigate the decision preferences of other management accountants than the researcher, and other management accountants were driving how the forecasts were prepared. Data analysis Analyzing the information and guiding the research happened in layers. From the beginning, both researchers kept their own separate research diaries. This was a way to reflect on what was happening in the organization, the research process, interesting topics, angles for the potential theoretical contribution of the study, and emerging theoretical ideas. For the researcher on site, the research diary was also one medium for collecting data by making notes on events, conversations, meetings, and so on. He also made handwritten notes during the workday in hardcover notebooks. The research diary and these notebooks turned out to be important and helpful assets that contained much information that enabled us to write the empirical part of this paper. The researcher wrote extensive chronological summaries of each of the two episodes, including hundreds of references to internal documents, such as presentations, meeting minutes, and emails, and to the notes in the research diary and handwritten notebooks. We used these summaries for discussing the findings, which was the basis for writing next versions of the extensive summaries, now more structured around on the interesting aspects the researchers had identified, such as: what did the management accountants want, how did the company deal with The researcher was involved in a third episode, which became the basis for a study on target costing and specifically addressed the inclusion of market-based cost targets for product development activities and model-specific investments. We did not include a reference to this paper to avoid disclosing author identifying details. Electronic copy available at: https://ssrn.com/abstract=3812939 uncertainty, how were rough assumptions derived and included in the calculations, how and why were such assumptions (not) presented, discussed, and accepted or rejected? In parallel, we started writing, discussing, rewriting, etc. our texts on theory development in the form of a working paper (writing theoretical ideas was always going on “in the background” in the research diaries). The abductive process of finding the specific ideas for the current paper was characterized by intense iterations between the literature, the data and our own evolving ideas and texts. 4. Case study Company background AutoCompany followed a modular product strategy, which it described as overall guidelines for utilizing car projects with the goal to realize synergies as well as to master and reduce complexity across cars and car segments. The case company was at the beginning of developing a new modular architecture, which would cover several vehicle types (sedans, station wagons, SUVs) in several size segments. A modular architecture consisted of several platforms and modules. A platform referred to the lower part of the car body, where the engine, transmission, axles and seats are connected, providing the common base of cars with similar dimensions. A module was defined as a “technical group of components that form a functional and logical unit, which is completely interchangeable.” Modules were meant to be used by all cars within the same modular architecture, sometimes with adaptions. A key intended benefit of the modular strategy was to save costs. Developing the new modular architecture required many decisions on the fundamental design of cars, which involved significant capital investments in product development and production assets. Management accountants produced forecast of the cash flow consequences of decision Segment is the European term for vehicle classes. For example, minicompact, subcompact, compact, mid-size, large, minivans and sports utility vehicles (SUV) correspond to cars in the A-F, M, and J segments. Instead of letters, we use numbers for segments (“Segment 3” and “Segment 5”) in the field study to disguise information. Electronic copy available at: https://ssrn.com/abstract=3812939 alternatives, which were surrounded by much uncertainty. Other departments, in particular engineering, production, procurement, and marketing and sales provided inputs for producing the forecasts and also provided qualitative arguments apart from the financial forecast. The forecasts were discussed at several hierarchical levels, sometimes more than once, and finally, the executive board level made the formal decisions. The first episode of the case study describes decisions to potentially have common platforms across different brands. AutoCompany was part of a large corporation (CarCorporation) that included two other brands, which we will name VehicleFirm and CarEnterprise. The second episode concerns a decision about how to design battery electric vehicles next to traditional combustion engine vehicles. Table 2 provides an overview of the case study section. [Insert TABLE 2] Episode 1: Common platforms? The starting point AutoCompany and other brands of CarCorporation were sometimes offering car models in the same size segments, but these car models shared little technology (apart from engines). This episode describes the investigation of the financial consequences if AutoCompany would base future car models on common platforms, together with VehicleFirm in Segment 3 and together with CarEnterprise in Segment 5. This episode lasted around four months. The two project teams (one for each segment) consisted of representatives from several departments of the involved brands: accounting, development, production, marketing & sales, purchasing, and quality management. The project team leader reported to a top manager directly below AutoCompany’s CEO. Next to the senior management accountant as the formal member in both project teams, the researcher was involved in conducting analyses and participated in most of the project team meetings. Electronic copy available at: https://ssrn.com/abstract=3812939 The project teams’ results were presented and discussed in several rounds and at several management levels. First, the project team presented to a management committee that could formally take decisions (the project team could only give recommendations). Almost all project team members were also part of this management committee, but not all members of this management committee were in the project team. Next, results were presented to a top management committee and, finally, to AutoCompany’s executive board. The issue of potentially going to a common platform of these two brands in Segment 3 had a history in CarCorporation. This possibility had been investigated and rejected several times before. And so, during the first meeting with some cost experts at AutoCompany, one person said: Okay, so we are doing it again. This discussion is coming back every few years. The senior management accountant early on expressed his doubts about AutoCompany adopting a common platform that would be provided by VehicleFirm. Here [in Segment 3], I am really not sure if it would be clever to go on VehicleFirm’s platform. However in Segment 5, the senior management accountant’s idea about the desired outcome of the exercise was very different. He expressed to his colleagues a clear preference for changing the status quo and going to a common platform for both brands. We have to somehow achieve to get all these … vehicles on one platform – no matter if it’s then going to be developed by [AutoCompany] or [CarEnterprise]. It makes absolutely no sense to develop two platforms for this segment. There is simply too much savings potential here. We simply cannot ignore it anymore. When later the resistance from several other project team members mounted, he said to colleagues in management accounting: If we succeed putting both cars on one platform, then we will have achieved something really good for the company. Electronic copy available at: https://ssrn.com/abstract=3812939 The forecast and formal decision for Segment 3 A key element of the calculation was a comparison of the variable costs (basically the material costs) if AutoCompany would adopt a common platform that would be developed by VehicleFirm (in Segment 3) or CarEnterprise (in Segment 5). These comparisons were made on the basis of the current car models, even though the decision at hand concerned future car models. There was simply too much uncertainty about future cars for a meaningful cost comparison. Therefore, the management accountants aimed to examine the financial impact in each segment if the current product generations would have been based on only one instead of two platforms. They started with already available material cost comparisons, which were regularly made at CarCorporation, and compared specific configurations of car models that did not necessarily provide a representative average of the material costs of the actual model configurations sold. An existing, two-year-old comparison in Segment 3 indicated that VehicleFirm’s material costs were about $600 lower than AutoCompany’s costs. The management accountants checked and updated this cost comparison to the current situation, which required an adjustment of only a few percent to $585 per car. These results are shown as the first three bars in Figure 1. All figures shown closely resemble the figures used in the company, but with modified numbers, disguised qualitative information, and words translated to English. Furthermore, transparent green rectangles with comments have been included in some figures for clarification purposes. [Insert Figure 1: Comparison of material costs per unit in Segment 3] The next step was to identify variable costs that could be avoided (or would increase) if AutoCompany adopted a common platform with VehicleFirm. The variable cost difference was split into two parts. Some technical differences between cars of the two brands could continue to freely exist in case of a common platform. These kinds of differences and associated costs would not be affected by having a common platform. This is the fourth, light-blue bar in Figure 1. Other Electronic copy available at: https://ssrn.com/abstract=3812939 technical aspects were not flexible but inherent to a particular platform, and the related costs consequences were relevant for the decision. This is the fifth, dark-blue bar in Figure 1, estimated at $401 per car. Figure 2 shows the total calculated impact if AutoCompany would adopt a common platform in Segment 3 that would be provided by VehicleFirm. The first bar shows an impact on variable costs of $900 million, based on the rounded number of $400 per car (also indicated in Figure 1) and a total number of 2.25 million cars. The second bar mentions scale effects with a question mark, suggesting it would be a small, negative impact, but without quantifying this. The third bar mentions “fixed costs” which refers to investments in product development and production assets. A common platform would require fewer such investments, saving $400 million. Finally, negative “profit effects” of $1000 million are shown, referring to contribution margins that could not be realized anymore if a car would be based on a common platform. Figure 2 indicates a question mark for the total effect (“group impact”), suggesting that it might be slightly positive, but not that clear and perhaps not worthwhile. [Insert Figure 2: Cash flow forecast for AutoCompany in Segment 3] The senior management accountant who presented the calculation to the top management committee had the impression was that people were relieved that the cash flow forecast suggested to not change the status quo, which was also what the other members of the project team had recommended. As the project leader stated: finally, there is a reason why we do the things how we do them. The formal decision to maintain separate platforms in Segment 3 was taken by the executive board of CarCorporation. Before the meeting in which this was decided, a top manager in finance at CarCorporation contacted the project team. The official document for that meeting only included the summary calculation, similar to Figure 2, and he wanted details of the calculation to be available Electronic copy available at: https://ssrn.com/abstract=3812939 for the meeting of the executive board of CarCorporation as back-up material. The project team leader was also going to be present at that meeting and asked the management accountants for further details that would enable him, if needed, to explain the variable cost difference in more detail. After the meeting, the same top manager in finance contacted the management accountants and requested more detailed information. In several rounds via email and telephone, they provided additional details and explanations. CarCorporation seemed to ask this information to make sure that AutoCompany had documented in detail why they had recommended to not change the status quo and that AutoCompany would be able to answer potential future questions about the analysis. The forecast and formal decision for Segment 5 The calculation for Segment 5 is shown in Figures 3, 4 and 5. A two-year old comparison of the material cost per unit was updated, thereby reducing the cost difference by about one third (from $4536 to $2986 per unit), see Figure 2. The management accountants argued that this was due to technical changes since the original material cost comparison was made. The next step was to identify variable costs that could be avoided (or would increase) if AutoCompany adopted a common platform with CarEntreprise. Again, some technical differences between cars of the two brands could continue to freely exist in case of a common platform, so these costs were classified as irrelevant for the comparison. The amounted to $2619 per car, as shown in Figure 3. Other technical aspects were not flexible and the related costs differences were inherent to the common platform. For example, AutoCompany would have to adopt particular more expensive parts that inherently belonged to CarEnterprise’s platform. These costs are shown as the final, dark-blue bar in Figure 3, estimated at $367 per car. [Insert Figure 3: Comparison of material costs per unit in Segment 5] Figure 4 shows the estimated financial impact of the scenario that AutoCompany would give up its own platform and adopt the platform of CarEnterprise as the common platform. This looked Electronic copy available at: https://ssrn.com/abstract=3812939 very favorable: variable costs increases were limited (a cost increase of $350 per car (the number of Figure 3, rounded) × 314,000 cars = $110 million), scale effects led to significant cost savings ($400 million), lower investments were needed ($600 million), and even positive effects for contribution margins were feasible ($100 million). The forecast suggested that significant financial benefits could be achieved if AutoCompany would adopt CarEnterprise’s platform. The senior management accountant explicitly suggested in Figure 4 that this result was fairly robust, because changing it by 20% did not matter for the conclusion. If somebody is going to grumble about one small element of our evaluation, we can prevent distraction from the whole thing by saying: We know our assumptions are rough, but even if we are 20% wrong, there is still so much money to be saved. [Insert Figure 4: Cash flow forecast for AutoCompany in Segment 5] Figure 5 shows the financial impact if, the other way around, CarEnterprise would adopt AutoCompany’s platform. This was estimated to be neither positive nor negative—not particularly attractive. Considerable savings in variable cost, scale effects, and investments would all be eradicated by a significant loss of contribution margin of $1000 million. [Insert Figure 5: Cash flow forecast for CarEnterprise in Segment 5] Besides these calculations, other project team members emphasized various other considerations that were not included in the financial analysis, such as qualitative statements about specific technical disadvantages and brand image effects. The project team leader tried to convince the senior management accountant to change, or at least moderate their recommendation to cancel AutoCompany’s platform in Segment 5. Isn’t there a way we can formulate it somehow differently or in a softer way? The senior management accountant did not agree, and later formulated on the slides from an [AutoCompany] perspective … maintaining the status quo is financially not conceivable. Electronic copy available at: https://ssrn.com/abstract=3812939 The project team did not come to a common recommendation. As the researcher on site observed: [The senior management accountant] remained steadfast, which, in the end, makes that the other areas attach a green check to many scenarios, but then there’s a red check in the finance column. On the other hand, the scenarios where we put a green check are often with a red check from the other areas. CarEnterprise’s senior management accountant was frustrated with this outcome and mentioned that from my perspective, this is a clear failure of our assignment. After presenting the project team results, neither the management committee nor the top management committee made a formal decision. However, they decided to start the new modular architecture development anyway, and initially not to consider requirements for Segment 5 vehicles for AutoCompany. A few months later, the CEO of AutoCompany mentioned that he now recognized the necessity of a common platform in Segment 5 and a few weeks later, the CEO of CarCorporation stated in a meeting that future Segment 5 vehicles of both brands should be based on a common platform. A few months later, it was officially decided that this common platform would be developed under the responsibility of CarEnterprise. Episode 2: An integrated or split architecture for battery electric vehicles? The starting point This episode concerned a decision about the technical concept for future battery electric vehicles (BEVs). The fundamental decision was whether to have integrated or separate modular architectures for conventional cars and BEVs. This episode lasted around 13 months. The project team had a comparable composition as in the first episode, and the structure of how this team reported to management was also similar. The senior management accountant was another person than in the first episode. Again, one of the researchers was informally part of the project team. In the beginning of this episode, the project team leader and the representatives from Electronic copy available at: https://ssrn.com/abstract=3812939 engineering, marketing, and production expressed a clear preference for an integrated architecture. They mentioned flexibility as the main reason: AutoCompany would be able to manufacture both conventional cars and BEVs on the same production lines and could easily react to changes in the actual sales mix of conventional cars and BEVs. With two separate architectures, they claimed separate production facilities would be needed for both types of cars, which would require much larger investments. Furthermore, they claimed that product development investments would be much higher for developing separate architectures. They also acknowledged that the integrated architecture would cause some disadvantages for conventional cars, such as a higher weight. None of these qualitative arguments was quantified, but the decision taken by the top management committee at that time was that the option of the separate architectures was not expedient. The management accountants started trying to quantify some of the arguments, especially the difference in investments for product development and production facilities, which was supposed to be an advantage of the integrated design, and the difference in variable costs, which was expected to be the main financial disadvantage of the integrated architecture. As the senior management accountant said I am really tired of being the only one who talks sometimes against [an integrated design]. It is completely obvious that all the other departments already have made a decision. He wanted to achieve that the disadvantages of the integrated design, as well as the advantages of the split design with two separate modular architectures got much more attention. The forecast and formal decision After about seven months, a cash flow forecast was presented to the top management committee and the executive board, which concerned cars in one particular size segment. The slide shown in Figure 6 summarized the management accountants’ analysis by comparing four scenarios in terms Electronic copy available at: https://ssrn.com/abstract=3812939 of contribution margin, fixed costs (i.e., investments in product development and production facilities), and resulting net earnings. Scenario 2 concerned a split architecture, Scenario 3 concerned an integrated architecture, whereby the a and b scenarios were based on different sales forecasts. Figure 6 suggested that the split architecture had higher fixed costs (higher red bars for 2a compared to 3a and for 2b compared to 3b) but also higher contribution margins (higher green bars for 2a compared to 3a and for 2b compared to 3b). Overall, the net earnings did not differ under the lower sales forecast (comparing 2b and 3b); under the higher sales forecast, the net earnings were only one billion higher for the integrated architecture (3a compared to 2a). The presentation contained many more slides with more detailed calculations for the various numbers, and the management accountant provided further explanations during the presentation. [Insert Figure 6: Forecast of cash flow differences between four scenarios] The contribution margins were influenced by the variable costs per unit, which included a quantification of specific technical differences between the integrated and separate product architectures. The integrated architecture necessitated cars to be a bit higher and heavier, which increased production costs and CO2 emissions, and also required using wheels with a larger diameter. As indicated in Figure 6, the variable cost per vehicle was around $1000 higher for the integrated architectures (3a and 3b). The difference of around $1000 per unit between the integrated and separate architectures played a central role in the discussions of the executive board. When preparing the board meeting, a top manager commented on the financial effects: This [investment difference] is not that interesting. But the thing about the variable costs, this is the thing that is really exciting. During the board meeting, the CEO responded But guys, I am extremely struggling with a burden of thousand [dollar] for each combustion car. So, this [integrated architecture] is obviously not it. Electronic copy available at: https://ssrn.com/abstract=3812939 The meeting minutes mentioned that there is a total burden of [$ 1000] per unit for [combustion cars] in the [integrated] approach. This is evaluated as very critical by the executive board, especially against the background of the tense profit situation in the … segment. Against this background, the examination of various alternative split-scenarios that are using existing/planned platforms … is demanded. In the next months, the accountants made similar analyses for related size segments. Other management accountants made analyses for two SUV segments. The researchers were not involved in this, but could observe from the slides that the calculations were done similarly, with some aspects being more detailed. In particular, variable cost increases because of an integrated architecture were examined at a much more granular level. The result of approximately $1000 additional variable costs per unit remained quite stable, though, and after two additional executive board meetings, AutoCompany’s executive board made the final decision to develop separated architectures for BEVs and combustion-engine cars in all segments. 