Multiple heuristics in the investment decision making process

(1)

Author : R.A.A.M. Smit Student number : 10317341

Date : 12-12-2015

Version : V7.00 (Final)

Institution : University of Amsterdam Faculty : Economics and Business

Programme : Executive Programme in Management Studies

Track : Strategy

(2)

2

Abstract

This article presents case studies concerning the decision making process of investment decisions at the corporate level of the firm. The purpose of the study is to grant a better understanding of the cognitive processes applied when making investment decisions. The study contributes to existing literature with insights into the possibility of using multiple heuristics at the same time in the investment decision making process. Furthermore, approaching the recognition of heuristics in a qualitative way is also something new. The central research question reads; is it possible that multiple types of heuristics influence the investment decision making process at the same time? The study is designed to find evidence for the application of availability (what comes to mind), recognition (what is similar to what), satisficing (what will do), anchoring and adjustment (what comes first) and heuristics of discovery in the decision making process of the case decisions. The primary source of qualitative data includes reconstructions of the decision making processes and in-depth interviews. Documents related to the decisions made serve as a secondary data source which is qualitative in nature as well. The paper presents findings from case studies, which suggest that it is possible that multiple heuristics influence the investment decision making process at the same time. In the investment decision making processes studied, recognition heuristics, satisficing heuristics and heuristics of discovery were identified. The study also provides a way in which heuristics can be identified in a qualitative way, instead of with mechanisms and narrow definitions.

(6)

6

Introduction

Decision making has been a subject for research and discussion for decades. In the early days of research on decision making, the field was primarily focused on the decision-making process itself and how and why its outcomes differed from leading models and theories (Simon, 1955; Simon, 1979). Although Simon showed that the assumptions of neoclassical theory violated reality, the use of out-dated models is still dominant (Gigerenzer 1991; Van Ees, 2009). This caused the encouragement for a greater focus on understanding organizational decision making in present research. This focus will require theory that incorporates the social processes and contextual factors that affect organizational decisions (Argote & Greve, 2007).

It is argued that much economic data is in fact not factual, as neoclassical theory assumes, but it represents concepts that rely on figure of speech, logic and heuristics (De Graaf, 2014). Heuristics can be defined as decision making practices that are displayed in artefacts and simplified cognitive models of reality (De Graaf, 2015). Research on those cognitive models has provided evidence for the effects of heuristics in the decision making process in different contexts (Kahneman & Tversky, 1974; Gigerenzer, 1991; Van Ees, 2009; and others). Despite increased attention paid to the fallacies associated with deploying neoclassical models and the risen awareness on the application of cognitive models that simplify complex situations, little is known about the possible influence of multiple heuristics on the investment decision making process at once. Previous literature often refers to the recognition, availability (Gigerenzer, 1991), anchoring and adjustment (Epley & Gilovich, 2006), and satisficing (Van Ees, 2009) heuristic as part of the decision making process. Complemented with the, at some points, different heuristics of discovery proposed by Nooteboom (2000), these are the five types of heuristics that are examined for their influence on investment decisions in this study.

(7)

7 Figure 1: Conceptual model

Explorative in nature, the study attempts to find evidence for the employment of multiple heuristics in the process of investment decision making at the same time. As seen in the conceptual model, the heuristics of discovery tile has a slightly darker colour representing its deviation from other heuristics as will be explained in the literature review. To reach the goal, investment decision making processes are reconstructed and assessed for heuristics using in-depth interviews and document reviews.

The study enriches existing literature with insights in the possibility of the employment of different heuristics at the same time in the investment decision making process. Social-psychological experiments conducted by Kahneman, and others, to enlarge knowledge about heuristics all worked with narrow definitions and focussed on mechanisms. This study aims to recognize heuristics in a decision making process in a qualitative way.

(8)

8 Answering the research question increases knowledge about heuristics and the practice of investment decision making. Its relevance primarily stems from the still dominant use of neoclassical models. The notion that these models do not suit reality, and are hard to asses for their actual usefulness, caused a call for better understanding of social factors and cognitive processes in decision making theory and decision making practice itself. Furthermore, creating better understanding of heuristics and their effects on decision making in practice has been plead for as well. This study aims to extend knowledge on both cognitive processes and the influence of heuristics in the investment decision making process. Scientific knowledge focused on heuristics can improve the decision making process in practice (De Graaf, 2015). There is a need to examine actual board behaviour and decision making processes in real business settings (Van Ees, 2009).

The paper consists of different sections that work towards drawing conclusions. In the next part an overview of previous literature is presented. In the literature review the general concepts of decision making, investment decisions, heuristics and different types of heuristics will be discussed. The final part of the literature review summarizes how heuristics can be recognized in qualitative data. After the discussion on the literature, the research methodology is explained and substantiated. In the case study section, two investment decision processes that led to investments in firms after bankruptcy are reconstructed. The reconstructions were assessed for the use of heuristics by interviews, ultimately resulting in drawing conclusions from the data gathered. Limitations and suggestions for further research are discussed in the final sections of the research paper.

(9)

9

1. Literature review

The first part, the literature review, serves as an introduction to the phenomena that together provide the backbone of the research paper. The concepts of decision making, investment decisions, heuristics in general, and the specific types of heuristic that are subject to this study will be explained based on previous research. The first phenomenon discussed is decision making in the sense of how understanding of decision making processes has evolved over time, and what openings there are to expand this understanding with regard to the research topic. This part starts with the well-known argument that neoclassical models of decision making conflict with reality (Simon, 1955; Simon, 1979), and the cry for models that better represent what is actually going on in decision making practice (Argote & Greve, 2007). After the concept of decision making is clarified, the focus of the literature review shifts towards investment decisions and its underlying processes. The investment decision making process that is recognized in existing literature (Ekanem, 2005) will serve as a template to reconstruct the investment decisions that are subject to the study. The next step in the literature review is to explain what heuristics are as it may come across as quite a vague concept. In this part the concept is defined and it is illustrated how heuristics are believed to affect judgment and decision making. After the clarification of heuristics as a general concept, different types of heuristics that are believed to influence the decision making process will be further explained. These heuristics are availability, recognition, satisficing, anchoring and adjustment heuristics and the heuristic of discovery. The goal of this part of the literature review is to find distinctive characteristics that make heuristics recognizable in qualitative data. The characteristics found in the literature will be used to find evidence for the deployment of heuristics in two case investment decision making processes.