5. Analysis and discussion Motivated reasoning is driven by directional preferences, requires normative ambiguity, and is constrained by the need to appear rational and provide a justification for the decision (Kunda 1990; Boiney et al. 1997; Kadous et al. 2003, 2008). We will develop these notions further in the context of accountants who are creating cash flow forecasts for capital budgeting decisions, thereby providing a more nuanced insight of how motivated reasoning “works” is this accounting context. Table 3 provides an overview of the discussion. Finally, we will address a potential cause of the accountants’ directional preferences. [Insert TABLE 3] Exploiting normative ambiguity Normative ambiguity in the context of this study was caused by uncertainty about the future impact of capital budgeting decision alternatives. Normative ambiguity is a requirement but, as such, we Electronic copy available at: https://ssrn.com/abstract=3812939 propose it is not enough to affect the forecast through motivated reasoning. This requires that accountants actively make use of normative ambiguity to purposefully bias the forecast in a direction that makes it more in line with their directional preferences. So, besides directional preferences and normative ambiguity, there needs to be flexibility that makes motivated reasoning actually feasible. We suggest the possibility to influence the forecast as the mechanism between normative ambiguity and the occurrence of motivated reasoning. Specifically, we suggest four ways in which accountants can translate normative ambiguity into degrees of freedom for influencing the forecast and changing the outcome: claiming number inputs, choosing method details, determining the forecast scope, and counteracting information providers. These ways make use of a grey zone of uncertainty. Extreme ways for constructing the forecast will be considered as too unrealistic, speculative, or unusual, but there is also a broad zone of reasonable ways for constructing a forecast about an unknown future. In this grey zone, nobody really knows and one way for constructing the forecast (by making a particular assumption, for example) is as believable—and uncertain—as another way. Management accountants could put forward assumptions about numerical input values, ways for implementing the forecasting method, and the scope of the forecast, which support getting to a forecast that is consistent with their preferred conclusion. Their actions would be in the grey zone to make them acceptable enough to most other stakeholders, and they would not be biased more than needed for achieving the preferred conclusion (Boiney et al. 1997). Accountants could also readily accept inputs and assumptions from others that are in the grey zone and fit their agenda. By making their actions explicit, the accountants can make the first move and reverse the burden of proof. In the grey zone, not much evidence is needed if you are the first stating an assumption that is “reasonable” (as well as arbitrary). Others who question that assumption would be expected to provide much more evidence for an alternative assumption that would overturn the initial assumption. Stating an assumption is Electronic copy available at: https://ssrn.com/abstract=3812939 like staking your claim. Management accountants can make assumptions and take other actions that suit them, but they can only act strategically within boundaries (Goretzki et al. 2018). We propose that, in an organizational context, the grey zone provides a more nuanced idea of normative ambiguity for motivated reasoning, The grey zone matters not so much for individual reasoning but plays a role in social processes in which the forecast is discussed, questioned, defended, and has more or less influence (Rowe et al. 2012). Making use of the various ways for influencing the forecast can be done silently and defended if detected and questioned, but it can also be done prominently, to draw attention. Providing explicit information about the forecast may have the effect of focusing the burden of proof. Explicating specific ways of how a forecast has been constructed draws attention to particular numerical inputs, method choices, or scope considerations, suggesting that these are the relevant aspects to talk about. It makes those aspects more salient and puts those up for discussion and challenge. Furthermore, remaining silent on other ways of how the forecast has been constructed makes those aspects more difficult to be challenged. Others will have more difficulties to understand the forecast, to identify issues that are relevant for them to address, and to discuss and challenge those issues. And so, the management accountants can cleverly influence which aspects of their forecasts they would prefer to be “on the stand” and which they would like to keep silent. Claiming number inputs Claiming number inputs refers to the accountant taking the lead in stating particular numerical values as inputs for the forecast. These claimed values support getting to an outcome that is more in the direction the accountant prefers. Episode 1 of the field study showed that the senior management accountant’s directional preference was to maintain the status quo of AutoCompany’s own platform in Segment 3. Yet in Segment 5, he preferred changing to a common platform and did not mind if this would be Electronic copy available at: https://ssrn.com/abstract=3812939 CarEnterprise’s platform. Reversely, CarEnterprise’s senior management accountant did not want to give up their platform and agreed with AutoCompany adopting their platform. It is especially interesting to see how the calculation in Segment 5 was constructed, because the senior management accountant’s preference for what should be done would require a drastic change from the current status quo and his preference was quite different from what other team members wanted. We will now analyze how the calculation had been carefully constructed to get to a result that supported the preferred conclusion of AutoCompany’s senior management accountant. Our examples concern differing assumptions made for the forecasts in Segments 3 and 5 about sales substitution effects (Example #1), feature sales (Example #2), and scale effects (Example #3). Example 1: Assumptions about sales substitution effects. AutoCompany offered a strong, large powertrain in Segment 3 that it could not sell anymore in case VehicleFirm’s platform would become the common platform. The powertrain would not fit into VehicleFirm’s platform, because of geometrical limitations that were fundamental to the modular architecture, and even significant additional development investments would not be able to solve this problem. Although it was unclear how important that powertrain would be for the future product generation, AutoCompany’s senior management accountant decided to evaluate this issue on the basis of the current product generation. The management accountants gathered pricing data, cost data, and installation rate data about the various powertrains. They assumed that all customers who had purchased the strongest powertrain would buy the next strongest powertrain instead, and they estimated the contribution margin losses accordingly. In other words, they assumed no sales volume losses (100% substitution rate) and estimated the lost contribution margin to be $1000 million, shown in Figure 2. In Segment 5, a similar powertrain issue played a role. CarEnterprise would not be able to offer a strong powertrain anymore, if it would adopt AutoCompany’s platform. This time, significantly less than 100% substitution was assumed. Instead, it was assumed that many Electronic copy available at: https://ssrn.com/abstract=3812939 CarEnterprise customers would not purchase a vehicle at all if this powertrain would not be available. Figure 5 (“Canceling strong powertrains”) indicated a contribution margin loss of $750 million. Although this absolute number was less than in the other segment, it was much more relative to the total contribution margin, because sales in Segment 5 were less than in Segment 3. CarEnterprise’s senior management accountant had inserted the bar with this number of $750 million contribution margin loss in an exhibit in a PowerPoint presentation and explained in the accompanying email to AutoCompany’s senior management accountant only that without offering this [powertrain], we expect a significant loss of sales volume. In other words, less than 100% substitution was assumed, but without providing any further explanation. Despite the significant impact of this assumption, the lack of details and support, and the inconsistency with Segment 3, details were neither provided by CarEnterprise’s senior management accountant, nor required by AutoCompany’s senior management accountant, who readily agreed to adjusting the calculation, writing Thank you for the input, we will include the information accordingly. It goes rather exactly to (almost) zero … When he later presented Figure 5 to the management committee, he mentioned only briefly the different assumptions about substitution. His readily acceptance of this number was also surprising for another reason: it would probably be avoidable. The management accountant could reasonably have assumed that it would be technically possible to develop a platform in Segment 5 in such a way that including the large powertrain would still be possible. This would require significant extra product development investments, but these would still be much less than the very large contribution margin loss. Similar assumptions had been made about other technical features (e.g., the transmissions in Segment 5). However, such an assumption was not made this time. The assumption about substitution effects Electronic copy available at: https://ssrn.com/abstract=3812939 favored the outcome of CarEnterprise not adopting AutoCompany’s platform. Notice also that motivated reasoning was limited to what was needed (Boiney et al. 1997). The assumption of 100% substitution in Segment 3 still provided sufficient contribution margin losses, whereas less than 100% substitution was needed in Segment 5 to show the required amount of contribution margin losses. Example 2: Assumptions about feature sales. Another example of diverse assumptions for both brands in Segment 5 concerned future sales of the four-wheel drive feature. Currently, CarEnterprise offered this feature as an expensive option that the vast majority of customers ordered, but AutoCompany included this as a standard feature. In case CarEnterprise would provide the common platform, AutoCompany would also offer the feature as a paid option. The senior management accountant (not the marketing representative) estimated a positive impact of $100 million, as shown in Figure 4, arguing that this would be comparable to the business practices of all competitors. The senior management accountant used data about current prices, costs and adoption rates from CarEnterprise to estimate a business case. The parameters for the calculation of this number were also provided in Figure 4, which allowed verifying that the different parts were internally consistent: 314,000 units × 65% × (0.5 × $2000 – $500) = $102,050,000. In case AutoCompany would provide the common platform, CarEnterprise would include the feature standard on all cars. CarEnterprise assumed a contribution margin loss of $250 million, shown in Figure 5 (“Cancelling option X”), but did not provide further explanation or details. These assumptions again supported the conclusion that it would be favorable for the group if AutoCompany adopted CarEnterprise’s platform and unfavorable if CarEnterprise adopted AutoCompany’s platform. While the business case for AutoCompany and the estimation of revenue losses for CarEnterprise might be plausible on their own, the combination of both contradictory assumptions Electronic copy available at: https://ssrn.com/abstract=3812939 is remarkable. It could reasonably have been assumed that a technical solution would be possible enabling CarEnterprise to still offer this feature in the same way as it currently did, so as a paid option, even with AutoCompany’s platform as the common platform. However, this alternative assumption would have made the outcomes less in line with the accountants’ preferences. Claiming number inputs may also happen by stating the assumption that particular inputs are too uncertain and should be ignored, thereby implicitly assuming particular numbers to be zero, however. So instead of not considering an effect (because it would be too uncertain), a numerical input (of zero) is actually assumed, which does affect the outcome in a particular direction. The next example demonstrates this. Example 3: Quantifying scale effects. Another interesting difference is the quantification of scale effects. Figure 2 indicates scale effects as having a small, negative impact, but without quantifying this—Figure 2 shows a question mark. Figures 4 and 5 shows a financial impact of scale effects of $400 million. The management accountants based these numbers on a one-year-old analysis, which had another purpose, but it also quantified scale effects. It showed a cost impact of between 3% and 12% on material costs and these estimates had previously been accepted by CarCorporation’s top management. The management accountants applied these percentages to the platforms’ current material costs, which resulted in a positive scale effect of $400 million, regardless which platform would be cancelled. This assumption favored the outcome of AutoCompany adopting CarEnterprises’s platform in Figure 4. It also favored that CarEnterprise would adopting AutoCompany’s platform (which is not what the senior management accountant wanted), but this effect could be sufficiently countered in total in Figure 5. Choosing method details Choosing method details refers to making particular detailed choices for how the forecast is being constructed. Within the implementation of an overall approach that, as such, may be pretty Electronic copy available at: https://ssrn.com/abstract=3812939 “neutral,” the accountant makes particular choices for implementing the approach such a way, that the outcome is more in line with the decision preference. Several examples in the case study demonstrate how the accountants made subtle choices regarding the method for creating a forecast that strengthened getting to a result that supported their preferred conclusion. We will discuss the following examples below in more detail: Example #4 concerns the classification of the costs of axles as either a relevant cost difference (in Segment 3) or as an irrelevant cost different (in Segment 5). Example #5 concerned the classification of the transmission in Segment 5 as initially a relevant cost difference, but subsequently as an irrelevant cost difference. Example 4: Classifying the axles. Major variable cost differences resulted from the fact that AutoCompany used more expensive axles than VehicleFirm in Segment 3 and CarEnterprise used more expensive axles than AutoCompany in Segment 5. In Segment 3, the cost experts wanted to classify the axles as a technical difference that was inherent to the platform, making the cost difference relevant for the decision. Axles are shown in the dark-blue bar in Figure 1. The senior management accountant did not question this classification. He suggested it was not his competence or job to define which kind of axle AutoCompany or VehicleFirm would need, saying Who am I that I could answer all these questions today? As controllers, we cannot. In Segment 5, however, the senior management accountant took a very different position and pushed back in similar discussions. He argued that it was absolutely not understandable for him why axles were inherent to the current platforms. I wouldn’t know why [CarEnterprise] could not use our axles tomorrow. He assumed that AutoCompany could still use the less expensive axles if both brands were going to adopt CarEnterprise’s platform, and likewise, CarEnterprise could still use its own axles if Electronic copy available at: https://ssrn.com/abstract=3812939 AutoCompany’s platform would be the common one. So this time, axles were in the light-blue bar in Figure 3 and did not impact the cost comparison. Example 5: Classification of transmission. The material cost difference calculation shown for Segment 5 in Figure 3 was not the first version. An earlier version differed regarding the classification of many costs as relevant or irrelevant for the comparison. This earlier version looked like Figure 7 and showed a relevant cost difference of $1996 per car, because many more parts were classified as inherent to the platform. This version of the calculation suggested that each unit would become about $1996 more expensive if AutoCompany would adopt CarEnterprise’s platform; and the other way around, CarEnterprise would be able to save this amount per unit. This result triggered further discussions with CarEnterprise’s senior management accountant, who strongly opposed these numbers. I disagree with the diagram ‘Material cost delta from [AutoCompany’s platform] to [CarEnterprise’s platform]’. I cannot confirm the platform-determined [material cost] delta like this. … Please adjust the diagram accordingly. Thank you. [Insert Figure 7: Earlier version of the comparison of material costs per unit in Segment 5] In the following weeks, the management accountants looked into technical differences in more detail. The separation between two kinds of variable cost differences (the avoidable and unavoidable variable cost differences) was sometimes difficult to make, and the management accountants often ended up having very detailed technical discussions with cost experts and making assumptions that were defensible, but which could also have been made differently. CarEnterprise’s expensive transmission was one reason for the initially large cost differences. The further discussions led to the assumption that the future platform would be able to accommodate several kinds of transmissions and AutoCompany would not be forced to also adopt the expensive transmission if it were to have a common platform with CarEnterprise. Reversely, Electronic copy available at: https://ssrn.com/abstract=3812939 CarEnterprise would not achieve cost savings with the transmission if it were to have AutoCompany’s platform in common. As a result, the cost difference for the different transmissions moved from the dark-blue to the light-blue area of irrelevant costs. The relevant difference in material cost per unit was reduced. For several other parts, assumptions were also changed as to whether costs should be considered as relevant or irrelevant for the decision. In the end, the management accountants were able to reduce the relevant cost difference to $367 (as shown in Figure 3). With this smaller relevant cost difference, it became more attractive for AutoCompany to adopt CarEnterprise’s platform, and less attractive the other way around. The assumptions in Examples #1 through #5 helped to get to the outcomes shown in Figures 2, 4 and 5, which made two conclusions inevitable: In Segment 3, AutoCompany should not adopt a common platform with VehicleFirm, but in Segment 5, AutoCompany should cancel its own platform and adopt CarEnterprise’s architecture. In other words, these assumptions influenced the forecasts in such a way, that these supported the management accountant’s preferred conclusions. Determining forecast scope Determining forecast scope refers to the formulation of alternatives and criteria, thereby establishing what is considered and quantified, and what is not considered and quantified within the scope of the cash flow forecast. Determining forecast scope is another way in which accountants can influence forecast outcomes and make these more consistent with their directional preferences. Episode 2 of the field study showed that the management accountant’s preference was to make sure that the disadvantages of the integrated design and the advantages of the split design received much more attention. The forecast presented to the management committees and the executive board supported this preferred conclusion. That was no coincidence and we will describe several examples of motivated reasoning in this episode. Electronic copy available at: https://ssrn.com/abstract=3812939 Example 6: Quantifying the cost impact of technical differences. The accountants insisted on quantifying the higher unit cost implications of technical characteristics of the integrated design. As mentioned earlier, the integrated architecture necessitated cars to be a bit higher and heavier, which increased production costs and CO2 emissions and also required using wheels with a larger diameter. The management accountants had collected data on these technical differences and insisted they would estimate the cost impact of these differences. When presenting the data on technical differences in the project team, the project leader expressed his doubts: That is too much for me – surely, only around 15 kilograms will remain. The management accountants protested and refused to change the numbers, arguing there was no need for speculation. They insisted on accepting these estimates, arguing these been provided by the engineers. In the following weeks, the management accountants quantified the technical differences together with cost experts, and also presented and discussed these with the project team several times. They relied on cost information of the current car models, which indicated a material cost of around $4 per kg of weight of a car body, which they multiplied with the additional weight per car. They valued CO2 emissions with $95 per gram, which was the fine car companies would have to pay per gram from 2021 in the EU if they failed their emission goals. They considered the additional complexity costs by multiplying material costs by 3%, which was a reasonable but also quite arbitrary assumption about higher material costs due to greater complexity of the integrated architecture. As a concept engineer said: Somehow, they will be there. I just cannot tell you in detail today which parts will be affected. In total, the additional material costs of combustion cars for the integrated design was estimated at roughly $ 800–1000 per car. Electronic copy available at: https://ssrn.com/abstract=3812939 Not everyone was convinced and the sales manager in the project team stated, after the meeting with the executive board, those $1000, I still do not believe them to this day. He had not been able to contest this result, however. The accountants had explained that their calculation of additional costs per unit included the assumption of a 3% cost increase because of additional complexity. If someone challenged that assumption and would like to change it to 2%, for example, then both assumptions would be in the grey zone and be equally reasonable and uncertain. Why would 2% be any better? Why would we change it? More generally, when management accountants presented their forecasts (to the project team, management committee, top management committee, and the executive board) they made the point that if others would have better information, they would be like to hear it. But it was also clear that “better” would need to be supported with strong evidence. Without that, their initial assumptions remained. Counteracting information providers Finally, counteracting information providers refers to the accountant’s response to the information that is provided by other stakeholders. Depending on whether that information supports an outcome that is in line with the accountant’s directional preferences, they may either to accept or fight the input information. In the case company, the management accountants’ responses to the information they received was consistent with how that information favorably or unfavorably influenced the cash flow forecasts. As we saw above in Example #6, the management accountants had received information about technical differences between cars as the basis for a calculation of variable cost differences. When other actors raised doubts, the management accountants immediately defended this information. They needed that information for their calculation of unit cost disadvantages of the integrated architecture. Furthermore, in Example #7 below, they refused to accept investment Electronic copy available at: https://ssrn.com/abstract=3812939 estimates from the production, development and purchasing departments, arguing that these were too large and too uncertain, yet they accepted in Example #1 in Episode 1 estimates of contribution margin losses that were arguably comparably large and uncertain. Both actions were consistent with the decision preferences of the management accountants in both episodes. In Example #8 below, they could not argue against sales forecast provided by the sales representative in the project team, but then they modified the information and largely neutralized its unfavorable impact. Example 7: Rejecting investment estimates. Three departments provided the project team with their first estimates of the required investments for both concepts, which the accountants refused to accept. The estimates indicated significantly higher investments for the separate architectures. Production simply stated it would need everything double to be able to build both kinds of cars and estimated twice the investments for the separate architectures. Development and purchasing also estimated almost a double investment. At this stage, the project leader asked the senior management accountant in the project team for an exhibit to show the investment differences to top management, but he refused to provide such a exhibit. We want to provide decision alternatives to the executive board. At the moment, we have two scenarios, but one of them is definitely not an alternative. The management accountant provided only a qualitative diagram. In the following weeks, development managers came up with new and more detailed estimates for development costs, which showed smaller cost differences between the concepts. The production managers also produced new and more detailed numbers. Their conclusion still was that the separate architectures would cause higher production investments, but the extraordinarily large difference was gone. But, they insisted to also mention that they believed the company’s production site was not large enough to implement the separate architectures that required much larger production facilities. The management accountants now accepted these estimates from the Electronic copy available at: https://ssrn.com/abstract=3812939 development and production departments and incorporated these in Figure 8. The purchase manager in the project team refused to provide a new estimate. However, the management accountants produced their own estimate for purchasing investments, based on a relationship between purchasing investments and development costs in today’s car models, which they also included in Figure 8. [Insert Figure 8: Details of the forecasted investment requirements for each scenario] Example 8: Changing the comparison of sales estimates. When the management accountants could not avoid incorporating particular sales estimates provided by the sales department—these sales numbers were disadvantageous for the separate architectures that they preferred—they changed the analysis and neutralized the effect of these sales numbers. Sales volumes were another key element of the calculation. The project team had agreed on specific BEVs and combustion models as the basis for the analysis, and the management accountants asked the sales manager in the project team to provide sales estimates for these cars. The sales manager argued that only the integrated architecture would enable offering each model both as a conventional car and as a BEV. He claimed that sales volumes would be too low for doing the same with separate architectures and therefore assumed that only a few models would be offered both as a conventional car and as a BEV. He made some further assumptions about sales mix and substitution in a complex spreadsheet he sent to the management accountants, arriving at sales numbers that were around 10% lower for separate architectures. The management accountants did not like how these estimates disadvantaged the concept of separate architectures. They could not avoid using the data, but changed the comparisons by creating four scenarios. This is shown in Figure 9. Options 2a and 2b are separate architectures and Options 3a and 3b concern integrated architectures. Instead of comparing the integrated architecture including the higher sales volume (Option 3a) with the separate architectures based on Electronic copy available at: https://ssrn.com/abstract=3812939 the lower sales volume (Option 2b) as the sales manager had suggested, they created two comparisons, each time on the basis of the same sales volume. Instead of focusing on lower sales for a particular scenario, the calculation now focused on cost differences, given comparable estimates of sales volumes. [Insert Figure 9: Sales forecasts in units for each scenario] Creating justification Motivated reasoning is constrained by the need to appear rational and provide a justification for the decision (Boiney et al. 1997; Kunda 1990). Managers who need to decide whether and how to act on the management accountants’ recommendations, will likely critically review the information supporting those recommendations (Rowe et al. 2012). Anticipating such reviews, management accountants may try to enhance the justifiability of their forecasts similar to, for example, auditors or consultants who are providing justification for their recommendations that will be reviewed by other auditors and client managers (Kennedy et al. 1997; Kadous et al. 2013; Koonce et al. 1995; Agoglia et al. 2003; Kadous and Sedor 2004). Justifications provide support for an expressed viewpoint and aim to persuade the target audience that this viewpoint is valid (Shankar and Tan 2006). Justifiability may increase, if the supporting information recognizes trade-offs, includes benchmarks and other comparisons, and provides evidence of extensive efforts for searching, checking and validating information (Kadous et al. 2013; Rowe et al. 2012; Huikku and Lukka 2016; Goretzki et al. 2016). We discuss two ways for accountants to create justification for their forecasts: by showing comparisons and demonstrating scrutiny. While these activities are known to potentially increase justifiability, the intended contribution of this field study is to provide empirical evidence as to how this may happen, in particular what are various ways in which accountants may show comparisons to justify their cash flow forecasts for capital budgeting decisions. Electronic copy available at: https://ssrn.com/abstract=3812939 Showing comparisons We define the action of showing comparisons as relating the input for the forecast, the method for crating the forecast, and the outcome of the forecast to other information that is important and credible to decision makers. Comparisons enable information receivers to identify similarities and differences, to verify consistency, and to relate information to credible benchmarks and anchor points, which can help to reduce doubts about the quality of the information (Rowe et al. 2012; Kadous et al. 2013). The field study suggests four ways in which accountants can show comparisons. Between various parts of the forecast calculation Comparisons between various parts of the forecast calculation demonstrate internal consistency of the forecast. Receivers of the information can see how numbers are aggregated and disaggregated, so how numbers add up or are otherwise being combined, and how input data and several calculation steps lead to aggregate information (Englund and Gerdin 2015). Such relationships between different elements of the forecast provide receivers of the information with comparisons and consistency checks, thereby helping to justify the forecast outcomes. Example 9: Connecting numbers. Numbers connected elements of the forecast calculation and demonstrated internal consistency, within the same figure and between related figures. Within one figure, relationships between the bars were shown. For example, in Figure 1: 600 (first bar) – 15 (second bar) = 585 (third bar), etc. For many of these bars, boxes provided more detailed numbers that added up to the total number for the bar. For example, 23 + 37 + 42 + 76 + 6 = 184, the total amount for the fourth bar in Figure 1, or 340 + 260 – 200 = 400, the total amount for the third bar in Figure 2. Similarly in Figure 3, 350 × 314,000 = 110 million, the total amount for the first bar, and the assumptions for the calculation of the business case ware show in a box with the fourth bar (as described above in Example #2). Electronic copy available at: https://ssrn.com/abstract=3812939 Connections between numbers in related figures were also shown. For example, notice the remarks “calculated with 400” and “calculated with 350” in Figures 1 and 3. Figure 1 was connected to Figure 2 and Figure 3 to Figures 4 and 5 by showing that the rounded numbers of $400 and $350 for the material cost differences per unit were used consistently throughout the calculations. Furthermore, when the management accountants presented these calculations to the project team and later to top management, they pointed out that the material cost difference per car was quite similar in both segments ($400 and $350 per unit). They also mentioned that the method used was similar for both segments and made sure that the figures looked similar. To numbers and sources outside the forecast calculation Secondly, justifiability may also be enhanced by showing comparisons to numbers and sources outside the forecast that are hard, because these are important and credible to decision makers. In the context of uncertain numbers about the future, the forecast could be compared to existing actual numbers that people would not question. The forecast may also build on other estimated, future numbers that have already been validated by decision makers. Furthermore, it can be shown that particular people whose opinions matter to decision makers (for example, because of their expertise or hierarchical position) have provided particular information as inputs to the forecast. Example 10: Starting with cost numbers of current cars. The comparison of material costs per unit in Episode 1 was based on an existing material cost comparison for current cars, which was adjusted on the basis of the question “what if the current cars would have been based on a common platform?” The use of these hard data as the foundation for the forecast was explicitly shown on Figures 1 and 3 and was also mentioned during presentations of the forecasts. By showing how the calculations started from the basis of the actual material costs for current cars, information receivers were provided with a comparison to information they would already consider hard. Several other calculations were also based on hard data for existing cars that were adjusted Electronic copy available at: https://ssrn.com/abstract=3812939 to reflect changes as a result of adopting a common platform, such as the calculation of the business case for feature sales (Example #2) and the quantification of scale effects (Example #3). Showing comparisons to calculation methods in other forecasts Thirdly, justification can be strengthened by comparing the forecast calculation method to the calculation methods in other relevant forecasts that are important to decision makers. Examples could be comparisons to other forecasts that followed the same method, to legislation, technical standards, or accounting standards. In the case company, the similarity of the forecast methods for the two car segments in Episode 1 was emphasized, thereby providing comparisons between forecast methods. As mentioned in Example #9, the exhibits looked identical in terms of structure and colors, it was emphasized orally that the methods were similar, and it was pointed out that the relevant variable cost differences were in the same order of magnitude ($400 and $350). Showing comparisons to the broader context Finally, we propose that justifiability may be enhanced by making comparisons to the broader context of the forecast, based on information decision makers have from other sources (Hall 2010). For example, particular assumptions may become more persuasive by comparing these to business practices in the same organization or in similar other organizations, by connecting these to the strategy of the organization, or by showing how these are consistent with broader societal trends. Example #2 of the business case calculation showed this effect. Not only did the details provided in this example demonstrate internal consistency, but these also helped to build a comparison to hard numbers (the parameters were identical to the current situation for the other brand) and to a broader context of business practices (AutoCompany would offer this option in way that was quite common for most brands in this car segment). Demonstrating scrutiny Accountants may also create justification for their forecasts by indicating these have undergone Electronic copy available at: https://ssrn.com/abstract=3812939 scrutiny, in the form of activities such as debating, challenging, checking, correcting, and elaborating the information. Information receivers may find a forecast more convincing, if they believe it has undergone and survived scrutiny by representatives from various parts of the organization (Rowe et al. 2012; Kadous et al. 2013). Demonstrating scrutiny provides a counterbalance to the earlier actions of making explicit assumptions about number inputs, method details, and forecast scope. That was done to pretend neutrality and openness to input from others; now, demonstrating scrutiny can be used to suppress doubts and to silence other stakeholders. We suggest that accountants may try to enhance justifiability of their forecast by showing explicitly that the forecast has been extensively scrutinized. The scrutiny would be public (within the company) and documented (Rowe et al. 2012). Accountants could, for example, explain checks that have been conducted, describe sources for data, refer to earlier meetings in which the forecast has already been shown and discussed, and mention the names or positions of experts who provided estimates, approved particular assumptions, or sanctioned the forecasting method. By showing explicitly to senior management that the forecast has been scrutinized, accountants may also try to silence stakeholders who have been involved in preparing the forecast and who disagree. Disagreeing positions those stakeholders took during earlier discussions and which they have not been able to successfully defend, become difficult to propose in later discussions with senior management. The management accountants can signal that everybody has had their chances, and so what is now presented to senior management must be considered as the hardest information possible about an uncertain future. That message is not only directed at senior management to indicate that the information has been scrutinized, but the message is also intended to silence the disagreeing stakeholders. They got all the details and explanations, they had their chances to challenge the forecast, but now it is too late to fight what they could not change earlier. In the case company, when management accountants presented their forecasts, they often Electronic copy available at: https://ssrn.com/abstract=3812939 made explicit references to experts who had been involved in scrutinizing the forecast. For example, during the final presentation to the top management committee in Episode 2, Figure 8 showed that the data source was the departments of product development management, purchasing management, and production management. The accountants also mentioned that the analyses had been extensively discussed with representatives of these departments as members of the project team. They tried to signal to the top managers in the committee that their representatives in the project team had had their chances to argue the forecast, but those battles had been fought, this was the final result, and now the numbers were fixed. However, management accountants may also express implicitly that the calculation has been scrutinized. The fact that the management accountant is able to show detailed information about numbers, information sources, technical choices, etc. suggest that they must have been in contact with experts, consulted several data sources, and conducted specific analyses. The level of detail and explanation provided implies that scrutiny has been applied to be able to come up with that kind of information. We have described several instances of how management accountants in the case company explained how some very detailed costs had been included and some very specific issues had been considered, which implied that the numbers had been discussed with experts and scrutinized to be able to come up with such detailed analyses. Furthermore, management accountants can help information receivers to scrutinize the information themselves, again to create more justification for the forecast. Providing additional explanations and details without too much technical language, for example, can help making the information more understandable for information receivers who may have limited accounting knowledge. This could enable them to verify whether the overall implications of the calculation are making sense to them and are consistent with their broader experience and “gut feeling.” Providing information also helps information receivers to evaluate whether specific parts of the Electronic copy available at: https://ssrn.com/abstract=3812939 forecast are credible in relation to particular non-accounting knowledge these information receivers have (Hall 2010). A potential cause of directional preferences Motivated reasoning requires directional preferences. We will discuss the management accountant’s business partner role as a possible cause of such preferences in the context of capital budgeting decisions. We can only build on personal impressions during the field study, because we could not thoroughly investigate why the accountants in the case company had particular preferences. Thus, this part of the discussion is a tentative suggestion, an idea for future research. Management accountants have “simultaneous roles as ‘partners’ of operational management and ‘financially objective informers of the board’” (Ahrens 1997). The latter role is about safeguarding assets, enforcing rules, and reporting results. The business partner role, however, requires management accountants to cooperate with local management, to understand the business, to support decision-making, and to be involved in strategy building (Horton and Wanderley 2018; Goretzki and Messner 2019; Goretzki et al. 2013; Granlund and Lukka 1998). Tensions from role duality can strongly impact the behavior of accountants. For example, they selectively distribute information (Goretzki et al. 2018; Puyou 2018) and they sometimes engage in data misreporting (Fauré and Rouleau 2011; Maas and Matějka 2009) or budgetary slack building (Davis et al. 2006; Indjejikian and Matějka 2006) to respond to managers’ expectations for management accountants as their business partners. In the context of capital budgeting decisions, management accountants in their business partner role could also be responding to managers’ expectations. The management accountants interact with managers whose collaboration they need, not only for making the forecasts but also in many other situations (Puyou 2018), and those managers may have quite strong opinions about what should be decided. The management accountants could adopt the preferred conclusion of Electronic copy available at: https://ssrn.com/abstract=3812939 particular powerful actors. Management accountants providing their support for that conclusion may seek to build goodwill with those powerful actors and thereby to strengthen their own political position in the organization. Perhaps they believe that supporting the position of particular powerful managers in the organization could promote their own careers or more broadly increase the influence and status of the finance function. The business partner role also creates internal expectations and shapes the management accountant’s self-definition (Wolf et al. 2020; Morales and Lambert 2013). Pursuing an identity that includes understanding the business and supporting decision-making, management accountants could form a professional opinion about what they believe should be done—from a financial perspective. The management accountants draw on various kinds of information collected for the decision at hand, information concerning other, more or less similar decisions, general contextual information, and their own broader professional and personal experiences (Hall 2010; Bruns and McKinnon 1993). Their organizational identity is to be the ones who consider cost, profitability, return on investment, and such financial criteria (Lambert and Pezet 2011). Based on these criteria, perhaps they judge particular capital investment decisions as more desirable. It does not mean that the management accountants can fully quantify their conclusion—this can partly be based on qualitative arguments. Decisions on capital investment projects involve a lot of uncertainty about the future and a lot of different considerations, some of which cannot completely and meaningfully be expressed in cash flow terms. To give advice and be a business partner, accountants need to make judgments that involve other information besides cash flow forecasts. Although other groups in the organization may have different preferences, because they put more weight on other criteria, such as an exciting technology, cool design, manufacturing job preservation, sales volume, or brand image (Berhausen and Thrane 2018), the accountants consider the finances. For a specific decision at hand, identity and intuition make the management Electronic copy available at: https://ssrn.com/abstract=3812939 accountant prefer a particular capital investment project. Whatever the reason for their preferred conclusion may be, management accountants as business partners are also supposed to present themselves as independent and neutral “producers of truthful knowledge” (Lambert and Pezet 2011, 10). They need to present analyses that appear objective, evidence-based, and transparent—a forecast of the financial consequences of capital investment projects in this situation (AICPA 2017; IMA 2019). Therefore, they may adjust their official forecast to reflect their preferred conclusion. 6. Conclusion Biasing of cash flow forecast for capital budgeting proposals is an important topic in research and practice (Brüggen and Luft 2016; Turner and Guilding 2012; Haka 2006), but we know little about the role of accountants. They, too, could have a preferred conclusion, which would motivate preparing a cash flow forecast that shows their preferred capital investment project in a financially favorable light. We conducted a field study to investigate how accountants exhibit motivated reasoning when preparing cash flow forecasts for capital investment projects. This setting of accountants preparing cash flow forecasts for capital investment projects is a priori interesting, because this setting may be conducive to but also limit motivated reasoning. Motivated reasoning depends on normative ambiguity and the possibility to influence the forecast and provide justification for it. On one side, uncertainty about the future creates normative ambiguity; the preparation of undisclosed cash flow forecasts offers flexibility (compared to disclosed earnings forecast) that can be used for influencing and justifying the forecast; and forecast accuracy for a specific decision may be difficult to verify on the basis of actual outcomes for the entire organization. But, other stakeholders in the organization have information, which they provide and accountants cannot change, and which they can use to critically review the forecast. This situation makes it more difficult for accountants to influence and justify their forecast. Electronic copy available at: https://ssrn.com/abstract=3812939 Furthermore, accountants cannot use accrual-based mechanisms for influencing their cash flow forecast. So how would they do it? Our field study presented data from two episodes of decisions about the technical design of new cars and related capital investments. We show that management accountants preferred particular capital investment alternatives from the start, and at the end of each episode, the cash flow forecasts supported their preferred conclusions. We suggested that the preferred conclusions of management accountants could have arisen from their role as business partners, who are expected to understand the business, provide information for decision-making, and give recommendations to management (Fauré and Rouleau 2011; Maas and Matějka 2009; Davis et al. 2006; Indjejikian and Matějka 2006). Powerful managers and other influential organizational members may expect the management accountants to support their position and adjust the cash flow forecast accordingly. Furthermore, management accountants as business partners may have their own assessment of what—from a financial perspective—they believe should be done, and they may adjust their forecast accordingly. The goals to deal with the expectations from other organizational members and to support their own assessment could lead to motivated reasoning. As a first contribution, we develop four ways in which accountants can exploit normative ambiguity (Kadous et al. 2003) for influencing the cash flow forecast. These mechanisms provide the link between normative ambiguity and actually influencing the forecast to make it more supportive of the preferred conclusion. The mechanisms are based on claiming specific number inputs for the forecast, making specific choices for the detailed implementation of a foresting method, determining the scope of the forecast in terms of alternatives and criteria that are considered, and interacting with information providers depending on whether the information they provide is consistent with the accountants’ directional preferences. We also propose that normative ambiguity could be understood as a grey zone of uncertainty. Electronic copy available at: https://ssrn.com/abstract=3812939 Within that zone, alternative assumptions and choices for preparing the forecast are equally plausible (or implausible) and there is little convincing proof for one assumption above another assumption. The boundary of the grey zone is when assumptions and choices become so extreme that too many other stakeholders would consider these as simply unrealistic. Accountants can make assumptions and choices within the grey zone of uncertainty that support getting to a cash flow forecast that supports their preferred alternative. Doing so may help to reverse and to focus the burden of proof. In other words, accountants could explicitly stake their claims (which support their preferred conclusion), hint at issues to review (and deflect from issues they would rather not be discussed), and invite “better ideas” (but then also require proof as to why these ideas would be better). After having been so “neutral and open,” they can make it clear that “everyone has had their chances” (so their forecast is now final). We provide empirical evidence from the field study. These insights complement earlier research on motivated reasoning in accounting, which has focused on other accounting settings and has primarily investigated factors that stimulate motivated reasoning (Kadous et al. 2003, 2008; Koch and Salterio 2017; Kadous et al. 2013; Austin et al. 2020). However, far less is known about phenomenon of motivated reasoning in accounting itself. This research also to adds to the understanding of informational tactics of management accountants, which mostly been investigated in the context of preparing and discussing ex-post information for performance evaluation (Goretzki et al. 2018, 2016). Furthermore, this study adds to our understanding of the broader topic of budgeting for planning and resource allocation, which is important to study specifically, besides performance evaluation (Becker et al. 2016). As a second contribution, we develop a more specific understanding of how accountants can create justification for their cash flow forecast. Prior research has mostly investigated justification in the contexts of auditing tasks (Trotman et al. 2015; Shankar and Tan 2006; Kennedy et al. 1997; Agoglia et al. 2003; Kadous et al. 2013) and somewhat for in management accounting settings Electronic copy available at: https://ssrn.com/abstract=3812939 (Rowe et al. 2012; Kadous et al. 2005; Loraas 2009; Huikku and Lukka 2016). Building on this, we develop specific ways for accountants to create justification for their cash flow forecast by showing comparisons. That is, by relating the forecast input, method, or outcome to other information that is important and credible to decision makers. Management accountants can demonstrate internal consistency of different parts of the forecast, connections to numbers or sources that information receivers already find persuasive, links of the particular forecast to other forecasts and forecasting methods that matter to information receivers, and by connecting the forecast to a broader context. We provide empirical evidence from the field study. A limitation of this study is that, despite fantastic access in the case company, there is always more data to wish for. For an even better understanding of the forecasts in these two episodes, we would have liked to witness more of the discussions that were taking place in the top management committee and the executive board. And, the story goes on. Further forecasts are being constructed and discussed, decisions are being made, which have not been included but could have further informed our understanding. Another limitation concerns our tentative explanation for the cause of the accountants’ preferred conclusion. The explanation could only be based on impressions from the case company, but we lack similar kind of detailed empirical evidence that was provided as support for the theoretical framework presented in Table 3. Various explanations for the directional preferences and motivated reasoning in the case company are possible and we do not make empirical claims here. Future research could look at questions such as: How do management accountants form their decision preferences? How are these preferences related to their professional experience, incentives, self-interest, or psychological mechanisms? Furthermore, if accountants deliberately craft forecasts to back up their preferences and skillfully enhance the justifiability of these forecasts, which is what our study suggests, how do Electronic copy available at: https://ssrn.com/abstract=3812939 information receivers react in the longer run? Management accountants need to be seen as competent and truthful professionals to be effective in their role (Goretzki et al. 2018). If other stakeholders would start to suspect that accountants have decision preferences that influence the information and justification they provide, this could hurt their image as producers of truthful knowledge (Goretzki et al. 2018). Especially the accountants’ discretion for providing information may increase suspicion of persuasion attempts (Friestad and Wright 1994). Using persuasion tactics may “backfire” if this increases information receivers’ skepticism and reduces how much they accept the information that is provided (Bhattacharjee and Brown 2018; Nelson and Rupar 2015). However, if the information provided actually helps managers making decisions, the credibility of the information may be enhanced, even if managers would believe management accountants are also using persuasion tactics (Isaac and Grayson 2016). Finally, future research could address motivated reasoning and the construction of forecasts when accountants have less discretion, because the similar accounting information is regularly produced. Information receivers would expect particular information and the management accountants would not have much flexibility to purposely influence the information they provide and to increase the persuasiveness of it. How would they bias and justify the information in such situations? 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Electronic copy available at: https://ssrn.com/abstract=3812939 Figure 1 Comparison of material costs per unit in Segment 3 Axles $110 Values in $ Per Vehicle Topic 2 $105 Topic 3 $47 600 Topic 4 $33 15 Topic 5 $15 Others $91 Topic 1 $15 Calculated with 400 Topic 1 $23 Topic 2 $37 Topic 3 $42 Topic 4 $76 Others $6 Existing Update Current Material Brand-individual Conceptual Material Cost Cost Delta Requirements / Differences Comparison Lightweight Constructions Only this number is a relevant cost difference that Note: Transparent green rectangles with comments were not part of the is used in the next steps for original exhibits in the case company, but these have been included in Segment 3. some of the figures in the paper for clarification purposes. Figure 2 Cash flow forecast for AutoCompany in Segment 3 (AutoCompany canceling its own platform and adopting the platform of VehicleFirm as the common platform in Segment 3) Group Perspective Values in $Million Result from Figure 1 Variable Cost Saving $400 Sales Volume 2.25 Mio. Loss of Strong Powertrains Assumptions: 1. Sales Volume Unchanged 2. 