(10)

10 1.1 Decision making

To be able to understand the role of heuristics in the decision making process, it is important to delineate how understanding of decision making in general has evolved over time. This part of the literature review shows why researchers encouraged the development of decision making theory beyond the neoclassical point of view and how heuristics fit in the gap between existing models and decision making reality.

In the eighteenth and nineteenth century classical economic theory was developed that drew on a number of assumptions, including the rationality assumption for example, that simplified reality. In 1955 Herbert A. Simon proposed that classical theory with respect to the rationality assumption was in need of revision as it did not represent reality in terms of the limited capacity of human beings to process all available information. Simplifications of reality introduce discrepancies between the simplified models and reality, which in turn "serve to explain many of the phenomena of organizational behaviour" (Simon, 1955). As Andrikopoulos (2012) pointed out, assumptions in financial models lead to a transformation of reality which causes deviations from reality and unknown usability. The inability of existing models to predict in the absence of pre-existing goals in a variety of decisions is proposed as a reason for the deviation between the outcome of models and reality (Sarasvathy, 2001). De Graaf (2014) argues that this is the result of human beings tending to simplify the world into one single goal. This makes search easier but compromises reality because it is far more complex, resulting in rigorous search that is not as relevant is it is sometimes valued.

The inaccurate replications of reality included in neoclassical models, were at first attributed to violations of probability theory, but where later attributed to information processing errors (Gigerenzer, 1991). Simon (1979) explained that despite opportunities to observe how decisions were actually made in real life, theories of bounded rationality did not

(11)

11 get a foothold in the mainstream of economics because of scientist who tried to defend neoclassical theory on methodological grounds, the spread of mathematical knowledge that made classical theory more sophisticated and the one thing at a time character of human rationality. To replace classical theory, an alternative theory that was consistent with the evidence was needed. Based on a large amount of data on human problem solving and decision making, Simon stated that there could no longer be any doubt that the assumption of perfect rationality was in conflict with reality. Bounded rationality is here referred to as "the notion that decision makers in organizations experience limits in their ability to process information and solve complex problems". Summarized, decisions made are rarely perfectly rational as it is impossible or costly to gather all information.

Van Ees et al. (2009) agree with Simon that there still is a need for an alternative to the economic approach. Corporate governance and most board research are dominated by agency theory, which has led to conflicting and ambiguous empirical results. Van Ees showed that "a growing number of studies have emphasized the need to more closely study behavioural processes and dynamics in and around the boardroom to better understand conditions for effective corporate governance". The aim of van Ees' research was to establish a starting point to develop a behavioural theory of boards and corporate governance. Van Ees et al. (2009) call for the application of the key concepts of A Behavioural Theory of the Firm written by Cyert and March in 1963. These concepts include bounded rationality, satisficing behaviour, the routinization of heuristic decision-making practices and political bargaining. Satisficing and routinization will be discussed in further detail later in this paper. Despite the work of Simon (1955, 1979), Gigerenzer (1991), Van Ees et al. (2009) and other authors that questioned neoclassical assumptions, and provided evidence for the fallacies of neoclassical models, the use of out-dated models is still dominant. Much economic data is not factual, but it represents concepts that rely on figure of speech, logic and heuristics (De Graaf, 2014).

(12)

12 Sarasvathy (2001) also acknowledged March's justification that the idea of changing goals is unmanageable for normative theory of choice and the fact that some researchers have struggled to take on this problem. Sarasvathy (2001) tried to make a contribution to developing a decision model that incorporates processes of effectuation, rather than causation. To build more realistic models of decision making, elements of dynamism and contingencies should be added. The outcome is the operationalization of an abstract human aspiration (Sarasvathy, 2001), which means that a decision should have the same desirable outcome (i.e. higher turnover, increased employee engagement) in both effectuation and causation processes. This corresponds with Van Ees' (2009) arguments on satisficing based on changing aspiration levels. Another attempt to establish a better understanding of cognitive decision making processes was contemplated by Kahneman (2002). Kahneman revisited earlier work he did with Tversky that distinguished two generic modes of cognitive function. An intuitive and a controlled mode, the first contributing to judgments and decisions based on intuition and on reasoning. The view of Dietrich (2010), is in line with Kahneman and Tversky as she states that the decision making process is specific to the decision being made. Dietrich separated between simple and straight forward decisions and complex decisions that require a multi-step approach before making the decision. Evolutionary economics has used behavioural theory as a tool for developing concepts which offer an alternative to neoclassical theory, but a dominant theory is not yet developed in this field (Dosi & Marengo, 2007). In the behavioural perspective of organizations, De Graaf (2015) proposes evidence-based management is used in decision making processes. Evidence-based management is said to develop heuristics, defined as simplified cognitive models of reality (De Graaf, 2015). De Graaf suggests that evidence based management indicates that some kind of procedure is used for gathering the most suitable information for a specific decision. It is said that in practice decisions are often made based on intuition, and that scientific knowledge focused on

(13)

13 heuristics can still improve decision making processes. (De Graaf, 2015). He argues a greater understanding of decision making practices unravels how information is dealt with in practice and how different types of information are combined to make a decision.

Previous research on strategic capital investment decision making showed that the use of financial analysis techniques increased over time (Alkaraan & Northcott, 2006). It also seemed that the choice of analysis technique was independent of the type of project and that the relatively simple methods were used more often. According to Jullisson, Karlsson and Garling (2005) people tend to make financial decisions based on how committed they are to the outcome of the decision, meaning that when commitment is high, the effort put in making the decision is high as well. Alkaraan and Northcott suggest that perhaps simple is still evaluated as best in the case of risk analysis. In their interviews they found that judgment was valued higher as formal analysis. Unless non-financial criteria were considered important, the strategic criteria valued most important were closely linked to financial outcomes. It suggests that people are making decisions based on more than the numbers that are presented. “The appraisal of capital projects seems to reflect a simple is best philosophy and a commitment to the role of intuition and judgment” (Alkaraan & Northcott, 2006). This shows that intuition, which has strong connections with heuristics, plays an important role in at least the strategic dimensions of capital investments.

The literature review on decision making started with the notion that neoclassical theory infringed reality based on its assumptions. This resulted in a call for understanding behavioural decision making processes and dynamics that included bounded rationality, satisficing behaviour and the routinization of heuristics decision making practices. It is argued that much economic data relies on figure of speech, logic and heuristics. Earlier research made a distinction between intuitive and simple, and controlled and complex decision making processes. In making decisions the simplicity of the applied model, commitment to the

(14)

14 outcome and judgment and intuition are believed to have influence on the decision making process. This indicates that there is more to decision making than numbers alone, as can be seen in figure 2. Besides models themselves, simplicity of those models, intuition, judgment and commitment to outcomes influence behavioural decision making processes.