100% Substitution with Hard to quantify because Initial Fixed Costs Strongest Remaining Powertrain both platforms already large Savings 340 volume (but tending to be Updating Costs Savings negative, because the AutoCompany car model 260 1,000 has a much larger volume Additional Fixed Costs share of its platform than for VehicleFirm Platform theVehicleFirm car model of its respective platform) Variable Costs Scale Effects Fixed Costs Profit Effects Group Impact The cash flow forecast suggests that the total impact for the group is not that clear but might be only slightly positive in Segment 3. Electronic copy available at: https://ssrn.com/abstract=3812939 Figure 3 Comparison of material costs per unit in Segment 5 Values in $ Per Vehicle 4,536 Topic 1 $1467 Topic 5 $150 Axles $250 Topic 6 $125 Topic 3 $165 Topic 7 $70 1,550 Topic 4 $113 Topic 8 $279 2,986 Topic 1 $250 Reason 1 $150 Reason 2 $100 2,619 Others $367 Topic 2 $310 Topic 3 $990 Calculated with 350 Existing Update Current Material Brand-individual Conceptual Material Cost Cost Delta Requirements / Differences Comparison Lightweight Constructions Only this number is a relevant cost difference that is used in the next steps for Segment 5. Figure 4 Cash flow forecast for AutoCompany in Segment 5 (AutoCompany canceling its own platform and adopting the platform of CarEnterprise as the common platform in Segment 5) 1.188 Values in $Million Group Perspective Additional Option X for AutoCompany Premises: 1.Installation Rate: 65% 4% Cost Saving Rate (Negative scale effect for 2.Pricing: $2.000 remaining architecture 990 3.Costs: $500 neglectable) 4.Revenue Factor 0.5 Result from Figure 3 5.Sales Volume 314.000 Additional Variable Costs $350 Initial Fixed Costs Savings 670 Sales Volume 314.000 Updating Costs Savings 80 Additional Fixed Costs for CarEnterprise Platform −150 -110 Variable Costs Scale Effects Fixed Costs Profit Effects Group Impact +/− 20 % The cash flow forecast suggests that the total impact for the group is very positive and robust. Electronic copy available at: https://ssrn.com/abstract=3812939 Figure 5 Cash flow forecast for CarEnterprise in Segment 5 (CarEnterprise canceling its own platform and adopting the platform of AutoCompany as the common platform in Segment 5) Group Perspective Values in $Million Cancelling Option X −250 Cancelling Strong Powertrains −750 4% Cost Saving Rate 1,000 Additional Variable Costs $350 Sales Volume 285.000 Variable Costs Scale Effects Fixed Costs Profit Effects Group Impact The cash flow forecast suggests that the total impact for the group is zero. Electronic copy available at: https://ssrn.com/abstract=3812939 Figure 6 Forecast of cash flow differences between four scenarios All Car Models as Electric & Combustion Version Several Car Models Only as Electric Version 2.0 2.0 -10% -10% Sales Volume (in Million Vehicles) 1.8 1.8 Variable Cost Burden for Combustion CO2 Compensation Cars in Option 3 Approx. $1.000 / Vehicle Contribution Margin Fixed Costs Net Earnings (in $Billion) 4.5 6.4 8.5 7.6 5.1 5.4 4.2 4.4 3.1 2.1 1.4 1.3 1.6 1.6 Scenario Scenario Scenario Scenario 2a 3a 3b 2b Separate architecture 2b and Separate architecture 2a estimated to yield lower net integrated architecture 3b estimated to yield similar earnings earnings than integrated architecture 3a Notes: Scenarios 2a and 2b concerned separate product architectures for electric and internal combustion vehicles, Scenarios 3a and 3b concerned an integrated architecture. These scenarios were compared on the basis of two different sales forecasts, 2a and 3a using the higher sales forecasts, 2b an 3b using the lower sales forecasts. Electronic copy available at: https://ssrn.com/abstract=3812939 Figure 7 Earlier version of the comparison of material costs per unit in Segment 5 Values in $ Per Vehicle 4,536 Topic 1 $1391 1,550 Topic 2 $50 Topic 3 $150 2,986 Others $405 Topic 1 $250 Reason 1 $150 1,996 Reason 2 $100 Topic 2 $310 Axles $230 Topic 3 $990 Topic 2 $250 Topic 3 $170 Topic 4 $345 Existing Update Current Material Brand-individual Conceptual Material Cost Cost Delta Requirements / Differences Comparison Lightweight Constructions Example #5: The relevant cost difference was much greater in this earlier version. Electronic copy available at: https://ssrn.com/abstract=3812939 Figure 8 Details of the forecasted investment requirements for each scenario Total Fixed Costs in $Million 6,400 5,400 Production 2,694 4,500 4,400 2,303 1,929 1,906 Purchase 1,295 1,060 2,411 Development 2,037 1,720 1,665 Option 2a Option 3a Option 3b Option 2b Source: [Department Abbreviations of] Development Management, Purchase Management, Production Management Notes: Initial investments in product development and in production facilities, which can be located at AutoCompany’s factories or at suppliers’ sites. Initial investments are called “fixed costs” at AutoCompany. Electronic copy available at: https://ssrn.com/abstract=3812939 Figure 9 Sales forecasts in units for each scenario Combustion Car -10% BEV (Sales Volume in x1000 Vehicles) 2,000 2,000 1,800 1,800 63% 63% 1,260 1,260 48% 864 48% 864 936 936 52% 52% 37% 740 740 37% Option 2a Option 3a Option 3b Option 2b Assumptions Sales Department: ➢ Options 2a/3a: Equates Planned Sales Volume (Ratio Combustion Cars/BEVs According to Compliance) ➢ Options 2b/3b: Particular Models 100% BEVs → Migration 50% of Combustion Cars to BEVs, 50% to Competitors Notes: Projected sales units are lower in the “b” scenarios, because for those forecasts it is assumed that AutoCompany would offer fewer car models than in the “a” scenarios. Electronic copy available at: https://ssrn.com/abstract=3812939 TABLE 1 Interactions with organizational members People or groups the researcher interacted with Nature of the interaction Kinds of data the researcher obtained* Close controlling colleagues Multitude of meetings, phone calls, Presentation slides and other kinds of (in the same team, approximately 10 people) face-to-face and phone discussions, company documents (gathering these or emailing, working together being involved in producing these), Controlling colleagues background information (regarding specific (in other teams of the management accounting data and analyses and for a broader department, approximately 50 people) understanding), emails, notes on Senior management accountants conversations and observations, quotes (2 different people, corresponding to both episodes) Other controlling managers (management accountants at the same senior level as the focal senior management accountants, around 10 people) Top manager of the whole management Monthly meetings about the research Emails, quotes, background information accounting department project, more frequent meetings about AutoCompany topics, emailing Project team segment 3 Weekly meetings for several months, Presentation slides and other kinds of (representatives from various departments at working together with separate company documents (gathering these or AutoCompany and other brands, 30-40 people) team members for creating analyses being involved in producing these), cost and documents, face-to-face and estimates and other information for Project team segment 5 phone discussions about specific creating forecasts, emails, notes on (representatives from various departments at issues, emailing conversations, observations, and AutoCompany and other brands, 30-40 people) impressions of what seemed to matter for Project team episode 2 team members (what they considered (representatives from various AutoCompany important or sensitive, wanted to achieve or departments, around 20 people) avoid), quotes Electronic copy available at: https://ssrn.com/abstract=3812939 People or groups the researcher interacted with Nature of the interaction Kinds of data the researcher obtained* Management committee Participation in meetings, presenting Some additional presentation slides and other (approximately 20 people) cost estimates and other forecasts kinds of company documents, meeting minutes, oral information communicated with the presentation slides, notes on observations and impressions, quotes, meeting minutes Top management committee Participation in meetings, getting If the researcher could join the meeting: as (approximately 10 people) formal and informal information above about meetings If he could not join the meeting: Some additional presentation slides and other kinds of company documents, meeting minutes, oral descriptions from participants AutoCompany's executive board Getting formal and informal The researcher could join the meeting once: (approximately 20 people) information about meetings as above Normally, when he could not join the meeting: as above * The researcher was also able to collect information individually by accessing information systems with quantitative data and qualitative information, similar to AutoCompany employees, such as on manufacturing costs, investments (technology development, product development, and production assets), sales data and sales estimates, production plans, or strategic plans. Electronic copy available at: https://ssrn.com/abstract=3812939 TABLE 2 Overview of case study section Episode 1: Common platforms? Episode 2: Integrated or split product architectures? Decision focus Should AutoCompany adopt common platforms? This Should AutoCompany develop integrated or separate would be together with VehicleFirm in Segment 3 and modular architectures for conventional cars and battery together with CarEnterprise in Segment 5* electric vehicles (BEVs)? Accountant’s Maintain the status quo of AutoCompany’s own platform in Make sure that the disadvantages of the integrated directional Segment 3. However, in Segment 5: change to a architecture and the advantages of the separate preference common platform, which did not have to be architectures received much more attention. AutoCompany’s own platform. Formal Maintain separate platforms in Segment 3. Adopt a Develop separate architectures in all size segments. decision common platform in Segment 5, which would be developed under the responsibility of CarEnterprise. Final forecast Figures 2, 4 and 5, which showed cost savings and Figure 6, which compared four scenarios in terms of contribution margin effects for CarCorporation, if a contribution margins, investments in product brand would cancel its own platform and adopt the other development and production facilities, and resulting net brand’s platform as the common platform. earnings. * AutoCompany is the case company, VehicleFirm and CarEnterprise are two other brands with the group CarCorporation. These are disguised names. Electronic copy available at: https://ssrn.com/abstract=3812939 TABLE 3 Overview of the analysis and discussion section, showing a theoretical framework for motivated reasoning in the setting of preparing cash flow forecasts for capital budgeting decisions Exploiting normative ambiguity Creating justification Claiming Choosing Determining Counteracting Showing Demonstrating number inputs method details forecast scope information comparisons* scrutiny providers Examples of Assumptions Classification of Quantifying the Rejecting Connecting References to biasing and about sales transmissions cost impact of investment numbers (#9) representatives justifying substitution (#4) technical estimates (#7) of various Starting with cost the forecast effects differences functional areas Classification of Changing the numbers of in the case (engines) (#1) (#6) in the project axles (#5) comparison of current cars company team Assumptions sales estimates (#10) about feature (#8) Explanations of Assumptions about sales (four- detailed issues feature sales (#2) wheel drive) Quantification of (#2) scale effects (#3) Quantification (or not) of scale effects (#3) * Showing comparisons: 1) Between various parts of the forecast calculation 2) To hard numbers and sources outside the forecast calculation 3) To calculation methods in other forecasts 4) To the broader context, such as the organization’s strategy, common business practices, and societal trends. Electronic copy available at: https://ssrn.com/abstract=3812939 http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ARN Conferences & Meetings SSRN http://www.deepdyve.com/lp/ssrn/capital-budgeting-decisions-cash-flow-forecasts-and-management-bjHfAXoSV3

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Copyright: Copyright © 2021 Elsevier Inc.
eISSN: ARCH-4059
DOI: 10.2139/ssrn.3812939
Publisher site: See Article on Publisher Site

Abstract

Management accountants who are preparing cash flow forecasts for capital budgeting decisions may have preferred conclusions that lead to motivated reasoning. Whereas previous research has mainly demonstrated antecedents of accountants’ motivated reasoning (e.g., client pressure), we look more closely at the phenomenon of accountants’ motivated reasoning itself. We conducted a field study in the management accounting department in product development in a car company. We describe two detailed episodes around the technical design of new cars, preparation of cash flow forecasts, and decisions on capital investment projects. We develop and provide empirical evidence for a theoretical framework that builds on key elements of motivated reasoning: normative ambiguity and justification. The framework includes four ways in which accountants may exploit normative ambiguity for influencing their forecasts, and it contains four ways for accountants to create justification by showing comparisons. Keywords: cash flow forecasts, capital budgeting, motivated reasoning, product development, management accountants’ work Electronic copy available at: https://ssrn.com/abstract=3812939 1. Introduction Forecasts of future financial results play an important role for investors and managers, and forecasting is a central topic in accounting research (e.g., Brüggen et al. 2020; Cassar and Gibson 2008; Kadous et al. 2009). Forecasts may contain intentional and unintentional biases that influence decisions of investors and management (Hirst et al. 2007; Armstrong et al. 2007; Goodman et al. 2014; Hribar and Yang 2016), Whereas most research has focused on biases in disclosed, accruals- based earnings forecasts (e.g., Rogers and Stocken 2005; Veenman and Verwijmeren 2018; Dong et al. 2017), we consider undisclosed, cash flow forecasts that are related to capital budgeting decisions (Brüggen and Luft 2016). Managers often have incentives to provide biased information in the process of preparing such cash flow forecasts, for example because they want to increase the probability of initial acceptance of a capital investment proposal (Turner and Guilding 2012; Fehrenbacher et al. 2020; Brüggen and Luft 2011; Haka 2006). Even though biased cash flow forecasts for capital investment proposals is an economically highly relevant issue, our current understanding is still sparse. We focus on accountants in the process of preparing cash flow forecasts for capital budgeting decisions. Although accountants may also be involved in biasing, we know almost nothing about their role. The profession promotes the image of the neutral, transparent, and fact-based accountant (AICPA 2017; IMA 2019), but research shows many instances of behavior that differs from this image. Accountants and CFOs are not only acting as independent information preparers but sometimes deliberately distort information (Maas and Matějka 2009; Bishop et al. 2017; Feng et al. 2011; Indjejikian and Matějka 2009). Against this background, we investigate how accountants might be biasing cash flow forecasts in capital budgeting processes. Better understanding their behavior is important, because decision-makers rely on input from accountants and biased information may unknowingly influence their decisions. Electronic copy available at: https://ssrn.com/abstract=3812939 We propose that motivated reasoning theory may help to explain accountants’ biasing in this setting of preparing cash flow forecasts for capital budgeting decisions. Motivated reasoning theory (Kunda 1990; Boiney et al. 1997) explains that people with directional preferences ‘‘search for, interpret, and process information in a biased manner and, consequently, are more likely to reach the preferred conclusion’’ (Kadous et al. 2003, 759). Prior accounting research has found extensive support for behavior in line with motivated reasoning theory, including auditors and tax professionals (Anderson et al. 2017; Bradshaw et al. 2016; Kadous et al. 2003, 2008; Koch and Salterio 2017; Kadous et al. 2013; Luft et al. 2016). We expect motivated reasoning to also play a role when accountants are creating cash flow forecasts for capital budgeting decisions. They may not be neutrally evaluating the decision alternatives and letting the forecasted financial outcomes determine their recommendations (AICPA, 2017; IMA, 2019). Instead, accountants—like other stakeholders in capital budgeting processes—may prefer particular investment decisions. These preferences would create a directional goal to prepare a cash flow forecast that supports their preferred alternative. Accountants could engage in motivated reasoning to reach that goal, i.e., to show their preferred alternative in a financially favorable light. That something like that would happen may not be so surprising, but we investigate how it may happen. Much potential for motivated reasoning may exist in the setting of preparing cash flow forecasts for capital investment proposals. Motivated reasoning depends on normative ambiguity, in other words, vagueness about what conclusions should or should not be reached. Motivated reasoning also depends on the ability to appear rational and to provide justifications for conclusions. Considering these factors, motivated reasoning would seem feasible and likely to occur. Uncertainty about the future creates ambiguity and much of the input for capital budgeting forecasts is “soft” information (Kadous et al. 2005; Rowe et al. 2012), i.e., it can be changed in one direction or the other, because it is difficult to formally, objectively verify the information Electronic copy available at: https://ssrn.com/abstract=3812939 (Bertomeu and Marinovic 2016). Accountants may also have considerable opportunities to create justifications for their cash flow forecasts, not only because of uncertainty, but also because they have substantial flexibility for how to construct these internal, undisclosed, cash-flow forecasts that are not bound to financial reporting standards (Goretzki et al. 2018). Moreover, forecast biasing is more likely if it is more difficult to detect (Armstrong et al. 2007). The cash flow forecast in our context concerns a separate decision, instead of entity-wide results, making it difficult to later isolate the actual impact of the specific decision, to verify the forecast, and to pinpoint any biases. On the other hand, how motivated reasoning would work is a priori quite puzzling and the focus of this paper. Management accountants need to rely on significant input from others (Goretzki and Messner 2019), also in the context of capital budgeting (Rowe et al. 2012), which reduces their control over the forecast and could limit opportunities for forecast biasing and constructing justifications. Also, information asymmetry between accountants and management may be less than between management and external stakeholders. Managers may be able to critically review the forecasts that support the accountants’ recommendations (Rowe et al., 2012), which may reduce opportunities for forecast biasing that remains unnoticed. Furthermore, for biasing cash-flow based forecasts, the accountants cannot exploit the typical accrual mechanisms (e.g., around revenue recognition, discretionary accruals, or asset valuation) (Liu 2019). Finally, how would accountants engage in motivated reasoning whilst needing to maintain an image of competent and truthful professionals (Goretzki et al. 2018)? Against this background, we investigate how accountants engage in motivated reasoning when preparing cash flow forecasts for capital budgeting decisions. We focus on their means for influencing and justifying their forecasts. We conducted a field study to gain detailed insights into how accountants created estimates of the financial consequences of key decisions regarding the technical design of new products. We could establish that the accountants had directional Electronic copy available at: https://ssrn.com/abstract=3812939 preferences, i.e., their own ideas regarding these decisions. We could also establish how the accountants created cash flow forecasts that supported their preferred conclusions. Particular courses of action they wanted managers to implement were shown in a financially favorable light. We could identify a number of tactics accountants adopted for this purpose, which provide a more nuanced understanding of how motivated reasoning “works” in an organizational context. As a first contribution, we develop a deeper understanding of how accountants may produce biased cash flow forecasts for capital investment proposals. Uncertainty about the future creates normative ambiguity. We develop four mechanisms for actively making use of this ambiguity for influencing cash flow forecasts, and we provide empirical evidence from the field study. These mechanisms are based on claiming specific number inputs, making particular choices for the detailed implementation of a foresting method, determining the scope of the forecast in terms of alternatives and criteria that are being considered, and interacting with information providers depending on whether the information they provide is consistent with the accountant’s preferred conclusion. This result provides a more detailed understanding of the process of motivated reasoning in accounting. It complements research that has focused on antecedents of motivated reasoning in other accounting settings, but not much on how accountants engage in motivated reasoning (Kadous et al. 2003, 2008; Koch and Salterio 2017; Kadous et al. 2013; Austin et al. 2020). This results also contributes to the broader topic of budgeting for planning and resource allocation, which warrants specific research, besides performance evaluation (Becker et al. 2016). Second, we develop a more specific understanding of how accountants can create justification for their biased cash flow forecast by showing comparisons. Motivated reasoning requires the ability to appear rational and provide justification for a decision. It is known that showing comparisons supports justification (Kadous et al. 2013; Rowe et al. 2012; Huikku and Lukka 2016) and we build on this. We develop four different ways in which comparisons can be Electronic copy available at: https://ssrn.com/abstract=3812939 shown and provide empirical evidence from the field study. These ways are based on making connections between different parts of the forecast calculation, and on linking the forecast to hard numbers and sources outside the forecast calculation, to calculation methods used in other forecasts, and to the broader context. The remainder of the paper is structured as follows. Section 2 reviews literature to motivate the research question. Section 3 motivates and describes the research method. The findings from the study at the case company are presented in Section 4. We discuss these findings to develop a theoretical framework in Section 5, and Section 6 concludes the paper. 2. Background and research questions Biased cash flow forecast for capital investment projects Cash flow forecasts for capital investment projects may include unintentional errors. Technical issues, such inadequate forecasting techniques or missing data, could be causing these (Turner and Guilding 2012). Psychological biases may also be at work, such as over-optimism (Haka 2006) or escalation of commitment during later project stages when cash flow forecasts are being updated (Brüggen and Luft 2016). However, intentional errors may also play a role. Managers who provide input to cash flow forecasts may be inclined to misrepresent their private information. If they benefit from having a capital investment accepted, they may provide information that increases the likelihood of project acceptance, especially when they are competing for limited investment resources (Brüggen and Luft 2011). They might overstate sales numbers or sales revenues and understate required investment amounts or operating expenses. Psychological mechanisms leading to biases may also be at work on the side of the proposal reviewers and decision- makers, such as biases by affective reactions to a proposing manager (Kida et al. 2001; Fehrenbacher et al. 2020). Reversely, it may also in the interest of the manager to estimate less favorable cash flows, so targets for the investment proposal become more easily achievable, but this is less likely in a capital budgeting setting compared to operating budgets, because of the risk that the project may be completely rejected (Brüggen and Luft 2016). Electronic copy available at: https://ssrn.com/abstract=3812939 Managers are typically the agents who provide biased cash flows in the research, and the role of accountants is usually not addressed (Haka 2006). Perhaps accountants are assumed to be independent advisors (AICPA 2017; IMA 2019), who should make sure that decisions are based on neutral and verifiable forecasts: “whenever computation shows that an investment decision would delay or jeopardize the fulfillment of their (cash) objectives, the management accountants are not slow to wield their veto” (Lambert and Pezet 2011, 20). However, research in other contexts has shown many instances of behavior that differs from this image of neutral information providers, such as CFOs manipulating earnings (Bishop et al. 2017; Feng et al. 2011; Indjejikian and Matějka 2009) and management accountants selectively distributing or withholding information (Goretzki et al. 2018; Mahlendorf et al. 2018; Puyou 2018), misreporting data (Fauré and Rouleau 2011; Maas and Matějka 2009) or creating budgetary slack (Davis et al. 2006; Indjejikian and Matějka 2006). Therefore, the role of accountants biasing cash flow forecasts for capital budgeting decisions also seems theoretically worthwhile to investigate. Instead of management accountants having no prior opinion about a particular capital investment decision—they wait and see what the cash flow forecasts suggests should be done— management accountants may have their own preferences, i.e., they could favor a particular capital investment already before creating a cash flow forecast. Management accountants as business partners are expected to work closely with local management and to be involved in decision- making. Understanding the preferences of powerful managers, management accountants may consider it beneficial for themselves to support those preferences with their forecasts, similar to auditors and tax accountants responding to client preferences (Kadous et al. 2003, 2008; Koch and Salterio 2017; Hatfield et al. 2011; Austin et al. 2020; Cloyd and Spilker 1999). Furthermore, management accountants’ identity as business partners creates self-expectations (Wolf et al. 2020; Morales and Lambert 2013). Seeing themselves as analysists who need to understand the business Electronic copy available at: https://ssrn.com/abstract=3812939 from a financial perspective, they may believe that particular investment decisions would benefit the organization and, therefore, consider it acceptable to bias their cash flow forecast to promote those investment decisions. Pro-organizational motives can drive such behavior (Mahlendorf et al. 2018). In their study, accountants and CFOs with stronger organizational identification were more willing to not disclose negative company information if they believed this would benefit their organization, and this behavior was not associated with career self-interest. We do not focus in this study on the question why the management accountants would have particular preferences, but if they do, we investigate