Figure 2: Factors that influence the decision making process

After extracting that the decision making process concerns more than rationally interpreting information from existing literature, the next step is to illuminate the investment decision making process in order to be able to reconstruct decisions. The reconstructions can subsequently be assed for evidence pointing in the direction of the deployment of heuristics in the decision making process.

(15)

15 1.2 Investment decisions

In this part of the literature review close attention is paid to the type of decisions that are subject in this study. The aim of this paragraph is to distinguish different stages in the investment decision making process in order to be able to frame the process of the case decisions presented. A lot of research literature is available on how investment decision making can be improved, how they should be taken, and in which situations what outcomes should be desired, but that is none of this is the purpose of this study.

Investment decisions can be very demanding in the sense of time and money consumption, especially when the choice for one of the available options excludes the ability to invest in others. Organizations, or in this case extracted towards investors, are likely to have more promising leads to invest in as they have resources available to actually engage in all of them (Edwards et al., 2007). The inability, at least for most people and firms, to engage in limitless numbers of projects and investment opportunities refrains that investment decisions are planned and thought through to great extent in order to find the opportunity that fits the expectations and demands of the investor best. Does it really work like this, or do unconscious cognitive processes affect the decision making process in a way that may lead to making suboptimal decisions? From the perspective of this study, investment decisions are decisions about the assignment of resources, financial or nonfinancial, to projects or other types of investments that are generally believed to be well advised. Or, to put it in more popular words, a management decision on where the firm’s or the investor’s resources are invested in order to provide maximum returns.

Research on the decision making in small firms has provided a simple model containing the different stages of investment decision making (Ekanem, 2005). Small firms are defined here as firms up to 50 employees and an annual turnover of GBP 10 million. The

(16)

16 firms that provided the case decisions in this study fit the description of small firms applied. The distinguished investment decision stages are displayed in figure 3.

Figure 3: Model of stages in investment decision making (Ekanem, 2005)

The model provides a framework of the key elements of investment decisions (Ekanem, 2005). After the need or opportunity is identified, relevant information is gathered from different sources to get a better view of the situation and its environment. After information is available, alternatives are evaluated and the one perceived as best is chosen. The model enables to frame the process of the case decisions and subsequently to add information derived from interviews and secondary sources of data. In the next phase of the literature review heuristics are discussed as it is important to understand what they are and how they can be recognized in the data. The actual framing of the decision making process for the case decisions will be done in the “case studies” chapter.

(17)

17 1.3 Heuristics

In the decision making paragraph of the literature review the concept of heuristics already came by a number of times. As heuristics an important role within this study, the phenomenon obviously needs to be discussed in depth. When discussing decision making, a misfit between economic models and reality was identified. Gigerenzer (1991) argued that the gap between models and reality is bridged by heuristics. In this part of the literature heuristics will be discussed to develop understanding of the concept.

One view of heuristics is that they are imperfect versions of optimal statistical procedures considered too complicated for ordinary minds to carry out, but Goldstein and Gigerenzer (2002) consider heuristics as adaptive strategies that evolved in tandem with fundamental psychological mechanisms. In 1957 Simon already identified heuristics as a product of limited information processing abilities that caused humans to construct simplified models of the world. As mentioned before, Simon’s findings and propositions were mostly ignored by researchers for decades. In 1974, Kahneman and Tversky tried to explain why people are not smart with their heuristics and biases program (Gigerenzer, 1991). Gigerenzer (1991) disagreed on this as he identified heuristics as mechanisms that try to explain things that are not there, rather than why people are not smart. Supplementing Gigerenzer, De Graaf (2014) also disagrees with the proposed definition of heuristics by Kahneman as viewing biases as the foundation of heuristics, rules out the possibility that some outcomes of decisions are a result of social processes and interactions. Sarasvathy (2001) found evidence supporting Gigerenzer’s view, suggesting that decision makers use heuristics and inductive logics to come to effective decisions. This leads to decisions making in bounded rational circumstances without the decisions being irrational. The link between heuristics and decision making was further emphazised by Nooteboom (2004) when he reffered to representativeness, availability, and anchoring and adjustment heuristics as instinct-related decision heuristics.

(18)

18 With hindsight, the identification of heuristics as a bridge between models and reality did not necessarily lead to breakthroughs in decision making theory for three reasons (Gigerenzer, 1991). Heuristics were mistakenly assumed to explain why people are not smart, explanations of heuristics were more or less descriptions of the phenomena, and heuristics were largely undefined concepts. The main goal of Kahneman and Tversky's heuristics and biases program has been to understand the cognitive processes that produce both valid and invalid judgments. Gigerenzer agreed on the goal, but not on the means to get to it. The problem with the representativeness, availability and anchoring heuristics is that there is little knowledge on how and when they work. To understand how people reason in different circumstances, models that make surprising and falsifiable predictions that reveal the mental processes explaining both valid and invalid judgment are needed (Gigerenzer, 1996). After the discussion on the purpose of heuristics, Kahneman (2002) distinguished two systems of decision making. The first system is fast, automatic, effortless, associative, and difficult to control or modify (Kahneman, 2002). This system corresponds with characteristics of heuristics as discussed in the next part of this chapter. Kahneman (2002) argues that the use of the intuitive decision making systems is cohesive with the accessibility of information stored in peoples memory and defines that what information becomes accessible in a given situation is determined by the properties of the object of judgment. This would mean that decision making heuristics are routines based on how decision makers perceive the situations linked with what they already know. Kahneman also refers to another study on intuitive decisions in which is found that experienced decision makers working under pressure rarely choose between different options, because the one they chose is the only one that came to mind. This shows that it is likely that highly accessible features will influence decisions, while low accessible features will, up to some point, be ignored. It is important to understand how this features affect decisions as the most accessible features are not guaranteed to be the most

(19)

19 relevant ones. Kahneman (2002) argues that the list of factors that influence accessibility is not readily available yet, but he does mention that similarity, changes and averages are assumed to be more accessible as probability, absolute values and sums.