how these preferences may be biasing their cash flow forecasts for capital budgeting decisions. Motivated reasoning of accountants We analyze the accountants’ biasing of cash flow forecasts though the lens of motivated reasoning theory. The reasoning of people is sometimes not driven by accuracy goals but by motivational goals. Motivated reasoning concerns an individual’s cognitive processes for intentionally pursuing the goal of reaching a desired conclusion (Kunda 1990; Boiney et al. 1997). If from the beginning, an individual prefers a particular conclusion, such a directional motivation biases the judgment process. People selectively retrieve information in their own memory and creatively combine knowledge. They are more skeptical to the quality of information provided to them when it is inconsistent with their preferences. They process and present information to reach the preferred conclusion. For example, “people who want to believe that they will be academically successful may recall more of their past academic successes than of their failures. They may also use their world knowledge to construct new theories about how their particular personality traits may predispose them to academic success” (Kunda 1990, 483). Motivated reasoning depends on normative ambiguity (Kadous et al. 2003, 2008). Lacking clear guidance, for example from objective benchmarks, hard evidence, or strict rules, it becomes Electronic copy available at: https://ssrn.com/abstract=3812939 unclear what conclusions should or should not be reached. Furthermore, motivated reasoning depends on the ability to provide justification. People want to appear rational and they try to construct a plausible justification for the desired conclusion (Kunda 1990). Biasing in motivated reasoning is limited by “reasonableness constraints” and “while motivated decision makers will bias their judgments to favor the desired outcome, they will try to avoid biasing them more than necessary” (Boiney et al. 1997, 5). In other words, normative ambiguity and justifiability require sufficient degrees of freedom: there must be a range of conclusions that can be drawn and flexibility for constructing the reasoning towards those conclusions. We belief it is worthwhile to investigate accountants’ motivated reasoning in the specific situation of when they are creating forecasts for capital budgeting decisions. First, because this setting is empirically relevant. The way in which capital investment decisions are taken is clearly of economic importance and so we need to understand what kind of information accountants provide for these decisions. Research so far provides only limited insights into actual behavior of accountants in this setting. Although prior research suggests various biases that emerge with accountants in different roles (Maas and Matějka 2009; Bishop et al. 2017; Feng et al. 2011; Indjejikian and Matějka 2009), we lack empirical support for such behavior when it comes to preparing information for capital investment decisions. Second, this setting is theoretically relevant. Prior research provides an understanding of factors that are likely to enhance motivated reasoning in accounting, but we know far less about the phenomenon of motivated reasoning itself. Factors that have shown to be conducive to motivated reasoning include the nature of auditor focus and the strength of accuracy goals (Austin et al. 2020), the presence of quality assessment and the strength or directional goal commitment (Kadous et al. 2003), the level of practice risk (Kadous et al. 2008), independence threats and litigation risk (Blay 2005), advice justifiability and social ties with advice providers (Kadous et al. Electronic copy available at: https://ssrn.com/abstract=3812939 2013), client pressure and client affinity (Koch and Salterio 2017), or the implementation of an audit judgment rule (Kang et al. 2020). However, little is known about how accountants engage in motivated reasoning. How does motivated reasoning in an accounting context “work”? What do accountants actually do for modifying and justifying their recommendations? What are the accounting mechanisms that play a role in motivated reasoning? Our setting of creating cash flow forecasts for capital investment decisions is theoretically interesting for studying motivated reasoning itself. There is much potential for motivated reasoning but a priory, it is puzzling how accountants would be able to bias and justify their cash flow forecasts. It is a suitable setting for studying how motivated reasoning in accounting could “work.” How does motivated reasoning in accounting “work”? We would expect behavior in line with motivated reasoning to occur in the setting of accountants preparing cash flow forecasts for capital investment projects. Normative ambiguity surrounding the preparation of the cash flow forecast is high and considerable opportunities for constructing a justification exist. Uncertainty about the future makes it difficult to know which investment decision would be best. Moreover, the forecast is not disclosed and not bound to financial reporting standards, so there is considerable flexibility regarding the technical method for preparing the forecast. Furthermore, post-decisional verification is more difficult compared to earnings forecast, because it may be harder to isolate the actual outcomes in the organization’s overall results and to demonstrate forecast inaccuracy. However, it is theoretically not evident how motivated reasoning would work in this setting. First, we know less about motivated reasoning in a business context (Boiney et al. 1997), which differs from the situation in which an individual has a personal preference and is motivated to arrive at that preferred conclusion through biased, individual retrieving and processing of personal information. An organizational context involves multiple stakeholders, who also have their own Electronic copy available at: https://ssrn.com/abstract=3812939 goals and private information sources. Furthermore, routines partly formalize decision-making processes and limit flexibility for information preparers. Moreover, there is accountability of information preparers and decision makers to various audiences, and accountants need to fulfil particular roles. Accountants would want to be seen as acting according to professional expectations of objectivity and neutrality, which may limit motivated reasoning. Even more so, because they need to consider that other stakeholders in the organization also have information and may be able to critically review the forecast (Kadous et al. 2005). Accountants in an organizational context also depend on other stakeholders to provide information, which accountants may not be able to change when they are preparing their forecast. These organizational circumstances limit the degrees of freedom for influencing and justifying the forecast. Second, it is theoretically not evident how motivated reasoning would work because the setting of preparing cash flow forecasts for decisions on capital investment projects differs from preparing accrual-based earnings forecasts. Biasing of earnings forecasts may work through known accrual mechanisms (“tricks” around discretionary accruals, asset valuation, and revenue recognition, for example), which are not applicable to the same degree in cash flow forecasts. This technical circumstance makes it even more intriguing how management accountants’ motivated reasoning would lead to influencing their forecasts. In sum, the research question for this study is how accountants exhibit motivated reasoning when preparing cash flow forecasts for capital investment projects. 3. Research method A field study on cost management in product development The initial, broader focus of the research was cost management in product development, in particular through methods such as product modularity that go beyond target costing (Davila and Wouters 2004). We wanted to know how technical approaches such as modular design and product Electronic copy available at: https://ssrn.com/abstract=3812939 platforms were used for cost management purposes, how these approaches were implemented, how accounting departments and accountants were implicated, how tradeoffs were quantified, and which further issues played a role. Although modularity is a much-researched topic (Campagnolo and Camuffo 2009; Fixson 2007, 2005; Jiao et al. 2007), little is known about modularity for the specific purpose of cost management in product development (Labro 2004; Anderson and Dekker 2009; Jørgensen and Messner 2009, 2010). The exploratory nature of the research motivated conducting an in-depth field study in a single case company. Field research involves in-depth study of real-world accounting phenomena through direct contact with the organizational participants (Merchant and Van der Stede 2006) and provides the opportunity to grasp an accounting phenomenon in a broader context, to understand why it exists, how it works, and what its effects are (Hopwood 2007; Malsch and Salterio 2016). Recent interview-based field studies (Bills et al. 2018, 2020; Free and Murphy 2015; Free et al. 2021) and in-depth case studies demonstrate this potential (Ahrens and Chapman 2004; Free 2007; Fiolleau et al. 2013; Goretzki et al. 2017; Pfister and Lukka 2019; Väisänen et al. 2020; Wouters and Roijmans 2011). Field research starting with a theoretical focus offers can offer opportunities for surprising insights that trigger a process of going back and forth between thinking about theoretically relevant questions and explanations and collecting further information in the field (Ahrens and Chapman 2006). An abductive research process (Lukka and Modell 2010) capitalizes on the possibility to look at field data through a theoretical lens that changes over time, which helps to develop a better theoretical understanding of the accounting phenomenon and also guides and potentially redirects the course of the empirical field study. Furthermore, we intended to conduct the study in a car company, because of the strategic importance of cost management during product development in that industry (Anderson 1995; Ansari et al. 2006; Ibusuki and Kaminski 2007; Mahmoud-Jouini and Lenfle 2010). We aimed to Electronic copy available at: https://ssrn.com/abstract=3812939 conduct an interventionist field study (Suomala et al. 2014; Lyly-Yrjänäinen et al. 2017; Baard and Dumay 2021), because this could provide access to the organization at an unparalleled level (Jönsson and Lukka 2006), which we considered important because of the required in-depth understanding of complex product development processes and cost management methods that are used in car companies. We expected such an understanding to be very difficult to obtain by “only” visiting the company. Interventionist research involves the researcher being part of the action, longitudinally, as an asset for collecting detailed information, including information that may be difficult to specify from the outside and which may not always be shared with outside researchers (even if they would know which information they could request). Interventionist research varies with respect to the strength of the intervention. We only had a modest intervention (Jönsson 1996) in mind, interacting closely with organizational members to gather information, to contribute ideas, and to work on activities as these emerged. Thus, the collaboration was based in a genuine interest of the researchers to be working on issues the company considered helpful, yet the collaboration was also a means to another end: to establish a particularly good access and to collect information that would unlikely be available for outside researchers. Case company access and data collection We approached a car company known for a key project in the area of modularity and platforms for the purpose of cost management, making it a so-called extreme case (Cooper and Morgan 2008) that is useful to develop and test new theoretical insights. AutoCompany (a disguised name used in this paper) provided the opportunity to do the intended kind of interventionist study, involving two researchers who are also the authors of this paper. They were employees of the same university. The senior researcher visited the company but was basically offsite and coached the The crucial role of product development for cost management is notwithstanding the impact that short-term decisions later in the product life cycle may have on earnings in the car industry (Brüggen et al. 2011). Electronic copy available at: https://ssrn.com/abstract=3812939 research process. The junior researcher was mostly onsite, working in a management accounting department of AutoCompany that focused solely on product development activities and comprised around two hundred management accountants. The top manager of this department reported directly to AutoCompany’s CFO. Both researchers met roughly every six months with this top manager to discuss progress and the further direction of the research project. The university received funding from AutoCompany to be able to employ the junior researcher. Neither of the researchers received any personal financial compensation from the case company. At AutoCompany, the researcher was working as a colleague, involved in the daily business of the management accounting department. He interacted with many different organizational members in the course of working together (see Table 1), which provided an opportunity for collecting data, such as by making notes on conversations and observations, asking specific questions, exchanging emails, receiving company presentations and other documents, discussing background information regarding presentations and other documents, and having access to information systems. Thus, interviews solely for research purposes played a minor role in this study. We will present a few quotes, but our findings are mainly based on observations and artefacts, i.e., the cash flow forecasts. We have been able to collect much background information about how these artefacts had been shaped. Working together with people in the organization provided a natural way to build a much deeper understanding of what happened for the creation of the cash flow forecasts, before they were being formally presented in meetings. [Insert TABLE 1] Over time, the research topic became more focused. In the second half of the research project, the researcher was actively involved in producing analyses for several decisions concerning AutoCompany’s future modular strategy. Several themes resonated with us, such as the enormous uncertainty surrounding these analyses and anyway the impossibility to model all relevant Electronic copy available at: https://ssrn.com/abstract=3812939 considerations. We also noticed the very different preferences people had, not based on the financial numbers, and how the accountants fought to get attention for their viewpoints and analyses. We selected two episodes at AutoCompany as the empirical sources. The researcher played an active role as a management accountant in these episodes, but we investigate the decision preferences of other management accountants than the researcher, and other management accountants were driving how the forecasts were prepared. Data analysis Analyzing the information and guiding the research happened in layers. From the beginning, both researchers kept their own separate research diaries. This was a way to reflect on what was happening in the organization, the research process, interesting topics, angles for the potential theoretical contribution of the study, and emerging theoretical ideas. For the researcher on site, the research diary was also one medium for collecting data by making notes on events, conversations, meetings, and so on. He also made handwritten notes during the workday in hardcover notebooks. The research diary and these notebooks turned out to be important and helpful assets that contained much information that enabled us to write the empirical part of this paper. The researcher wrote extensive chronological summaries of each of the two episodes, including hundreds of references to internal documents, such as presentations, meeting minutes, and emails, and to the notes in the research diary and handwritten notebooks. We used these summaries for discussing the findings, which was the basis for writing next versions of the extensive summaries, now more structured around on the interesting aspects the researchers had identified, such as: what did the management accountants want, how did the company deal with The researcher was involved in a third episode, which became the basis for a study on target costing and specifically addressed the inclusion of market-based cost targets for product development activities and model-specific investments. We did not include a reference to this paper to avoid disclosing author identifying details. Electronic copy available at: https://ssrn.com/abstract=3812939 uncertainty, how were rough assumptions derived and included in the calculations, how and why were such assumptions (not) presented, discussed, and accepted or rejected? In parallel, we started writing, discussing, rewriting, etc. our texts on theory development in the form of a working paper (writing theoretical ideas was always going on “in the background” in the research diaries). The abductive process of finding the specific ideas for the current paper was characterized by intense iterations between the literature, the data and our own evolving ideas and texts. 4. Case study Company background AutoCompany followed a modular product strategy, which it described as overall guidelines for utilizing car projects with the goal to realize synergies as well as to master and reduce complexity across cars and car segments. The case company was at the beginning of developing a new modular architecture, which would cover several vehicle types (sedans, station wagons, SUVs) in several size segments. A modular architecture consisted of several platforms and modules. A platform referred to the lower part of the car body, where the engine, transmission, axles and seats are connected, providing the common base of cars with similar dimensions. A module was defined as a “technical group of components that form a functional and logical unit, which is completely interchangeable.” Modules were meant to be used by all cars within the same modular architecture, sometimes with adaptions. A key intended benefit of the modular strategy was to save costs. Developing the new modular architecture required many decisions on the fundamental design of cars, which involved significant capital investments in product development and production assets. Management accountants produced forecast of the cash flow consequences of decision Segment is the European term for vehicle classes. For example, minicompact, subcompact, compact, mid-size, large, minivans and sports utility vehicles (SUV) correspond to cars in the A-F, M, and J segments. Instead of letters, we use numbers for segments (“Segment 3” and “Segment 5”) in the field study to disguise information. Electronic copy available at: https://ssrn.com/abstract=3812939 alternatives, which were surrounded by much uncertainty. Other departments, in particular engineering, production, procurement, and marketing and sales provided inputs for producing the forecasts and also provided qualitative arguments apart from the financial forecast. The forecasts were discussed at several hierarchical levels, sometimes more than once, and finally, the executive board level made the formal decisions. The first episode of the case study describes decisions to potentially have common platforms across different brands. AutoCompany was part of a large corporation (CarCorporation) that included two other brands, which we will name VehicleFirm and CarEnterprise. The second episode concerns a decision about how to design battery electric vehicles next to traditional combustion engine vehicles. Table 2 provides an overview of the case study section. [Insert TABLE 2] Episode 1: Common platforms? The starting point AutoCompany and other brands of CarCorporation were sometimes offering car models in the same size segments, but these car models shared little technology (apart from engines). This episode describes the investigation of the financial consequences if AutoCompany would base future car models on common platforms, together with VehicleFirm in Segment 3 and together with CarEnterprise in Segment 5. This episode lasted around four months. The two project teams (one for each segment) consisted of representatives from several departments of the involved brands: accounting, development, production, marketing & sales, purchasing, and quality management. The project team leader reported to a top manager directly below AutoCompany’s CEO. Next to the senior management accountant as the formal member in both project teams, the researcher was involved in conducting analyses and participated in most of the project team meetings. Electronic copy available at: https://ssrn.com/abstract=3812939 The project teams’ results were presented and discussed in several rounds and at several management levels. First, the project team presented to a management committee that could formally take decisions (the project team could only give recommendations). Almost all project team members were also part of this management committee, but not all members of this management committee were in the project team. Next, results were presented to a top management committee and, finally, to AutoCompany’s executive board. The issue of potentially going to a common platform of these two brands in Segment 3 had a history in CarCorporation. This possibility had been investigated and rejected several times before. And so, during the first meeting with some cost experts at AutoCompany, one person said: Okay, so we are doing it again. This discussion is coming back every few years. The senior management accountant early on expressed his doubts about AutoCompany adopting a common platform that would be provided by VehicleFirm. Here [in Segment 3], I am really not sure if it would be clever to go on VehicleFirm’s platform. However in Segment 5, the senior management accountant’s idea about the desired outcome of the exercise was very different. He expressed to his colleagues a clear preference for changing the status quo and going to a common platform for both brands. We have to somehow achieve to get all these … vehicles on one platform – no matter if it’s then going to be developed by [AutoCompany] or [CarEnterprise]. It makes absolutely no sense to develop two platforms for this segment. There is simply too much savings potential here. We simply cannot ignore it anymore. When later the resistance from several other project team members mounted, he said to colleagues in management accounting: If we succeed putting both cars on one platform, then we will have achieved something really good for the company. Electronic copy available at: https://ssrn.com/abstract=3812939 The forecast and formal decision for Segment 3 A key element of the calculation was a comparison of the variable costs (basically the material costs) if AutoCompany would adopt a common platform that would be developed by VehicleFirm (in Segment 3) or CarEnterprise (in Segment 5). These comparisons were made on the basis of the current car models, even though the decision at hand concerned future car models. There was simply too much uncertainty about future cars for a meaningful cost comparison. Therefore, the management accountants aimed to examine the financial impact in each segment if the current product generations would have been based on only one instead of two platforms. They started with already available material cost comparisons, which were regularly made at CarCorporation, and compared specific configurations of car models that did not necessarily provide a representative average of the material costs of the actual model configurations sold. An existing, two-year-old comparison in Segment 3 indicated that VehicleFirm’s material costs were about $600 lower than AutoCompany’s costs. The management accountants checked and updated this cost comparison to the current situation, which required an adjustment of only a few percent to $585 per car. These results are shown as the first three bars in Figure 1. All figures shown closely resemble the figures used in the company, but with modified numbers, disguised qualitative information, and words translated to English. Furthermore, transparent green rectangles with comments have been included in some figures for clarification purposes. [Insert Figure 1: Comparison of material costs per unit in Segment 3] The next step was to identify variable costs that could be avoided (or would increase) if AutoCompany adopted a common platform with VehicleFirm. The variable cost difference was split into two parts. Some technical differences between cars of the two brands could continue to freely exist in case of a common platform. These kinds of differences and associated costs would not be affected by having a common platform. This is the fourth, light-blue bar in Figure 1. Other Electronic copy available at: https://ssrn.com/abstract=3812939 technical aspects were not flexible but inherent to a particular platform, and the related costs consequences were relevant for the decision. This is the fifth, dark-blue bar in Figure 1, estimated at $401 per car. Figure 2 shows the total calculated impact if AutoCompany would adopt a common platform in Segment 3 that would be provided by VehicleFirm. The first bar shows an impact on variable costs of $900 million, based on the rounded number of $400 per car (also indicated in Figure 1) and a total number of 2.25 million cars. The second bar mentions scale effects with a question mark, suggesting it would be a small, negative impact, but without quantifying this. The third bar mentions “fixed costs” which refers to investments in product development and production assets. A common platform would require fewer such investments, saving $400 million. Finally, negative “profit effects” of $1000 million are shown, referring to contribution margins that could not be realized anymore if a car would be based on a common platform. Figure 2 indicates a question mark for the total effect (“group impact”), suggesting that it might be slightly positive, but not that clear and perhaps not worthwhile. [Insert Figure 2: Cash flow forecast for AutoCompany in Segment 3] The senior management accountant who presented the calculation to the top management committee had the impression was that people were relieved that the cash flow forecast suggested to not change the status quo, which was also what the other members of the project team had recommended. As the project leader stated: finally, there is a reason why we do the things how we do them. The formal decision to maintain separate platforms in Segment 3 was taken by the executive board of CarCorporation. Before the meeting in which this was decided, a top manager in finance at CarCorporation contacted the project team. The official document for that meeting only included the summary calculation, similar to Figure 2, and he wanted details of the calculation to be available Electronic copy available at: https://ssrn.com/abstract=3812939 for the meeting of the executive board of CarCorporation as back-up material. The project team leader