When a problem rises, people tend to search for satisfactory solutions and to rely on heuristics that provide those (Van Ees, 2009). In this respect it is interesting to investigate which types of heuristics affect investment decisions in order to be able to rule out potential weaknesses in the decision making process. De Graaf (2014) questions how professionals in finance and accounting use knowledge to make a decision. De Graaf reverts to Simon's (1965) argument that heuristics are leading while making economic decisions. The difference is that science reasons based on a single set of assumptions and that heuristics can deploy multiple assumptions at the same time. Knowledge only gets valuable based on models of heuristics and a set of assumptions (de Graaf, 2014). It is crucial to understand that existing models can be useful, but that it is impossible to replicate reality with them (De Graaf, 2014). Furthermore, it is stated that the issues senior managers face require judgment instead of analysis (Billsberry and Barnik, 2010). Viewed by Dosi and Marengo (2007), managerial heuristics and diagnostic tools are the core of the dynamic capabilities of business organizations, making these concepts determinative for success.

When the decision making process is influenced by heuristics it is necessary to account for the downside of fast decisions compared to calculated decisions. Gigerenzer, Todd and the ABC Research Group have conducted research in 1999 to find out to which extend using heuristics in decision making affected the outcome of decisions. They let people make decisions based on four different strategies. These strategies were "Take the best", "The minimalist", "Multiple Regression", and "Dawe's Rule" from which the first two share characteristics with heuristics. The other two strategies mentioned by the authors are not fast/and frugal, and therefore cannot be defined as heuristics, they ask for complex

(20)

20 calculations. The authors conducted different experiments in different environments and concluded that using the speed and frugality of heuristics did not lead to a major loss in accuracy; the price of using heuristics was relatively small. Nooteboom (2004) states that heuristics in general enable people to develop expectations, explain broken expectations and attribute trustworthiness according to what is available in people’s minds, stereotypes, existing norms and recent practice. But he adds that although heuristics are rational in the adaptive sense, the downside is that they can still yield errors of myopia, prejudice and inertia. This also underlines the importance to understand how decisions are made in practice as when heuristics have a role in the decision making process, it is never guaranteed that these decisions are correct. Based on Argote and Greve (2007) and the commitments they cited from Cyert and March, this research paper is about the routinization of which heuristic affects decision making in investment decision practices. To get a better understanding of the types of heuristics discussed in this paper, they will be explained in further detail in the next section of the literature review.

Now that heuristics are identified as mechanisms that enable to make relatively quick decisions in complex situations, it is time to zoom in on different types of heuristics that are studied for their influence on investment decisions. In the heuristics chapter it was said that heuristics are there to make inferences from available information to come to a decision in an economical way, and that the speed an frugality that they serve do not necessarily lead to bad decisions or mistakes. Heuristics enable people to make decisions based on knowledge that is available, without much dedication to search for more information. In the next paragraph the type of heuristics concerned in this study will be discussed to find characteristics that make them recognizable.

(21)

21 1.4 Types of heuristics

As already mentioned in the previous section, the focus here is on a specific set of heuristics that are instinct related and have connections with decision making. The goal in this part of the literature review is to enlarge understanding of the different types of heuristics that are part of the study and to get to know the characteristics that make them identifiable. Characteristics that can be linked to specific types of heuristics will be summarized in table 1. One of the heuristics studied is distinct from the other four. Recognition, availability, satisficing and anchoring heuristics can be recognized at a given time in a given situation, while heuristics of discovery involve a process of learning and adaptation to different contexts.

1.4.1 The recognition heuristic

One of the concepts proposed by Cyert and March and substantiated by more recent research is the routinization of decision making (Van Ees, Gabrielsson and Huse, 2009). Boards operate based on knowledge or routines that are built up from past experiences. Or as Nooteboom (2004) puts it; the likelihood that an event is assessed by its similarity to stereotypes of similar occurrences. Routines are defined as "successful solutions to problems that store and reproduce experientially acquired competencies, which can then be repeated over time". This interfaces with the definition of the recognition heuristic. Goldstein and Gigerenzer (2002) argue that the recognition heuristic is probably the most frugal of all heuristics. It makes inferences from patterns of missing knowledge. The authors proposed a different program of cognitive heuristics to design and test computational models of heuristics that specify the conditions under which the recognition heuristic leads to situations in which less knowledge means more accurate inferences. First two counterintuitive consequences of the recognition heuristic are presented; the recognition heuristic predicts that a recognized object will be chosen over an unrecognized object and the second consequence is the capacity

(22)

22 of recognition, which means that the world is divided into new and known. The applicability of the recognition heuristic depends on the environment as recognition is correlated with the criterion being predicted. Examples of where the recognition heuristics can be applicable are in stock picking (alliances and competition), food choice (risk avoidance) and trust (social bonding). The defining characteristics of the recognition heuristics are search, stopping and decision; where to search for cues, when to stop searching without computing the optimal stopping point, and how to make an inference after search is stopped. These characteristics, limited search and non-optimizing stopping can be linked to the key processes in Simon's models of bounded rationality (Goldstein and Gigerenzer, 2002). The need for recognition also explains the role of cultural features, which in turn are needed for survival. People tend to be attracted towards things they are familiar with. This phenomenon was also referred to by Dietrich (2010), when someone experienced something positive from a decision, they are more likely to decide in a similar way although they may not be the best decision in the new situation. Goldstein and Gigerenzer (2002) put it in other words, when there is a given set of possible decisions and one of those is recognized by the decision maker, people tend to choose the decision that they recognize. Dietrich (2010) noted that often additional information at disposal of the decision maker is used in conjunction with the recognition heuristic. The recognition heuristic is likely to be employed when participants have to choose between different kinds of decisions, when they can only make one, or the other decision. If the decision to be made is compared to decisions made in the past, and it seems like there is a tendency towards making the same decision as was made in the past, indication for the application of the recognition heuristic is found. For example, when a firm has to make a decision about what brand machinery should be invested in the recognition heuristic would advocate for a well-known brand over a less known brand. When it is about less tacit investments the mechanism should work the same. If the characteristics or attributes of a

(23)

23 potential investment replicates those of a decision made in the past, this potential investment would be favoured over an investment with less familiar characteristics.

1.4.2 The availability heuristic

A person is considered to employ the availability heuristic when he estimates frequency or probability by the ease with which instances or associations are brought to mind (Kahneman & Tversky, 1973). Or to write it in other words, when the availability heuristic is applied, people are making a decision based on the retrieval of information that is most readily available in their minds (Redelmeier, 2005). Redelmeier also states that this is an important heuristic that is often used when making judgments and decisions. The availability heuristic is based on recall instead of recognition (Goldstein and Gigerenzer, 2002). The difference is that when the availability heuristics is applied, the attribute(s) is or are known to the decision maker. Nooteboom (2004) refers to the availability heuristic as the process in which people asses the probability and likely causes of an event by the degree to which instances of it are readily available. These events can be vivid, laden with emotion, recent, etc. (Nooteboom, 2004). This would indicate that when decisions are made, and the availability heuristic has a role in the decision making process, the decision shares characteristics with decisions or outcomes of decisions made in the past. The main difference with the recognition heuristic is that the availability heuristic induces a search and stop process until an outcome is believed to be similar to previous experiences, while the recognition heuristic refers to events that come to mind instantly. One way the availability heuristic can be interpreted is that immediate results deriving from the decision are more available as long term results (Nooteboom, 2004). This would mean that when investment decisions are made and the availability heuristic is applied, decision makers would favor short term results. An example of the application of the availability heuristic is applied is when people are presented with a list and names on this list must be identified, often the names people identify with are the ones someone is familiar with.

(24)

24 The availability heuristic is considered to have an influence when the decision maker tends to put little effort in the making of the decision and the decision is made based on what the decision maker is used to. An example of the use of the availability heuristic is when a new company car has to be bought, and the decision maker sticks to the brand that is ‘always’ bought, without considering other options. The downside of the employment of the availability heuristic is that it is affected by factors that are unrelated to the subject which potentially leads to biases.

1.4.3 The satisficing heuristic

Simon (1955) mentioned "Simple" Pay-off Functions, in which outcomes are divided in satisfactory or unsatisfactory, this corresponds with the satisficing heuristic. This requires an individual to gather far less information as search stops when an outcome that is satisfactory is found. Simon also introduced "aspiration level" as a concept that defines a satisfactory alternative which may change during search for a solution. Van Ees et al. (2009) refer to satisficing as a key concept for a behavioural theory of boards and corporate governance. The phenomenon occurs when actors accept solutions that are good enough based on current needs rather than extending search towards optimal solutions. Because of this, decisions cannot be regarded as optimal solutions to problems, but only as solutions that satisfy aspiration levels. In this case problems are regarded solved when a possible solution meets the goals that are set, or when the goals are adjusted to the level that makes one of the possible solutions sufficiently acceptable. When making a decision based on the satisficing heuristic, the goal of the decision is available up front. The decision is, like when the availability heuristic is deployed, made with little effort. The difference here is that the decision maker does not seek for what is recognized or what is available, just any decision that would satisfy the goal would be taken. Referring to the example of the company car, any car that would meet the

(25)

25 specification could be chosen within a split second. Jarvis et al. (1996) refer to this phenomenon as the decision that meets the minimum set of acceptable standards.

1.4.4 The anchoring and adjustment heuristic

“One way to make judgments under uncertainty is to anchor on information that comes to mind and adjust until a plausible estimate is reached” (Epley and Gilovich, 2006). The authors, following Tversky and Kahneman, refer to this phenomenon as the anchoring-and-adjustment heuristic, and assume that it is used when making intuitive judgments. Indications were found that adjustments from self-generated anchor values were insufficient because search for better values stopped as soon as the outcome was acceptable, unless someone was able and willing to search further. Additional to the adjustment of the aspiration level, Simon (1955) proposed that this level could change depending on the ease or difficulty to find satisfactory alternatives, which corresponds with the adjustment part of the anchoring and adjustment heuristic. It has been demonstrated that anchoring effects in the anchoring paradigm are a result of enhanced accessibility of related information instead of insufficient adjustment. People adjust from values they generate as starting point known to be incorrect but close to the target value. The use of these made up anchors serves as a judgmental heuristic by simplifying an otherwise complicated judgment, replacing a value that can be adjusted easily for an effortful assessment (Epley & Gilovich, 2006). Epley & Gilovich argue that one adjusts a possibly-sufficient amount from a given anchor, and tests whether the found value is plausible. This would mean this process goes on until a plausible value is found. This in turn means that adjustment stops toward the anchor side of a range of plausible values, a stopping rule that yields adjustment-based anchoring effects (Epley and Gilovich, 2006). The authors suggest, based on their study, that adjustment stops once a plausible estimate is reached, thus adjustment tends to be insufficient because people are often less inclined or able to continue search. On the contrary, people who did have the motivation or the ability to

(26)

26 search further, tend to adjust more from their generated anchor values. In short, Epley and Gilovich found that adjustment has a 'price' and that a person's willingness and ability to pay this price leads to a reduction in the magnitude of adjustment based anchoring biases. The effort needed for making adjustments to anchor values suggests that this bias can be reduced by encouraging to actually engaging in putting effort in effortful search. Nooteboom’s (2004) point of view was similar to Epley and Gilovich’s but he added that it is shown that people tend to stick with random anchors even when the relation to the issue is unclear, and that first impressions continued to influence the development of this relationship for a long time. The anchoring and adjustment heuristic leads people to make a decision based on a probable outcome that seems suitable for the cause at the moment, but when more information comes to the table, the suitable outcome is altered corresponding.

1.4.5 Heuristics of discovery

Compared to the first four heuristics discussed, heuristics of discovery are more dynamic instead of static in the sense that they involve processes of learning and adaptation. Nooteboom (2000) found, that the mind-set of entrepreneurs needs to be ‘cognitively distant’. This assumes that there is more than the dominant cognitive models available because the cognitive distance can potentially be modelled as well (Gavetti, 2007). The additional cognitive process or model is referred to in the literature as a heuristic of discovery, in which interpretations are critical (De Graaf, 2015). The heuristic of discovery is said to have an important role in learning and innovation (Nooteboom, 2000).

The concept of the cycle of discovery seeks to explain how exploration leads to exploitation and how exploitation may yield exploration in a step-by-step development towards radical innovation (Nooteboom, 2005). The cycle of discovery proposed by Nooteboom is based on a heuristic that indicates how there may be continuity in discontinuity and how exploration is able to emerge from exploitation. Nooteboom uses the term heuristic

(27)

27 because the cycle considers “a procedure that will generally work, while being subject to many contingencies and allowing for exceptions”. This lines up with the previously presented findings that in some cases making decisions based on heuristics, outperformed decisions based on rational calculations. Translated to decision making, the heuristic of discovery enables decision makers to transfer decisions that have proven to be successful in one context to other contexts.