was also going to be present at that meeting and asked the management accountants for further details that would enable him, if needed, to explain the variable cost difference in more detail. After the meeting, the same top manager in finance contacted the management accountants and requested more detailed information. In several rounds via email and telephone, they provided additional details and explanations. CarCorporation seemed to ask this information to make sure that AutoCompany had documented in detail why they had recommended to not change the status quo and that AutoCompany would be able to answer potential future questions about the analysis. The forecast and formal decision for Segment 5 The calculation for Segment 5 is shown in Figures 3, 4 and 5. A two-year old comparison of the material cost per unit was updated, thereby reducing the cost difference by about one third (from $4536 to $2986 per unit), see Figure 2. The management accountants argued that this was due to technical changes since the original material cost comparison was made. The next step was to identify variable costs that could be avoided (or would increase) if AutoCompany adopted a common platform with CarEntreprise. Again, some technical differences between cars of the two brands could continue to freely exist in case of a common platform, so these costs were classified as irrelevant for the comparison. The amounted to $2619 per car, as shown in Figure 3. Other technical aspects were not flexible and the related costs differences were inherent to the common platform. For example, AutoCompany would have to adopt particular more expensive parts that inherently belonged to CarEnterprise’s platform. These costs are shown as the final, dark-blue bar in Figure 3, estimated at $367 per car. [Insert Figure 3: Comparison of material costs per unit in Segment 5] Figure 4 shows the estimated financial impact of the scenario that AutoCompany would give up its own platform and adopt the platform of CarEnterprise as the common platform. This looked Electronic copy available at: https://ssrn.com/abstract=3812939 very favorable: variable costs increases were limited (a cost increase of $350 per car (the number of Figure 3, rounded) × 314,000 cars = $110 million), scale effects led to significant cost savings ($400 million), lower investments were needed ($600 million), and even positive effects for contribution margins were feasible ($100 million). The forecast suggested that significant financial benefits could be achieved if AutoCompany would adopt CarEnterprise’s platform. The senior management accountant explicitly suggested in Figure 4 that this result was fairly robust, because changing it by 20% did not matter for the conclusion. If somebody is going to grumble about one small element of our evaluation, we can prevent distraction from the whole thing by saying: We know our assumptions are rough, but even if we are 20% wrong, there is still so much money to be saved. [Insert Figure 4: Cash flow forecast for AutoCompany in Segment 5] Figure 5 shows the financial impact if, the other way around, CarEnterprise would adopt AutoCompany’s platform. This was estimated to be neither positive nor negative—not particularly attractive. Considerable savings in variable cost, scale effects, and investments would all be eradicated by a significant loss of contribution margin of $1000 million. [Insert Figure 5: Cash flow forecast for CarEnterprise in Segment 5] Besides these calculations, other project team members emphasized various other considerations that were not included in the financial analysis, such as qualitative statements about specific technical disadvantages and brand image effects. The project team leader tried to convince the senior management accountant to change, or at least moderate their recommendation to cancel AutoCompany’s platform in Segment 5. Isn’t there a way we can formulate it somehow differently or in a softer way? The senior management accountant did not agree, and later formulated on the slides from an [AutoCompany] perspective … maintaining the status quo is financially not conceivable. Electronic copy available at: https://ssrn.com/abstract=3812939 The project team did not come to a common recommendation. As the researcher on site observed: [The senior management accountant] remained steadfast, which, in the end, makes that the other areas attach a green check to many scenarios, but then there’s a red check in the finance column. On the other hand, the scenarios where we put a green check are often with a red check from the other areas. CarEnterprise’s senior management accountant was frustrated with this outcome and mentioned that from my perspective, this is a clear failure of our assignment. After presenting the project team results, neither the management committee nor the top management committee made a formal decision. However, they decided to start the new modular architecture development anyway, and initially not to consider requirements for Segment 5 vehicles for AutoCompany. A few months later, the CEO of AutoCompany mentioned that he now recognized the necessity of a common platform in Segment 5 and a few weeks later, the CEO of CarCorporation stated in a meeting that future Segment 5 vehicles of both brands should be based on a common platform. A few months later, it was officially decided that this common platform would be developed under the responsibility of CarEnterprise. Episode 2: An integrated or split architecture for battery electric vehicles? The starting point This episode concerned a decision about the technical concept for future battery electric vehicles (BEVs). The fundamental decision was whether to have integrated or separate modular architectures for conventional cars and BEVs. This episode lasted around 13 months. The project team had a comparable composition as in the first episode, and the structure of how this team reported to management was also similar. The senior management accountant was another person than in the first episode. Again, one of the researchers was informally part of the project team. In the beginning of this episode, the project team leader and the representatives from Electronic copy available at: https://ssrn.com/abstract=3812939 engineering, marketing, and production expressed a clear preference for an integrated architecture. They mentioned flexibility as the main reason: AutoCompany would be able to manufacture both conventional cars and BEVs on the same production lines and could easily react to changes in the actual sales mix of conventional cars and BEVs. With two separate architectures, they claimed separate production facilities would be needed for both types of cars, which would require much larger investments. Furthermore, they claimed that product development investments would be much higher for developing separate architectures. They also acknowledged that the integrated architecture would cause some disadvantages for conventional cars, such as a higher weight. None of these qualitative arguments was quantified, but the decision taken by the top management committee at that time was that the option of the separate architectures was not expedient. The management accountants started trying to quantify some of the arguments, especially the difference in investments for product development and production facilities, which was supposed to be an advantage of the integrated design, and the difference in variable costs, which was expected to be the main financial disadvantage of the integrated architecture. As the senior management accountant said I am really tired of being the only one who talks sometimes against [an integrated design]. It is completely obvious that all the other departments already have made a decision. He wanted to achieve that the disadvantages of the integrated design, as well as the advantages of the split design with two separate modular architectures got much more attention. The forecast and formal decision After about seven months, a cash flow forecast was presented to the top management committee and the executive board, which concerned cars in one particular size segment. The slide shown in Figure 6 summarized the management accountants’ analysis by comparing four scenarios in terms Electronic copy available at: https://ssrn.com/abstract=3812939 of contribution margin, fixed costs (i.e., investments in product development and production facilities), and resulting net earnings. Scenario 2 concerned a split architecture, Scenario 3 concerned an integrated architecture, whereby the a and b scenarios were based on different sales forecasts. Figure 6 suggested that the split architecture had higher fixed costs (higher red bars for 2a compared to 3a and for 2b compared to 3b) but also higher contribution margins (higher green bars for 2a compared to 3a and for 2b compared to 3b). Overall, the net earnings did not differ under the lower sales forecast (comparing 2b and 3b); under the higher sales forecast, the net earnings were only one billion higher for the integrated architecture (3a compared to 2a). The presentation contained many more slides with more detailed calculations for the various numbers, and the management accountant provided further explanations during the presentation. [Insert Figure 6: Forecast of cash flow differences between four scenarios] The contribution margins were influenced by the variable costs per unit, which included a quantification of specific technical differences between the integrated and separate product architectures. The integrated architecture necessitated cars to be a bit higher and heavier, which increased production costs and CO2 emissions, and also required using wheels with a larger diameter. As indicated in Figure 6, the variable cost per vehicle was around $1000 higher for the integrated architectures (3a and 3b). The difference of around $1000 per unit between the integrated and separate architectures played a central role in the discussions of the executive board. When preparing the board meeting, a top manager commented on the financial effects: This [investment difference] is not that interesting. But the thing about the variable costs, this is the thing that is really exciting. During the board meeting, the CEO responded But guys, I am extremely struggling with a burden of thousand [dollar] for each combustion car. So, this [integrated architecture] is obviously not it. Electronic copy available at: https://ssrn.com/abstract=3812939 The meeting minutes mentioned that there is a total burden of [$ 1000] per unit for [combustion cars] in the [integrated] approach. This is evaluated as very critical by the executive board, especially against the background of the tense profit situation in the … segment. Against this background, the examination of various alternative split-scenarios that are using existing/planned platforms … is demanded. In the next months, the accountants made similar analyses for related size segments. Other management accountants made analyses for two SUV segments. The researchers were not involved in this, but could observe from the slides that the calculations were done similarly, with some aspects being more detailed. In particular, variable cost increases because of an integrated architecture were examined at a much more granular level. The result of approximately $1000 additional variable costs per unit remained quite stable, though, and after two additional executive board meetings, AutoCompany’s executive board made the final decision to develop separated architectures for BEVs and combustion-engine cars in all segments. 5. Analysis and discussion Motivated reasoning is driven by directional preferences, requires normative ambiguity, and is constrained by the need to appear rational and provide a justification for the decision (Kunda 1990; Boiney et al. 1997; Kadous et al. 2003, 2008). We will develop these notions further in the context of accountants who are creating cash flow forecasts for capital budgeting decisions, thereby providing a more nuanced insight of how motivated reasoning “works” is this accounting context. Table 3 provides an overview of the discussion. Finally, we will address a potential cause of the accountants’ directional preferences. [Insert TABLE 3] Exploiting normative ambiguity Normative ambiguity in the context of this study was caused by uncertainty about the future impact of capital budgeting decision alternatives. Normative ambiguity is a requirement but, as such, we Electronic copy available at: https://ssrn.com/abstract=3812939 propose it is not enough to affect the forecast through motivated reasoning. This requires that accountants actively make use of normative ambiguity to purposefully bias the forecast in a direction that makes it more in line with their directional preferences. So, besides directional preferences and normative ambiguity, there needs to be flexibility that makes motivated reasoning actually feasible. We suggest the possibility to influence the forecast as the mechanism between normative ambiguity and the occurrence of motivated reasoning. Specifically, we suggest four ways in which accountants can translate normative ambiguity into degrees of freedom for influencing the forecast and changing the outcome: claiming number inputs, choosing method details, determining the forecast scope, and counteracting information providers. These ways make use of a grey zone of uncertainty. Extreme ways for constructing the forecast will be considered as too unrealistic, speculative, or unusual, but there is also a broad zone of reasonable ways for constructing a forecast about an unknown future. In this grey zone, nobody really knows and one way for constructing the forecast (by making a particular assumption, for example) is as believable—and uncertain—as another way. Management accountants could put forward assumptions about numerical input values, ways for implementing the forecasting method, and the scope of the forecast, which support getting to a forecast that is consistent with their preferred conclusion. Their actions would be in the grey zone to make them acceptable enough to most other stakeholders, and they would not be biased more than needed for achieving the preferred conclusion (Boiney et al. 1997). Accountants could also readily accept inputs and assumptions from others that are in the grey zone and fit their agenda. By making their actions explicit, the accountants can make the first move and reverse the burden of proof. In the grey zone, not much evidence is needed if you are the first stating an assumption that is “reasonable” (as well as arbitrary). Others who question that assumption would be expected to provide much more evidence for an alternative assumption that would overturn the initial assumption. Stating an assumption is Electronic copy available at: https://ssrn.com/abstract=3812939 like staking your claim. Management accountants can make assumptions and take other actions that suit them, but they can only act strategically within boundaries (Goretzki et al. 2018). We propose that, in an organizational context, the grey zone provides a more nuanced idea of normative ambiguity for motivated reasoning, The grey zone matters not so much for individual reasoning but plays a role in social processes in which the forecast is discussed, questioned, defended, and has more or less influence (Rowe et al. 2012). Making use of the various ways for influencing the forecast can be done silently and defended if detected and questioned, but it can also be done prominently, to draw attention. Providing explicit information about the forecast may have the effect of focusing the burden of proof. Explicating specific ways of how a forecast has been constructed draws attention to particular numerical inputs, method choices, or scope considerations, suggesting that these are the relevant aspects to talk about. It makes those aspects more salient and puts those up for discussion and challenge. Furthermore, remaining silent on other ways of how the forecast has been constructed makes those aspects more difficult to be challenged. Others will have more difficulties to understand the forecast, to identify issues that are relevant for them to address, and to discuss and challenge those issues. And so, the management accountants can cleverly influence which aspects of their forecasts they would prefer to be “on the stand” and which they would like to keep silent. Claiming number inputs Claiming number inputs refers to the accountant taking the lead in stating particular numerical values as inputs for the forecast. These claimed values support getting to an outcome that is more in the direction the accountant prefers. Episode 1 of the field study showed that the senior management accountant’s directional preference was to maintain the status quo of AutoCompany’s own platform in Segment 3. Yet in Segment 5, he preferred changing to a common platform and did not mind if this would be Electronic copy available at: https://ssrn.com/abstract=3812939 CarEnterprise’s platform. Reversely, CarEnterprise’s senior management accountant did not want to give up their platform and agreed with AutoCompany adopting their platform. It is especially interesting to see how the calculation in Segment 5 was constructed, because the senior management accountant’s preference for what should be done would require a drastic change from the current status quo and his preference was quite different from what other team members wanted. We will now analyze how the calculation had been carefully constructed to get to a result that supported the preferred conclusion of AutoCompany’s senior management accountant. Our examples concern differing assumptions made for the forecasts in Segments 3 and 5 about sales substitution effects (Example #1), feature sales (Example #2), and scale effects (Example #3). Example 1: Assumptions about sales substitution effects. AutoCompany offered a strong, large powertrain in Segment 3 that it could not sell anymore in case VehicleFirm’s platform would become the common platform. The powertrain would not fit into VehicleFirm’s platform, because of geometrical limitations that were fundamental to the modular architecture, and even significant additional development investments would not be able to solve this problem. Although it was unclear how important that powertrain would be for the future product generation, AutoCompany’s senior management accountant decided to evaluate this issue on the basis of the current product generation. The management accountants gathered pricing data, cost data, and installation rate data about the various powertrains. They assumed that all customers who had purchased the strongest powertrain would buy the next strongest powertrain instead, and they estimated the contribution margin losses accordingly. In other words, they assumed no sales volume losses (100% substitution rate) and estimated the lost contribution margin to be $1000 million, shown in Figure 2. In Segment 5, a similar powertrain issue played a role. CarEnterprise would not be able to offer a strong powertrain anymore, if it would adopt AutoCompany’s platform. This time, significantly less than 100% substitution was assumed. Instead, it was assumed that many Electronic copy available at: https://ssrn.com/abstract=3812939 CarEnterprise customers would not purchase a vehicle at all if this powertrain would not be available. Figure 5 (“Canceling strong powertrains”) indicated a contribution margin loss of $750 million. Although this absolute number was less than in the other segment, it was much more relative to the total contribution margin, because sales in Segment 5 were less than in Segment 3. CarEnterprise’s senior management accountant had inserted the bar with this number of $750 million contribution margin loss in an exhibit in a PowerPoint presentation and explained in the accompanying email to AutoCompany’s senior management accountant only that without offering this [powertrain], we expect a significant loss of sales volume. In other words, less than 100% substitution was assumed, but without providing any further explanation. Despite the significant impact of this assumption, the lack of details and support, and the inconsistency with Segment 3, details were neither provided by CarEnterprise’s senior management accountant, nor required by AutoCompany’s senior management accountant, who readily agreed to adjusting the calculation, writing Thank you for the input, we will include the information accordingly. It goes rather exactly to (almost) zero … When he later presented Figure 5 to the management committee, he mentioned only briefly the different assumptions about substitution. His readily acceptance of this number was also surprising for another reason: it would probably be avoidable. The management accountant could reasonably have assumed that it would be technically possible to develop a platform in Segment 5 in such a way that including the large powertrain would still be possible. This would require significant extra product development investments, but these would still be much less than the very large contribution margin loss. Similar assumptions had been made about other technical features (e.g., the transmissions in Segment 5). However, such an assumption was not made this time. The assumption about substitution effects Electronic copy available at: https://ssrn.com/abstract=3812939 favored the outcome of CarEnterprise not adopting AutoCompany’s platform. Notice also that motivated reasoning was limited to what was needed (Boiney et al. 1997). The assumption of 100% substitution in Segment 3 still provided sufficient contribution margin losses, whereas less than 100% substitution was needed in Segment 5 to show the required amount of contribution margin losses. Example 2: Assumptions about feature sales. Another example of diverse assumptions for both brands in Segment 5 concerned future sales of the four-wheel drive feature. Currently, CarEnterprise offered this feature as an expensive option that the vast majority of customers ordered, but AutoCompany included this as a standard feature. In case CarEnterprise would provide the common platform, AutoCompany would also offer the feature as a paid option. The senior management accountant (not the marketing representative) estimated a positive impact of $100 million, as shown in Figure 4, arguing that this would be comparable to the business practices of all competitors. The senior management accountant used data about current prices, costs and adoption rates from CarEnterprise to estimate a business case. The parameters for the calculation of this number were also provided in Figure 4, which allowed verifying that the different parts were internally consistent: 314,000 units × 65% × (0.5 × $2000 – $500) = $102,050,000. In case AutoCompany would provide the common platform, CarEnterprise would include the feature standard on all cars. CarEnterprise assumed a contribution margin loss of $250 million, shown in Figure 5 (“Cancelling option X”), but did not provide further explanation or details. These assumptions again supported the conclusion that it would be favorable for the group if AutoCompany adopted CarEnterprise’s platform and unfavorable if CarEnterprise adopted AutoCompany’s platform. While the business case for AutoCompany and the estimation of revenue losses for CarEnterprise might be plausible on their own, the combination of both contradictory assumptions Electronic copy available at: https://ssrn.com/abstract=3812939 is remarkable. It could reasonably have been assumed that a technical solution would be possible enabling CarEnterprise to still offer this feature in the same way as it currently did, so as a paid option, even with AutoCompany’s platform as the common platform. However, this alternative assumption would have made the outcomes less in line with the accountants’ preferences. Claiming number inputs may also happen by stating the assumption that particular inputs are too uncertain and should be ignored, thereby implicitly assuming particular numbers to be zero, however. So instead of not considering an effect (because it would be too uncertain), a numerical input (of zero) is actually assumed, which does affect the outcome in a particular direction. The next example demonstrates this. Example 3: Quantifying scale effects. Another interesting difference is the quantification of scale effects. Figure 2 indicates scale effects as having a small, negative impact, but without quantifying this—Figure 2 shows a question mark. Figures 4 and 5 shows a financial impact of scale effects of $400 million. The management accountants based these numbers on a one-year-old analysis, which had another purpose, but it also quantified scale effects. It showed a cost impact of between 3% and 12% on material costs and these estimates had previously been accepted by CarCorporation’s top management. The management accountants applied these percentages to the platforms’ current material costs, which resulted in a positive scale effect of $400 million, regardless which platform would be cancelled. This assumption favored the outcome of AutoCompany adopting CarEnterprises’s platform in Figure 4. It also favored that CarEnterprise would adopting AutoCompany’s platform (which is not what the senior management accountant wanted), but this effect could be sufficiently countered in total in Figure 5. Choosing method details Choosing method details refers to making particular detailed choices for how the forecast is being constructed. Within the implementation of an overall approach that, as such, may be pretty Electronic copy available at: https://ssrn.com/abstract=3812939 “neutral,” the accountant makes particular choices for implementing the approach such a way, that the outcome is more in line with the decision preference. Several examples in the case study demonstrate how the accountants made subtle choices regarding the method for creating a forecast that strengthened getting to a result that supported their preferred conclusion. We will discuss the following examples below in more detail: Example #4 concerns the classification of the costs of axles as either a relevant cost difference (in Segment 3) or as an irrelevant cost different (in Segment 5). Example #5 concerned the classification of the transmission in Segment 5 as initially a relevant cost difference, but subsequently as an irrelevant cost difference. Example 4: Classifying the axles. Major variable cost differences resulted from the fact that AutoCompany used more expensive axles than VehicleFirm in Segment 3 and CarEnterprise used more expensive axles than AutoCompany in Segment 5. In Segment 3, the cost experts wanted to classify the axles as a technical difference that was inherent to the platform, making the cost difference relevant for the decision. Axles are shown in the dark-blue bar in Figure 1. The senior management accountant did not question this classification. He suggested it was not his competence or job to define which kind of axle AutoCompany or VehicleFirm would need, saying Who am I that I could answer all these questions today? As controllers, we cannot. In Segment 5, however, the senior management accountant took a very different position and pushed back in similar discussions. He argued that it was absolutely not understandable for him why axles were inherent to the current platforms. I wouldn’t know why [CarEnterprise] could not use our axles