The heuristic consists of different stages in an on-going cycle of consolidation, generalization, differentiation and reciprocation. The first stage enables exploitation, as where the second stage demands for the transfer of the practice or product to novel contexts, this yields exploration while maintaining exploitation. When introducing the practice in novel contexts, limits of viability and usefulness of products, production and organization which require adaptation can be encountered. This is where heuristics of discovery distinguish themselves from the heuristics discussed before. While transferring to novel contexts adaptations can be made to fit the new decision environment better.

To maintain exploitation as long as possible the adaptations first remain close to established practice, this yields differentiation in the third stage of the cycle. In the last, but not final as the cycle is said to be on-going, stage adaptations that failed in the own practice, but succeeded in a novel context, lead to experimentation with the introduction of elements of those practices into the original practice in a process of reciprocation. “This opens up a new ‘variety of content’. This is a crucial move, since it allows for experimentation with novel elements, thereby testing for their usefulness, while still maintaining exploitation” (Nooteboom, 2005). Nooteboom determined three points of the heuristic of discovery that are considered analytically crucial. It indicates that there may be continuity in the generation of discontinuity, it shows why the emergence of radical innovations are slow compared to breakthroughs and it shows how radical innovations may be non-random, based on learning,

(28)

28 while still practicing much trial and error. The link between decision making and the heuristic of discovery can also be found in the ‘principle of overconfidence’ from developmental psychology. This principle describes the instinct to apply what is successful outside the context in which it was learned. For example, to make decision based on the successful outcome of another decision, but now in a completely different context.

Now that the anchoring, availability, recognition, satisficing heuristics, and the heuristic of discovery have been identified, their individual characteristics will be presented in table 1 on the next page. This creates the opportunity to see all information presented in paragraph 1.4 in an organized scheme. The presentation allows comparison with the cases provided to see if evidence of the use of heuristics can be found.

1.5 Identification of heuristics

The literature review provided insights that show that decision making practice consist of much more than neoclassical models and assumptions. In this paragraph the heuristics that are the subject of this study will summarized in a table based on their unique characteristics. The goal here is to identify situations in which the heuristics are already known to be applied as discovered in earlier research, and to create an image on what kind of events may indicate that heuristics were applied in the case decision making processes. Based on the literature table 1 distinguishes five different themes affiliated with application of heuristics. These themes are search, stopping, effort, pitfalls and application. After the reconstruction of two investment decisions, they are held up against the information provided in table 1. The data arising from the decision process reconstructions, combined with interviews with decision makers and document reviews, provide information to see whether or not heuristics affect the decision making process.

(29)

29 Table 1: Heuristic characteristics

(30)

30

2. Research design

2.1 Philosophy

Research philosophy is partially built up from the researchers’ assumptions about the environment. These assumptions have an important role when selecting the research strategy and the data collection methods that are a part of this strategy (Saunders & Lewis, 2012). Saunders and Lewis (2012) labelled four different philosophical positions; positivism, realism, interpretivism and pragmatism, which all have their own influence on the choice of research design. The question if various heuristics affect the decision making process when it concerns investment decisions, is mostly related to the interpretive and pragmatic philosophical positions. Interpretivism relates to the study of social phenomena in their natural environment, the key is to understand the role of the research subjects from their point of view (Saunders & Lewis, 2012). This study aims to uncover organisational behaviour in the sense of whether or not decision makers tend to fall back on the use of heuristics to interpret the information available to come to a decision on investing in firms. Saunders and Lewis (2012) point out that although the choice of research method is a reflection of the researchers’ values, what is possible in practice is important as well. This is the pragmatic philosophy which suggests that the research questions and objectives are meaningful determinants for the research philosophy.

2.2 Approach

Saunders and Lewis (2012) make a distinction between two different approaches to research. The first approach, deduction, involves testing theoretical propositions by using a research strategy using a research design specifically designed for this purpose (Saunders & Lewis, 2012). The second approach, induction, considers the development of theory as a result of analysing collected data. As inductive research lays emphasis on a close understanding of the

(31)

31 research context (Saunders & Lewis, 2012), it fits the design of the study on the application of multiple heuristics in the investment decision making process. Furthermore, the study is exploratory in nature. Exploratory research focuses on finding new insights, ask new questions and assessing topics in a new light (Saunders & Lewis, 2012). The characteristics of exploratory research combine well with the inductive approach and can be quantitative or qualitative in nature. In this study qualitative research methods are being used.

2.3 Strategy

With regard to the inductive and explanatory nature of this study, and to achieve detailed understanding of the context, and the activity taking place within the context, a case study strategy is suitable for data collection (Saunders & Lewis, 2012). Saunders and Lewis proceed to recite that when the aim is to understand “why” managers make certain decisions rather than what the actual decisions were, who made these decisions and how important these decisions are perceived, a case study is a recommended research strategy. Yen (2012) contributes that when research is focused on in-depth understanding and detail, and the research approach is not considered with testing hypotheses or theories, case studies are the research strategy to use. Saunders and Lewis (2012) continue to point out that context is the key word in all case studies explaining social phenomena, and should be taken into account and described precisely. This means that with regard to the research topic, the background of the decisions made have to be explained in detail, which is done in this study by reconstructing the decision making process. Yin (2012) also adds that performing a case study would be suitable when the study is about the perspective of the study’s participants and when relatively little is known about the exact phenomena that are studied. As mentioned earlier, decision making, decision making under uncertainty, investment decisions and heuristics have been studied extensively on their own, but now these phenomena are combined. Furthermore, research on the possible application of various heuristics at the same time has not been

(32)

32 conducted yet, and making heuristics recognizable in qualitative data is a new approach as well. Case studies may consist of multiple data collection methods to triangulate multiple sources of data to improve the reliability of the study (Saunders & Lewis, 2012). Decision process reconstructions, interviews and document reviews are combined to improve reliability of the conclusions drawn in this research paper.

Now the general research strategy is shaped, the question if a single case study or a multiple case study is suitable in this remains unanswered. Saunders and Lewis (2012) refer to the research question as a basis to see what is possible in terms of practical considerations such as access to information, and time and resources available. The study is cross-sectional; it provides information about a specific setting at a specific time. As this study aims to understand if heuristics influence the process of investment decisions, a study consisting of a small number of cases can provide enough information to draw conclusions about the situations in which the study was performed. Some criticise small number case studies because it would be no basis for generalisation, but a well-designed and executed study can yield insights that would never be found in more descriptive strategies (Saunders & Lewis, 2012). The findings of Eisenhardt (1989) confirmed the view of Saunders and Lewis, case studies are capable of inducting theory, and do not have a sole purpose for doing so. Furthermore, if the initial aim of the study is to establish understanding about if a phenomenon is present or not, generalization when the phenomenon proved to be present in the particular context can be marked as a topic for future research.

2.4 Research design

Empirical research into boards and corporate governance has long been dominated by the use of archival data, whereby the behaviour of boards has been inferred from their demographic characteristics. Or, as Billsberry and Barnik (2010) put it, the current means of doing research lead to a discord as methodology asks for thorough demarcation but answering practical

(33)

33 questions does not benefit from models that are hardly connected to reality. On the contrary, critics have argued that organizational theories that would be relevant for practitioners are obvious to them (Priem & Rosenstein, 2000). If this was the case, new theories would only confirm relationships that are already understood by experienced managers. The findings of Priem and Rosenstein proved that, at least for contingency theory, it was not obvious for all types of CEO's, which makes research to better understand how decision making on organizational level works relevant. From here, a research agenda based on a behavioural framework suggests that there is a need to examine actual board behaviour and decision making processes in real business settings (Van Ees, 2009). This is endorsed by Argote and Greve (2007) who encourage the understanding of organizational decision making, requiring theory that incorporates the social processes and contextual factors that affect decisions. What is stated above, demands empirical data collection methods such as questionnaire surveys, interviews and participant observations. Van Ees referred to the work of Huse (2009) in which he stated that there are considerable challenges in gaining access to behavioural processes in and around the boardroom. This challenge is met by finding an investor who boards multiple companies and is involved in investment decision making on a regular basis. Long before Van Ees, Scapens (1990) acknowledged that conducting research by using surveys only led to a superficial view of the management accounting practice. In the literature both case studies and fieldwork are referred to as studies of management accounting in its organizational context (Scapens, 1990). Scapens identified five types of case studies which all have different characteristics and research goals. As mentioned before, this study fits best with the description of exploratory case studies as it tries to explore reasons for particular accounting practices. Scapens refers to Kaplan when he states that case studies are treated as small samples which can generally be used to develop hypotheses and construct models.

(34)

34 Case study research was believed to aim to find theoretical generalizations but, like Eisenhardt (1989) and Saunders and Lewis (2012) demonstrated, hey can also be used for developing theories and insights. When searching for evidence of heuristics in investment decision processes, it must be clear that the participants in this particular study provide an insight in how decisions are made within a specific set of circumstances. Theory will be used to explain observations, and observations will be used to modify theory (Scapens, 1990). Scapens further acknowledges that, as said when referring to Eisenhardt and Saunders; multiple case studies can be used for both replication as well as theory development. In the absence of studies that have already proven the link between various heuristics and investment decisions, this study is focused on the latter.

Later on, Humphrey and Scapens (1996) argued that the use of social theories already brought social aspects into accounting knowledge, but that it had not lead to significant insights in accounting practice in contemporary organizations. Humphrey and Scapens believe that case studies have an important role in researching the day-to-day functioning of accounting in organizations. Van Ees (2009), as already mentioned, endorses this view with regard to decision making processes in business settings. Qualitative research is effective when gathering specific information about values, opinions, behaviours and social contexts of particular populations and in-depth interviews as a part of case studies are optimal for collecting data from participants regarding personal history, perspectives and experiences. De Graaf (2014) refers to the field of finance and accounting where most practitioners seem to be driven by numbers instead of the ways in which those can be interpreted. This makes it interesting to find out how finance and accounting professionals deal with available sources of information beyond numbers to make decisions. Complementary, Hall (2010) argued that existing experimental and field-based methods could be adapted to investigate how accounting information is used in practice. He plead that a focus on micro-practices enables

(35)

35 research into which activities managers engage into with not only accounting information, but also the interaction with other information and managers. Hall (2010) concludes that the approach of making accounting information a description of organizational activities, rules out the opportunity to include managerial beliefs about the quality and relevance of information in models and theories. Using qualitative research also makes room for flexibility in the design of the study as things can be learned during the process (Mack et al, 2005). In his study on the design and methods of case study research, Yin (2009) distinguished five subsequent steps that contribute to the research design of a case study. He starts at the research question, subsequently the propositions (if there are any), then the unit(s) of analysis, logic needed for linking data to propositions, and finally the criteria that makes the researcher able to interpret the data. This research study’s aim is not to accept or decline predefined propositions, the purpose can be found in what Weick et al. (2005) described as “carving out phenomena out of the undifferentiated flux of raw experience and conceptually fixing and labelling them so that they can become the common currency for communicational exchanges”. Yin (2009) endorsed the importance of theory when conducting case study research and subsequently preached for a theoretic starting point as has been done in the previous chapters.

In the type of research described above, reliability and validity are of concern. Reliability is the extent to which evidence is independent of the person using it, and validity is the extent to which the data is true (Scapens, 1990). In 1992 Scapens cooperated with Ryan and Theobold to address a number of issues regarding the methodology of research in financial disciplines. They believe that many forms of conducting research can lead to sensible outcomes as long as rational debate and enquiry and the sensible use of evidence are given enough attention (Ryan et al, 1992). In the research paper Yin wrote in 2009, he also distinguished different types of validity. The first is construct validity, the degree in which a

(36)

36 study actually investigates what it claims, can be obtained by using multiple sources of evidence enabling triangulation. The second type, internal validity, will be established in this study by matching patterns in cases (different decisions investigated). The last type of validity is the external type, which considers to which extend the findings of the study can be generalised toward other contexts. This research paper considers a couple of cases. When patterns of the decision making process for decisions with the same characteristics show similarities, the findings would be valid for other investment decisions that are made in a similar context. Besides on validity, Yin (2009) also addresses reliability as an important part of qualitative research. As reliability is the extent to which evidence is independent of the person using it, transparency of the research steps is crucial. To make sure that another researcher would come to the same conclusions, and to rule out differences in interpretation, steps taken to come to conclusions are to be described in detail. Vermeulen (2005) refers to this phenomenon as rigor, which can be achieved in the research method applied. Vermeulen (2005) also argues that the relevance of a study should be incorporated in the research question it tries to answer. The question if the types of heuristics included in this study influence decision makers in the process of making investment decisions, potentially makes decisions makers more aware of how decisions are made. When more of the hidden processes in decision making are unrevealed, investment decision making can be improved by taking into account these processes. As a product of their discussion on the impact of Cyert and March's A Behavioral Theory of the Firm, Argote and Greve (2007) refer to the commitments Cyert and March proposed that are important for current research. The commitments include a focus on a small number of key economic decisions, the development of process oriented models of the firm, linking these models to empirical observations and finally the development of a theory that can be generalized beyond the subjects studied. The research design answers to the first three of these commitments. The focus here is on investment

(37)

37 decisions and how these decisions are made, rather than the outcome, and is based on multiple sources of information that are directly related to practice.