tomorrow. He assumed that AutoCompany could still use the less expensive axles if both brands were going to adopt CarEnterprise’s platform, and likewise, CarEnterprise could still use its own axles if Electronic copy available at: https://ssrn.com/abstract=3812939 AutoCompany’s platform would be the common one. So this time, axles were in the light-blue bar in Figure 3 and did not impact the cost comparison. Example 5: Classification of transmission. The material cost difference calculation shown for Segment 5 in Figure 3 was not the first version. An earlier version differed regarding the classification of many costs as relevant or irrelevant for the comparison. This earlier version looked like Figure 7 and showed a relevant cost difference of $1996 per car, because many more parts were classified as inherent to the platform. This version of the calculation suggested that each unit would become about $1996 more expensive if AutoCompany would adopt CarEnterprise’s platform; and the other way around, CarEnterprise would be able to save this amount per unit. This result triggered further discussions with CarEnterprise’s senior management accountant, who strongly opposed these numbers. I disagree with the diagram ‘Material cost delta from [AutoCompany’s platform] to [CarEnterprise’s platform]’. I cannot confirm the platform-determined [material cost] delta like this. … Please adjust the diagram accordingly. Thank you. [Insert Figure 7: Earlier version of the comparison of material costs per unit in Segment 5] In the following weeks, the management accountants looked into technical differences in more detail. The separation between two kinds of variable cost differences (the avoidable and unavoidable variable cost differences) was sometimes difficult to make, and the management accountants often ended up having very detailed technical discussions with cost experts and making assumptions that were defensible, but which could also have been made differently. CarEnterprise’s expensive transmission was one reason for the initially large cost differences. The further discussions led to the assumption that the future platform would be able to accommodate several kinds of transmissions and AutoCompany would not be forced to also adopt the expensive transmission if it were to have a common platform with CarEnterprise. Reversely, Electronic copy available at: https://ssrn.com/abstract=3812939 CarEnterprise would not achieve cost savings with the transmission if it were to have AutoCompany’s platform in common. As a result, the cost difference for the different transmissions moved from the dark-blue to the light-blue area of irrelevant costs. The relevant difference in material cost per unit was reduced. For several other parts, assumptions were also changed as to whether costs should be considered as relevant or irrelevant for the decision. In the end, the management accountants were able to reduce the relevant cost difference to $367 (as shown in Figure 3). With this smaller relevant cost difference, it became more attractive for AutoCompany to adopt CarEnterprise’s platform, and less attractive the other way around. The assumptions in Examples #1 through #5 helped to get to the outcomes shown in Figures 2, 4 and 5, which made two conclusions inevitable: In Segment 3, AutoCompany should not adopt a common platform with VehicleFirm, but in Segment 5, AutoCompany should cancel its own platform and adopt CarEnterprise’s architecture. In other words, these assumptions influenced the forecasts in such a way, that these supported the management accountant’s preferred conclusions. Determining forecast scope Determining forecast scope refers to the formulation of alternatives and criteria, thereby establishing what is considered and quantified, and what is not considered and quantified within the scope of the cash flow forecast. Determining forecast scope is another way in which accountants can influence forecast outcomes and make these more consistent with their directional preferences. Episode 2 of the field study showed that the management accountant’s preference was to make sure that the disadvantages of the integrated design and the advantages of the split design received much more attention. The forecast presented to the management committees and the executive board supported this preferred conclusion. That was no coincidence and we will describe several examples of motivated reasoning in this episode. Electronic copy available at: https://ssrn.com/abstract=3812939 Example 6: Quantifying the cost impact of technical differences. The accountants insisted on quantifying the higher unit cost implications of technical characteristics of the integrated design. As mentioned earlier, the integrated architecture necessitated cars to be a bit higher and heavier, which increased production costs and CO2 emissions and also required using wheels with a larger diameter. The management accountants had collected data on these technical differences and insisted they would estimate the cost impact of these differences. When presenting the data on technical differences in the project team, the project leader expressed his doubts: That is too much for me – surely, only around 15 kilograms will remain. The management accountants protested and refused to change the numbers, arguing there was no need for speculation. They insisted on accepting these estimates, arguing these been provided by the engineers. In the following weeks, the management accountants quantified the technical differences together with cost experts, and also presented and discussed these with the project team several times. They relied on cost information of the current car models, which indicated a material cost of around $4 per kg of weight of a car body, which they multiplied with the additional weight per car. They valued CO2 emissions with $95 per gram, which was the fine car companies would have to pay per gram from 2021 in the EU if they failed their emission goals. They considered the additional complexity costs by multiplying material costs by 3%, which was a reasonable but also quite arbitrary assumption about higher material costs due to greater complexity of the integrated architecture. As a concept engineer said: Somehow, they will be there. I just cannot tell you in detail today which parts will be affected. In total, the additional material costs of combustion cars for the integrated design was estimated at roughly $ 800–1000 per car. Electronic copy available at: https://ssrn.com/abstract=3812939 Not everyone was convinced and the sales manager in the project team stated, after the meeting with the executive board, those $1000, I still do not believe them to this day. He had not been able to contest this result, however. The accountants had explained that their calculation of additional costs per unit included the assumption of a 3% cost increase because of additional complexity. If someone challenged that assumption and would like to change it to 2%, for example, then both assumptions would be in the grey zone and be equally reasonable and uncertain. Why would 2% be any better? Why would we change it? More generally, when management accountants presented their forecasts (to the project team, management committee, top management committee, and the executive board) they made the point that if others would have better information, they would be like to hear it. But it was also clear that “better” would need to be supported with strong evidence. Without that, their initial assumptions remained. Counteracting information providers Finally, counteracting information providers refers to the accountant’s response to the information that is provided by other stakeholders. Depending on whether that information supports an outcome that is in line with the accountant’s directional preferences, they may either to accept or fight the input information. In the case company, the management accountants’ responses to the information they received was consistent with how that information favorably or unfavorably influenced the cash flow forecasts. As we saw above in Example #6, the management accountants had received information about technical differences between cars as the basis for a calculation of variable cost differences. When other actors raised doubts, the management accountants immediately defended this information. They needed that information for their calculation of unit cost disadvantages of the integrated architecture. Furthermore, in Example #7 below, they refused to accept investment Electronic copy available at: https://ssrn.com/abstract=3812939 estimates from the production, development and purchasing departments, arguing that these were too large and too uncertain, yet they accepted in Example #1 in Episode 1 estimates of contribution margin losses that were arguably comparably large and uncertain. Both actions were consistent with the decision preferences of the management accountants in both episodes. In Example #8 below, they could not argue against sales forecast provided by the sales representative in the project team, but then they modified the information and largely neutralized its unfavorable impact. Example 7: Rejecting investment estimates. Three departments provided the project team with their first estimates of the required investments for both concepts, which the accountants refused to accept. The estimates indicated significantly higher investments for the separate architectures. Production simply stated it would need everything double to be able to build both kinds of cars and estimated twice the investments for the separate architectures. Development and purchasing also estimated almost a double investment. At this stage, the project leader asked the senior management accountant in the project team for an exhibit to show the investment differences to top management, but he refused to provide such a exhibit. We want to provide decision alternatives to the executive board. At the moment, we have two scenarios, but one of them is definitely not an alternative. The management accountant provided only a qualitative diagram. In the following weeks, development managers came up with new and more detailed estimates for development costs, which showed smaller cost differences between the concepts. The production managers also produced new and more detailed numbers. Their conclusion still was that the separate architectures would cause higher production investments, but the extraordinarily large difference was gone. But, they insisted to also mention that they believed the company’s production site was not large enough to implement the separate architectures that required much larger production facilities. The management accountants now accepted these estimates from the Electronic copy available at: https://ssrn.com/abstract=3812939 development and production departments and incorporated these in Figure 8. The purchase manager in the project team refused to provide a new estimate. However, the management accountants produced their own estimate for purchasing investments, based on a relationship between purchasing investments and development costs in today’s car models, which they also included in Figure 8. [Insert Figure 8: Details of the forecasted investment requirements for each scenario] Example 8: Changing the comparison of sales estimates. When the management accountants could not avoid incorporating particular sales estimates provided by the sales department—these sales numbers were disadvantageous for the separate architectures that they preferred—they changed the analysis and neutralized the effect of these sales numbers. Sales volumes were another key element of the calculation. The project team had agreed on specific BEVs and combustion models as the basis for the analysis, and the management accountants asked the sales manager in the project team to provide sales estimates for these cars. The sales manager argued that only the integrated architecture would enable offering each model both as a conventional car and as a BEV. He claimed that sales volumes would be too low for doing the same with separate architectures and therefore assumed that only a few models would be offered both as a conventional car and as a BEV. He made some further assumptions about sales mix and substitution in a complex spreadsheet he sent to the management accountants, arriving at sales numbers that were around 10% lower for separate architectures. The management accountants did not like how these estimates disadvantaged the concept of separate architectures. They could not avoid using the data, but changed the comparisons by creating four scenarios. This is shown in Figure 9. Options 2a and 2b are separate architectures and Options 3a and 3b concern integrated architectures. Instead of comparing the integrated architecture including the higher sales volume (Option 3a) with the separate architectures based on Electronic copy available at: https://ssrn.com/abstract=3812939 the lower sales volume (Option 2b) as the sales manager had suggested, they created two comparisons, each time on the basis of the same sales volume. Instead of focusing on lower sales for a particular scenario, the calculation now focused on cost differences, given comparable estimates of sales volumes. [Insert Figure 9: Sales forecasts in units for each scenario] Creating justification Motivated reasoning is constrained by the need to appear rational and provide a justification for the decision (Boiney et al. 1997; Kunda 1990). Managers who need to decide whether and how to act on the management accountants’ recommendations, will likely critically review the information supporting those recommendations (Rowe et al. 2012). Anticipating such reviews, management accountants may try to enhance the justifiability of their forecasts similar to, for example, auditors or consultants who are providing justification for their recommendations that will be reviewed by other auditors and client managers (Kennedy et al. 1997; Kadous et al. 2013; Koonce et al. 1995; Agoglia et al. 2003; Kadous and Sedor 2004). Justifications provide support for an expressed viewpoint and aim to persuade the target audience that this viewpoint is valid (Shankar and Tan 2006). Justifiability may increase, if the supporting information recognizes trade-offs, includes benchmarks and other comparisons, and provides evidence of extensive efforts for searching, checking and validating information (Kadous et al. 2013; Rowe et al. 2012; Huikku and Lukka 2016; Goretzki et al. 2016). We discuss two ways for accountants to create justification for their forecasts: by showing comparisons and demonstrating scrutiny. While these activities are known to potentially increase justifiability, the intended contribution of this field study is to provide empirical evidence as to how this may happen, in particular what are various ways in which accountants may show comparisons to justify their cash flow forecasts for capital budgeting decisions. Electronic copy available at: https://ssrn.com/abstract=3812939 Showing comparisons We define the action of showing comparisons as relating the input for the forecast, the method for crating the forecast, and the outcome of the forecast to other information that is important and credible to decision makers. Comparisons enable information receivers to identify similarities and differences, to verify consistency, and to relate information to credible benchmarks and anchor points, which can help to reduce doubts about the quality of the information (Rowe et al. 2012; Kadous et al. 2013). The field study suggests four ways in which accountants can show comparisons. Between various parts of the forecast calculation Comparisons between various parts of the forecast calculation demonstrate internal consistency of the forecast. Receivers of the information can see how numbers are aggregated and disaggregated, so how numbers add up or are otherwise being combined, and how input data and several calculation steps lead to aggregate information (Englund and Gerdin 2015). Such relationships between different elements of the forecast provide receivers of the information with comparisons and consistency checks, thereby helping to justify the forecast outcomes. Example 9: Connecting numbers. Numbers connected elements of the forecast calculation and demonstrated internal consistency, within the same figure and between related figures. Within one figure, relationships between the bars were shown. For example, in Figure 1: 600 (first bar) – 15 (second bar) = 585 (third bar), etc. For many of these bars, boxes provided more detailed numbers that added up to the total number for the bar. For example, 23 + 37 + 42 + 76 + 6 = 184, the total amount for the fourth bar in Figure 1, or 340 + 260 – 200 = 400, the total amount for the third bar in Figure 2. Similarly in Figure 3, 350 × 314,000 = 110 million, the total amount for the first bar, and the assumptions for the calculation of the business case ware show in a box with the fourth bar (as described above in Example #2). Electronic copy available at: https://ssrn.com/abstract=3812939 Connections between numbers in related figures were also shown. For example, notice the remarks “calculated with 400” and “calculated with 350” in Figures 1 and 3. Figure 1 was connected to Figure 2 and Figure 3 to Figures 4 and 5 by showing that the rounded numbers of $400 and $350 for the material cost differences per unit were used consistently throughout the calculations. Furthermore, when the management accountants presented these calculations to the project team and later to top management, they pointed out that the material cost difference per car was quite similar in both segments ($400 and $350 per unit). They also mentioned that the method used was similar for both segments and made sure that the figures looked similar. To numbers and sources outside the forecast calculation Secondly, justifiability may also be enhanced by showing comparisons to numbers and sources outside the forecast that are hard, because these are important and credible to decision makers. In the context of uncertain numbers about the future, the forecast could be compared to existing actual numbers that people would not question. The forecast may also build on other estimated, future numbers that have already been validated by decision makers. Furthermore, it can be shown that particular people whose opinions matter to decision makers (for example, because of their expertise or hierarchical position) have provided particular information as inputs to the forecast. Example 10: Starting with cost numbers of current cars. The comparison of material costs per unit in Episode 1 was based on an existing material cost comparison for current cars, which was adjusted on the basis of the question “what if the current cars would have been based on a common platform?” The use of these hard data as the foundation for the forecast was explicitly shown on Figures 1 and 3 and was also mentioned during presentations of the forecasts. By showing how the calculations started from the basis of the actual material costs for current cars, information receivers were provided with a comparison to information they would already consider hard. Several other calculations were also based on hard data for existing cars that were adjusted Electronic copy available at: https://ssrn.com/abstract=3812939 to reflect changes as a result of adopting a common platform, such as the calculation of the business case for feature sales (Example #2) and the quantification of scale effects (Example #3). Showing comparisons to calculation methods in other forecasts Thirdly, justification can be strengthened by comparing the forecast calculation method to the calculation methods in other relevant forecasts that are important to decision makers. Examples could be comparisons to other forecasts that followed the same method, to legislation, technical standards, or accounting standards. In the case company, the similarity of the forecast methods for the two car segments in Episode 1 was emphasized, thereby providing comparisons between forecast methods. As mentioned in Example #9, the exhibits looked identical in terms of structure and colors, it was emphasized orally that the methods were similar, and it was pointed out that the relevant variable cost differences were in the same order of magnitude ($400 and $350). Showing comparisons to the broader context Finally, we propose that justifiability may be enhanced by making comparisons to the broader context of the forecast, based on information decision makers have from other sources (Hall 2010). For example, particular assumptions may become more persuasive by comparing these to business practices in the same organization or in similar other organizations, by connecting these to the strategy of the organization, or by showing how these are consistent with broader societal trends. Example #2 of the business case calculation showed this effect. Not only did the details provided in this example demonstrate internal consistency, but these also helped to build a comparison to hard numbers (the parameters were identical to the current situation for the other brand) and to a broader context of business practices (AutoCompany would offer this option in way that was quite common for most brands in this car segment). Demonstrating scrutiny Accountants may also create justification for their forecasts by indicating these have undergone Electronic copy available at: https://ssrn.com/abstract=3812939 scrutiny, in the form of activities such as debating, challenging, checking, correcting, and elaborating the information. Information receivers may find a forecast more convincing, if they believe it has undergone and survived scrutiny by representatives from various parts of the organization (Rowe et al. 2012; Kadous et al. 2013). Demonstrating scrutiny provides a counterbalance to the earlier actions of making explicit assumptions about number inputs, method details, and forecast scope. That was done to pretend neutrality and openness to input from others; now, demonstrating scrutiny can be used to suppress doubts and to silence other stakeholders. We suggest that accountants may try to enhance justifiability of their forecast by showing explicitly that the forecast has been extensively scrutinized. The scrutiny would be public (within the company) and documented (Rowe et al. 2012). Accountants could, for example, explain checks that have been conducted, describe sources for data, refer to earlier meetings in which the forecast has already been shown and discussed, and mention the names or positions of experts who provided estimates, approved particular assumptions, or sanctioned the forecasting method. By showing explicitly to senior management that the forecast has been scrutinized, accountants may also try to silence stakeholders who have been involved in preparing the forecast and who disagree. Disagreeing positions those stakeholders took during earlier discussions and which they have not been able to successfully defend, become difficult to propose in later discussions with senior management. The management accountants can signal that everybody has had their chances, and so what is now presented to senior management must be considered as the hardest information possible about an uncertain future. That message is not only directed at senior management to indicate that the information has been scrutinized, but the message is also intended to silence the disagreeing stakeholders. They got all the details and explanations, they had their chances to challenge the forecast, but now it is too late to fight what they could not change earlier. In the case company, when management accountants presented their forecasts, they often Electronic copy available at: https://ssrn.com/abstract=3812939 made explicit references to experts who had been involved in scrutinizing the forecast. For example, during the final presentation to the top management committee in Episode 2, Figure 8 showed that the data source was the departments of product development management, purchasing management, and production management. The accountants also mentioned that the analyses had been extensively discussed with representatives of these departments as members of the project team. They tried to signal to the top managers in the committee that their representatives in the project team had had their chances to argue the forecast, but those battles had been fought, this was the final result, and now the numbers were fixed. However, management accountants may also express implicitly that the calculation has been scrutinized. The fact that the management accountant is able to show detailed information about numbers, information sources, technical choices, etc. suggest that they must have been in contact with experts, consulted several data sources, and conducted specific analyses. The level of detail and explanation provided implies that scrutiny has been applied to be able to come up with that kind of information. We have described several instances of how management accountants in the case company explained how some very detailed costs had been included and some very specific issues had been considered, which implied that the numbers had been discussed with experts and scrutinized to be able to come up with such detailed analyses. Furthermore, management accountants can help information receivers to scrutinize the information themselves, again to create more justification for the forecast. Providing additional explanations and details without too much technical language, for example, can help making the information more understandable for information receivers who may have limited accounting knowledge. This could enable them to verify whether the overall implications of the calculation are making sense to them and are consistent with their broader experience and “gut feeling.” Providing information also helps information receivers to evaluate whether specific parts of the Electronic copy available at: https://ssrn.com/abstract=3812939 forecast are credible in relation to particular non-accounting knowledge these information receivers have (Hall 2010). A potential cause of directional preferences Motivated reasoning requires directional preferences. We will discuss the management accountant’s business partner role as a possible cause of such preferences in the context of capital budgeting decisions. We can only build on personal impressions during the field study, because we could not thoroughly investigate why the accountants in the case company had particular preferences. Thus, this part of the discussion is a tentative suggestion, an idea for future research. Management accountants have “simultaneous roles as ‘partners’ of operational management and ‘financially objective informers of the board’” (Ahrens 1997). The latter role is about safeguarding assets, enforcing rules, and reporting results. The business partner role, however, requires management accountants to cooperate with local management, to understand the business, to support decision-making, and to be involved in strategy building (Horton and Wanderley 2018; Goretzki and Messner 2019; Goretzki et al. 2013; Granlund and Lukka 1998). Tensions from role duality can strongly impact the behavior of accountants. For example, they selectively distribute information (Goretzki et al. 2018; Puyou 2018) and they sometimes engage in data misreporting (Fauré and Rouleau 2011; Maas and Matějka 2009) or budgetary slack building (Davis et al. 2006; Indjejikian and Matějka 2006) to respond to managers’ expectations for management accountants as their business partners. In the context of capital budgeting decisions, management accountants in their business partner role could also be responding to managers’ expectations. The management accountants interact with managers whose collaboration they need, not only for making the forecasts but also in many other situations (Puyou 2018), and those managers may have quite strong opinions about what should be decided. The management accountants could adopt the preferred conclusion of Electronic copy available at: https://ssrn.com/abstract=3812939 particular powerful actors. Management accountants providing their support for that conclusion may seek to build goodwill with those powerful actors and thereby to strengthen their own political position in the organization. Perhaps they believe that supporting the position of particular powerful managers in the organization could promote their own careers or more broadly increase the influence and status of the finance function. The business partner role also creates internal expectations and shapes the management accountant’s self-definition (Wolf et al. 2020; Morales and Lambert 2013). Pursuing an identity that includes understanding the business and supporting decision-making, management accountants could form a professional opinion about what they believe should be done—from a financial perspective. The management accountants draw on various kinds of information collected for the decision at hand, information concerning other, more or less similar decisions, general contextual information, and their own broader professional and personal experiences (Hall 2010; Bruns and McKinnon 1993). Their organizational identity is to be the ones who consider cost, profitability, return on investment, and such financial criteria (Lambert and Pezet 2011). Based on these criteria, perhaps they judge particular capital investment decisions as more desirable. It does not mean that the management accountants can fully quantify their conclusion—this can partly be based on qualitative arguments. Decisions on capital investment projects involve a lot of uncertainty about the future and a lot of different considerations, some of which cannot completely and meaningfully be expressed in cash flow terms. To give advice and be a business partner, accountants need to make judgments that involve other information besides cash flow forecasts. Although other groups in the organization may have different preferences, because they put more weight on other criteria, such as an exciting technology, cool design, manufacturing job preservation, sales volume, or brand image (Berhausen and Thrane 2018), the accountants consider the finances. For a specific decision at hand, identity and intuition make the management Electronic copy available at: https://ssrn.com/abstract=3812939 accountant prefer a particular capital investment project. Whatever the reason for their preferred conclusion may be, management accountants as business partners are also supposed to present themselves as independent and neutral “producers of truthful knowledge” (Lambert and Pezet 2011, 10). They need to present analyses that appear objective, evidence-based, and transparent—a forecast of the financial consequences of capital investment projects in this situation (AICPA 2017; IMA 2019). Therefore, they may adjust their official forecast to reflect their preferred conclusion. 