2.5 Methodology

The methodology for finding out whether or not one or more of the proposed heuristics have influence in the investment decision making process is considered best to be mixed-method. Different methods are applied in different stages of the research process. The main reason for choosing mixed method is to enable for triangulation, but there are more benefits. Some data collection methods are more suitable for finding a specific type of information as others, it enables to focus on different aspects of a study, findings can be confirmed by more than one type of data and qualitative methods can be used to interpret quantitative data (Saunders & Lewis, 2012). This chapter is meant to distinguish the different steps in this study, and to connect these steps with methods of data collection.

2.5.1 Reconstruction of the decision making process

The first step in the research methodology is to reconstruct the decision making process of two decisions to invest in firms after they faced bankruptcy. The decisions are made by the same investor in order to be able to find recurring steps in the process. The decisions, as they comprise of investments in new businesses, are considered investment decisions at the corporate level of the firm. The decision reconstructions are created by interviewing the decision maker aiming to build up a timeline of events that eventually led to the final decision of investing in the firms. The starting point for the reconstructions is the emergence of the opportunity, followed by what information was gathered to subsequently asses the opportunity, and what was considered decisive in the outcome of the investment decision making process.

(38)

38 2.5.2 In-depth interviews

The next steps in the research process are conducting another interview with the investor/decision maker and conducting archival research in the sense of document reviews. Interviewing and document reviewing was not necessarily conducted in fixed order. The latter will be discussed in section 2.5.3. The focus of the second interview differs from the first as the aim is to find evidence for the use of heuristics in the decision making process based on the search, stop and application characteristics identified in the literature review. It tries to derive the cognitive processes that enabled to make inferences from the information available at the time the decision was made. The question is, what information was (perceived) decisive, and why? At the time the second interview was held, the decision maker was still unaware about heuristics and how this study tries to recognize their application in the data. This rules out the possibility for the participant to give answers that influence the outcome predetermined way. The interviews were held at the office of one of the firms the investor owns and were informal in nature. The interview was semi-structured and can be found in the appendices section of the paper.

2.5.3 Archival research

As a source of secondary data a document review is executed. This stage is meant to see if the data gathered from the interviews and decision process reconstructions can be validated by documents that served the decision making process, or the other way around. For this study one can think of emails, inventory lists, due diligence reports, bankruptcy reports and bid books. This means that the decisions considered are being examined beyond how the decision maker perceived information that was available in the decision making process.

2.6 Summarizing the research design

After elaborating on the choices made in the design of the study, an overview is given in this paragraph. The study includes case study research to incorporate contextual factors that affect

(39)

39 decision making practice. Validity is achieved by using different sources of information, multiple cases and thorough description of the study’s context which enables generalization in similar contexts. Reliability lies in transparency in the steps taken to come to conclusions. On the next page a model of the research design is displayed to give an overview of the cases, data sources and result section at the end of the study.

Figure 4: Overview of the research design

Context is shaped by profiling the companies that are subject to the decisions themselves, or where the decisions were made within. This is extended by reconstructing the case decisions that give an insight in the decision making processes involved. Investment decision reconstructions, combined with interviews of decision makers and document reviews constitute the total scope of date. Findings from the literature review will be used to explain observations in order to be able to draw conclusions on what evidence can be found in the data that endorses the use of heuristics. After conclusions are drawn, suggestions and implications for future research are discussed.

(40)

40

3. Case studies

In the following the case study decisions will be discussed based on the framework that has been provided in earlier chapters. First the companies that were subject in the decision making process are presented to create an image of their activities and size. Afterwards the decisions are framed in accordance with the model presented in figure 3 on page 15, this provides insights in different stages of the decision making process. After the decisions are reconstructed insights into what information was crucial in the process are obtained. In the third phase the decision reconstructions, combined with the heuristic characteristics identified, will serve as a basis to conduct in-depth interviews. The interviews will provide insights into the ways of thinking of the decision maker. This will, in combination with secondary sources of data, provide data to asses for the use of heuristics in the decision making process. In the last part of the case studies chapter differences and similarities across decisions are discussed.

3.1 Profile case study companies

The case study decisions were made in two different limited companies. The first company, referred to as “Company I” in table 2, is mainly involved in manufacturing steel structures for utility construction and a wide range of steel parts for machines and machine supporting frames. The company was acquired in September 2012 and had an annual turnover of approximately EUR 2.8 million in 2013. The turnover grew relatively fast in a slow market towards approximately EUR 4.0 million in 2015. At first, the firm adopted a cost leadership strategy, and it tried to serve the market as a production service utility for firms in the construction business. Because of the massive downturn in the construction market, the firm was forced to compete in the construction business itself. In order to dodge the severe competition in the construction market, the company recently upgraded certifications to be able compete in markets with higher quality requirements and subsequent better margins. The new target markets entail machine part manufacturing. The main target for the next few years

(41)

41 is to establish a sustainable market position in the new target markets. Furthermore, turnover levels are aimed to be maintained as improving margins is perceived most important now.

The second company was added to the investor’s portfolio more recently in October 2014. It is divided in two different operating companies that serve their own markets. One is dedicated to building parts for ultrasonic flow measurement devices for a single client. The other is involved in manufacturing parts for food processing companies, and various parts for the oil and gas industry. The investor deliberately chose to build up the company starting from a modest setting because administrative procedures were insufficiently kept by the previous owner as will be quoted in the next section. With the current capacity, a combined turnover exceeding EUR 1.0 million should be achievable for the firm.

Table 2: Profile of case study firms

Similarities between the two firms are that they are project oriented and metals processing. Company I is merely processing steel, but Company II is also able to process high alloy and more exotic kinds of metal. The main difference between the firms is the markets in which they operate and the profitability and quality standards within those markets. The employee-turnover ratio displays gross margins achievable in the relevant markets, and provides a superficial but veracious indication of what separates both firms.

Multiple heuristics in the investment decision making process

Table of contents

Abstract

Introduction

1. Literature review

2. Research design

3. Case studies