6. Conclusion Biasing of cash flow forecast for capital budgeting proposals is an important topic in research and practice (Brüggen and Luft 2016; Turner and Guilding 2012; Haka 2006), but we know little about the role of accountants. They, too, could have a preferred conclusion, which would motivate preparing a cash flow forecast that shows their preferred capital investment project in a financially favorable light. We conducted a field study to investigate how accountants exhibit motivated reasoning when preparing cash flow forecasts for capital investment projects. This setting of accountants preparing cash flow forecasts for capital investment projects is a priori interesting, because this setting may be conducive to but also limit motivated reasoning. Motivated reasoning depends on normative ambiguity and the possibility to influence the forecast and provide justification for it. On one side, uncertainty about the future creates normative ambiguity; the preparation of undisclosed cash flow forecasts offers flexibility (compared to disclosed earnings forecast) that can be used for influencing and justifying the forecast; and forecast accuracy for a specific decision may be difficult to verify on the basis of actual outcomes for the entire organization. But, other stakeholders in the organization have information, which they provide and accountants cannot change, and which they can use to critically review the forecast. This situation makes it more difficult for accountants to influence and justify their forecast. Electronic copy available at: https://ssrn.com/abstract=3812939 Furthermore, accountants cannot use accrual-based mechanisms for influencing their cash flow forecast. So how would they do it? Our field study presented data from two episodes of decisions about the technical design of new cars and related capital investments. We show that management accountants preferred particular capital investment alternatives from the start, and at the end of each episode, the cash flow forecasts supported their preferred conclusions. We suggested that the preferred conclusions of management accountants could have arisen from their role as business partners, who are expected to understand the business, provide information for decision-making, and give recommendations to management (Fauré and Rouleau 2011; Maas and Matějka 2009; Davis et al. 2006; Indjejikian and Matějka 2006). Powerful managers and other influential organizational members may expect the management accountants to support their position and adjust the cash flow forecast accordingly. Furthermore, management accountants as business partners may have their own assessment of what—from a financial perspective—they believe should be done, and they may adjust their forecast accordingly. The goals to deal with the expectations from other organizational members and to support their own assessment could lead to motivated reasoning. As a first contribution, we develop four ways in which accountants can exploit normative ambiguity (Kadous et al. 2003) for influencing the cash flow forecast. These mechanisms provide the link between normative ambiguity and actually influencing the forecast to make it more supportive of the preferred conclusion. The mechanisms are based on claiming specific number inputs for the forecast, making specific choices for the detailed implementation of a foresting method, determining the scope of the forecast in terms of alternatives and criteria that are considered, and interacting with information providers depending on whether the information they provide is consistent with the accountants’ directional preferences. We also propose that normative ambiguity could be understood as a grey zone of uncertainty. Electronic copy available at: https://ssrn.com/abstract=3812939 Within that zone, alternative assumptions and choices for preparing the forecast are equally plausible (or implausible) and there is little convincing proof for one assumption above another assumption. The boundary of the grey zone is when assumptions and choices become so extreme that too many other stakeholders would consider these as simply unrealistic. Accountants can make assumptions and choices within the grey zone of uncertainty that support getting to a cash flow forecast that supports their preferred alternative. Doing so may help to reverse and to focus the burden of proof. In other words, accountants could explicitly stake their claims (which support their preferred conclusion), hint at issues to review (and deflect from issues they would rather not be discussed), and invite “better ideas” (but then also require proof as to why these ideas would be better). After having been so “neutral and open,” they can make it clear that “everyone has had their chances” (so their forecast is now final). We provide empirical evidence from the field study. These insights complement earlier research on motivated reasoning in accounting, which has focused on other accounting settings and has primarily investigated factors that stimulate motivated reasoning (Kadous et al. 2003, 2008; Koch and Salterio 2017; Kadous et al. 2013; Austin et al. 2020). However, far less is known about phenomenon of motivated reasoning in accounting itself. This research also to adds to the understanding of informational tactics of management accountants, which mostly been investigated in the context of preparing and discussing ex-post information for performance evaluation (Goretzki et al. 2018, 2016). Furthermore, this study adds to our understanding of the broader topic of budgeting for planning and resource allocation, which is important to study specifically, besides performance evaluation (Becker et al. 2016). As a second contribution, we develop a more specific understanding of how accountants can create justification for their cash flow forecast. Prior research has mostly investigated justification in the contexts of auditing tasks (Trotman et al. 2015; Shankar and Tan 2006; Kennedy et al. 1997; Agoglia et al. 2003; Kadous et al. 2013) and somewhat for in management accounting settings Electronic copy available at: https://ssrn.com/abstract=3812939 (Rowe et al. 2012; Kadous et al. 2005; Loraas 2009; Huikku and Lukka 2016). Building on this, we develop specific ways for accountants to create justification for their cash flow forecast by showing comparisons. That is, by relating the forecast input, method, or outcome to other information that is important and credible to decision makers. Management accountants can demonstrate internal consistency of different parts of the forecast, connections to numbers or sources that information receivers already find persuasive, links of the particular forecast to other forecasts and forecasting methods that matter to information receivers, and by connecting the forecast to a broader context. We provide empirical evidence from the field study. A limitation of this study is that, despite fantastic access in the case company, there is always more data to wish for. For an even better understanding of the forecasts in these two episodes, we would have liked to witness more of the discussions that were taking place in the top management committee and the executive board. And, the story goes on. Further forecasts are being constructed and discussed, decisions are being made, which have not been included but could have further informed our understanding. Another limitation concerns our tentative explanation for the cause of the accountants’ preferred conclusion. The explanation could only be based on impressions from the case company, but we lack similar kind of detailed empirical evidence that was provided as support for the theoretical framework presented in Table 3. Various explanations for the directional preferences and motivated reasoning in the case company are possible and we do not make empirical claims here. Future research could look at questions such as: How do management accountants form their decision preferences? How are these preferences related to their professional experience, incentives, self-interest, or psychological mechanisms? Furthermore, if accountants deliberately craft forecasts to back up their preferences and skillfully enhance the justifiability of these forecasts, which is what our study suggests, how do Electronic copy available at: https://ssrn.com/abstract=3812939 information receivers react in the longer run? Management accountants need to be seen as competent and truthful professionals to be effective in their role (Goretzki et al. 2018). If other stakeholders would start to suspect that accountants have decision preferences that influence the information and justification they provide, this could hurt their image as producers of truthful knowledge (Goretzki et al. 2018). Especially the accountants’ discretion for providing information may increase suspicion of persuasion attempts (Friestad and Wright 1994). Using persuasion tactics may “backfire” if this increases information receivers’ skepticism and reduces how much they accept the information that is provided (Bhattacharjee and Brown 2018; Nelson and Rupar 2015). However, if the information provided actually helps managers making decisions, the credibility of the information may be enhanced, even if managers would believe management accountants are also using persuasion tactics (Isaac and Grayson 2016). Finally, future research could address motivated reasoning and the construction of forecasts when accountants have less discretion, because the similar accounting information is regularly produced. Information receivers would expect particular information and the management accountants would not have much flexibility to purposely influence the information they provide and to increase the persuasiveness of it. How would they bias and justify the information in such situations? 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Electronic copy available at: https://ssrn.com/abstract=3812939 Figure 1 Comparison of material costs per unit in Segment 3 Axles $110 Values in $ Per Vehicle Topic 2 $105 Topic 3 $47 600 Topic 4 $33 15 Topic 5 $15 Others $91 Topic 1 $15 Calculated with 400 Topic 1 $23 Topic 2 $37 Topic 3 $42 Topic 4 $76 Others $6 Existing Update Current Material Brand-individual Conceptual Material Cost Cost Delta Requirements / Differences Comparison Lightweight Constructions Only this number is a relevant cost difference that Note: Transparent green rectangles with comments were not part of the is used in the next steps for original exhibits in the case company, but these have been included in Segment 3. some of the figures in the paper for clarification purposes. Figure 2 Cash flow forecast for AutoCompany in Segment 3 (AutoCompany canceling its own platform and adopting the platform of VehicleFirm as the common platform in Segment 3) Group Perspective Values in $Million Result from Figure 1 Variable Cost Saving $400 Sales Volume 2.25 Mio. Loss of Strong Powertrains Assumptions: 1. Sales Volume Unchanged 2. 100% Substitution with Hard to quantify because Initial Fixed Costs Strongest Remaining Powertrain both platforms already large Savings 340 volume (but tending to be Updating Costs Savings negative, because the AutoCompany car model 260 1,000 has a much larger volume Additional Fixed Costs share of its platform than for VehicleFirm Platform theVehicleFirm car model of its respective platform) Variable Costs Scale Effects Fixed Costs Profit Effects Group Impact The cash flow forecast suggests that the total impact for the group is not that clear but might be only slightly positive in Segment 3. Electronic copy available at: https://ssrn.com/abstract=3812939 Figure 3 Comparison of material costs per unit in Segment 5 Values in $ Per Vehicle 4,536 Topic 1 $1467 Topic 5 $150 Axles $250 Topic 6 $125 Topic 3 $165 Topic 7 $70 1,550 Topic 4 $113 Topic 8 $279 2,986 Topic 1 $250 Reason 1 $150 Reason 2 $100 2,619 Others $367 Topic 2 $310 Topic 3 $990 Calculated with 350 Existing Update Current Material Brand-individual Conceptual Material Cost Cost Delta Requirements / Differences Comparison Lightweight Constructions Only this number is a relevant cost difference that is used in the next steps for Segment 5. Figure 4 Cash flow forecast for AutoCompany in Segment 5 (AutoCompany canceling its own platform and adopting the platform of CarEnterprise as the common platform in Segment 5) 1.188 Values in $Million Group Perspective Additional Option X for AutoCompany Premises: 1.Installation Rate: 65% 4% Cost Saving Rate (Negative scale effect for 2.Pricing: $2.000 remaining architecture 990 3.Costs: $500 neglectable) 4.Revenue Factor 0.5 Result from Figure 3 5.Sales Volume 314.000 Additional Variable Costs $350 Initial Fixed Costs Savings 670 Sales Volume 314.000 Updating Costs Savings 80 Additional Fixed Costs for CarEnterprise Platform −150 -110 Variable Costs Scale Effects Fixed Costs Profit Effects Group Impact +/− 20 % The cash flow forecast suggests that the total impact for the group is very positive and robust. Electronic copy available at: https://ssrn.com/abstract=3812939 Figure 5 Cash flow forecast for CarEnterprise in Segment 5 (CarEnterprise canceling its own platform and adopting the platform of AutoCompany as the common platform in Segment 5) Group Perspective Values in $Million Cancelling Option X −250 Cancelling Strong Powertrains −750 4% Cost Saving Rate 1,000 Additional Variable Costs $350 Sales Volume 285.000 Variable Costs Scale Effects Fixed Costs Profit Effects Group Impact The cash flow forecast suggests that the total impact for the group is zero. Electronic copy available at: https://ssrn.com/abstract=3812939 Figure 6 Forecast of cash flow differences between four scenarios All Car Models as Electric & Combustion Version Several Car Models Only as Electric Version 2.0 2.0 -10% -10% Sales Volume (in Million Vehicles) 1.8 1.8 Variable Cost Burden for Combustion CO2 Compensation Cars in Option 3 Approx. $1.000 / Vehicle Contribution Margin Fixed Costs Net Earnings (in $Billion) 4.5 6.4 8.5 7.6 5.1 5.4 4.2 4.4 3.1 2.1 1.4 1.3 1.6 1.6 Scenario Scenario Scenario Scenario 2a 3a 3b 2b Separate architecture 2b and Separate architecture 2a estimated to yield lower net integrated architecture 3b estimated to yield similar earnings earnings than integrated architecture 3a Notes: Scenarios 2a and 2b concerned separate product architectures for electric and internal combustion vehicles, Scenarios 3a and 3b concerned an integrated architecture. These scenarios were compared on the basis of two different sales forecasts, 2a and 3a using the higher sales forecasts, 2b an 3b using the lower sales forecasts. Electronic copy available at: https://ssrn.com/abstract=3812939 Figure 7 Earlier version of the comparison of material costs per unit in Segment 5 Values in $ Per Vehicle 4,536 Topic 1 $1391 1,550 Topic 2 $50 Topic 3 $150 2,986 Others $405 Topic 1 $250 Reason 1 $150 1,996 Reason 2 $100 Topic 2 $310 Axles $230 Topic 3 $990 Topic 2 $250 Topic 3 $170 Topic 4 $345 Existing Update Current Material Brand-individual Conceptual Material Cost Cost Delta Requirements / Differences Comparison Lightweight Constructions Example #5: The relevant cost difference was much greater in this earlier version. Electronic copy available at: https://ssrn.com/abstract=3812939 Figure 8 Details of the forecasted investment requirements for each scenario Total Fixed Costs in $Million 6,400 5,400 Production 2,694 4,500 4,400 2,303 1,929 1,906 Purchase 1,295 1,060 2,411 Development 2,037 1,720 1,665 Option 2a Option 3a Option 3b Option 2b Source: [Department Abbreviations of] Development Management, Purchase Management, Production Management Notes: Initial investments in product development and in production facilities, which can be located at AutoCompany’s factories or at suppliers’ sites. Initial investments are called “fixed costs” at AutoCompany. Electronic copy available at: https://ssrn.com/abstract=3812939 Figure 9 Sales forecasts in units for each scenario Combustion Car -10% BEV (Sales Volume in x1000 Vehicles) 2,000 2,000 1,800 1,800 63% 63% 1,260 1,260 48% 864 48% 864 936 936 52% 52% 37% 740 740 37% Option 2a Option 3a Option 3b Option 2b Assumptions Sales Department: ➢ Options 2a/3a: Equates Planned Sales Volume (Ratio Combustion Cars/BEVs According to Compliance) ➢ Options 2b/3b: Particular Models 100% BEVs → Migration 50% of Combustion Cars to BEVs, 50% to Competitors Notes: Projected sales units are lower in the “b” scenarios, because for those forecasts it is assumed that AutoCompany would offer fewer car models than in the “a” scenarios. Electronic copy available at: https://ssrn.com/abstract=3812939 TABLE 1 Interactions with organizational members People or groups the researcher interacted with Nature of the interaction Kinds of data the researcher obtained* Close controlling colleagues Multitude of meetings, phone calls, Presentation slides and other kinds of (in the same team, approximately 10 people) face-to-face and phone discussions, company documents (gathering these or emailing, working together being involved in producing these), Controlling colleagues background information (regarding specific (in other teams of the management accounting data and analyses and for a broader department, approximately 50 people) understanding), emails, notes on Senior management accountants conversations and observations, quotes (2 different people, corresponding to both episodes) Other controlling managers (management accountants at the same senior level as the focal senior management accountants, around 10 people) Top manager of the whole management Monthly meetings about the research Emails, quotes, background information accounting department project, more frequent meetings about AutoCompany topics, emailing Project team segment 3 Weekly meetings for several months, Presentation slides and other kinds of (representatives from various departments at working together with separate company documents (gathering these or AutoCompany and other brands, 30-40 people) team members for creating analyses being involved in producing these), cost and documents, face-to-face and estimates and other information for Project team segment 5 phone discussions about specific creating forecasts, emails, notes on (representatives from various departments at issues, emailing conversations, observations, and AutoCompany and other brands, 30-40 people) impressions of what seemed to matter for Project team episode 2 team members (what they considered (representatives from various AutoCompany important or sensitive, wanted to achieve or departments, around 20 people) avoid), quotes Electronic copy available at: https://ssrn.com/abstract=3812939 People or groups the researcher interacted with Nature of the interaction Kinds of data the researcher obtained* Management committee Participation in meetings, presenting Some additional presentation slides and other (approximately 20 people) cost estimates and other forecasts kinds of company documents, meeting minutes, oral information communicated with the presentation slides, notes on observations and impressions, quotes, meeting minutes Top management committee Participation in meetings, getting If the researcher could join the meeting: as (approximately 10 people) formal and informal information above about meetings If he could not join the meeting: Some additional presentation slides and other kinds of company documents, meeting minutes, oral descriptions from participants AutoCompany's executive board Getting formal and informal The researcher could join the meeting once: (approximately 20 people) information about meetings as above Normally, when he could not join the meeting: as above * The researcher was also able to collect information individually by accessing information systems with quantitative data and qualitative information, similar to AutoCompany employees, such as on manufacturing costs, investments (technology development, product development, and production assets), sales data and sales estimates, production plans, or strategic plans. Electronic copy available at: https://ssrn.com/abstract=3812939 TABLE 2 Overview of case study section Episode 1: Common platforms? Episode 2: Integrated or split product architectures? Decision focus Should AutoCompany adopt common platforms? This Should AutoCompany develop integrated or separate would be together with VehicleFirm in Segment 3 and modular architectures for conventional cars and battery together with CarEnterprise in Segment 5* electric vehicles (BEVs)? Accountant’s Maintain the status quo of AutoCompany’s own platform in Make sure that the disadvantages of the integrated directional Segment 3. However, in Segment 5: change to a architecture and the advantages of the separate preference common platform, which did not have to be architectures received much more attention. AutoCompany’s own platform. Formal Maintain separate platforms in Segment 3. Adopt a Develop separate architectures in all size segments. decision common platform in Segment 5, which would be developed under the responsibility of CarEnterprise. Final forecast Figures 2, 4 and 5, which showed cost savings and Figure 6, which compared four scenarios in terms of contribution margin effects for CarCorporation, if a contribution margins, investments in product brand would cancel its own platform and adopt the other development and production facilities, and resulting net brand’s platform as the common platform. earnings. * AutoCompany is the case company, VehicleFirm and CarEnterprise are two other brands with the group CarCorporation. These are disguised names. Electronic copy available at: https://ssrn.com/abstract=3812939 TABLE 3 Overview of the analysis and discussion section, showing a theoretical framework for motivated reasoning in the setting of preparing cash flow forecasts for capital budgeting decisions Exploiting normative ambiguity Creating justification Claiming Choosing Determining Counteracting Showing Demonstrating number inputs method details forecast scope information comparisons* scrutiny providers Examples of Assumptions Classification of Quantifying the Rejecting Connecting References to biasing and about sales transmissions cost impact of investment numbers (#9) representatives justifying substitution (#4) technical estimates (#7) of various Starting with cost the forecast effects differences functional areas Classification of Changing the numbers of in the case (engines) (#1) (#6) in the project axles (#5) comparison of current cars company team Assumptions sales estimates (#10) about feature (#8) Explanations of Assumptions about sales (four- detailed issues feature sales (#2) wheel drive) Quantification of (#2) scale effects (#3) Quantification (or not) of scale effects (#3) * Showing comparisons: 1) Between various parts of the forecast calculation 2) To hard numbers and sources outside the forecast calculation 3) To calculation methods in other forecasts 4) To the broader context, such as the organization’s strategy, common business practices, and societal trends. Electronic copy available at: https://ssrn.com/abstract=3812939

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Published: Mar 17, 2021

Keywords: cash flow forecasts, capital budgeting, motivated reasoning, product development, management accountants’ work

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Capital Budgeting Decisions, Cash Flow Forecasts, and Management Accountants’ Motivated Reasoning: A Field Study

Capital Budgeting Decisions, Cash Flow Forecasts, and Management Accountants’ Motivated Reasoning: A Field Study

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Capital Budgeting Decisions, Cash Flow Forecasts, and Management Accountants’ Motivated Reasoning: A Field Study

Capital Budgeting Decisions, Cash Flow Forecasts, and Management Accountants’ Motivated Reasoning: A Field Study

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