2. Literature Review
For a critical analysis of the impact of loss aversion and availability error on human decision-making in projects, it is necessary to clarify some fundamental concepts for a better understanding of the research topic; these are discussed below;
Project is a vague word defined differently by many researchers (Maylor, 2010). The concept “Project” lacks a universal and comprehensive definition (Reiss, 2007). Gardiner (2005) defines a project as a transitory and exceptional activity that utilise scarce resources to generate benefits for stakeholders amid uncertainty and complexity. A project is a temporal alliance that deals with distributing capitals to prospects that are capable of creating a positive change (Turner, 2014) Similarly, a project is a short time venture embarked on to attain a valuable outcome, product or services (Project Management Institute 2008) (Association of Project Managers 2012).
2.2 Project Management.
Project Management has no universally accepted definition and it is a relatively new genre of study (Pellegrinelli, 2011). According to Kerzner (2013), project management is the actualisation of a business’s objective and goals through planning, directing. Organizing and adequate management of accessible resources. The Association of Project Management (2012) states that project management is the use of processes, systems, information, skills and experience for the accomplishment of project goals.
2.3 Project Success
Project success is a concept in project management that is hard to define due to the uncertainty of the factors that necessitates project success (Papke-Sheilds, Beise and Quan, 2010). Apparently, the conventional yardstick of project success based on the Iron triangle’s criteria of cost, time and quality; which appears unreasonable and rigid is of a lesser priority and a more realistic modern approach of measuring success; which entails the ability of a project to create value and stakeholders’ satisfaction; is now the criteria for measurement of project success. (Muller and Jugdev, 2012). According to Stingl and Geraldi (2017), one of the essential element for project success in the management of project is decision making, which considers the most appropriate strategies for the pre- conception to post- completion phases of the project.
2.4 Decision Making
Decision-making is the powerhouse of every project; decisions made at pre-conception, in-project and post -project stages of the project, defines the ultimate success of the project (Stingl and Geraldi 2017). The Business Dictionary (2018) defines decision-making as the logical selection of the most appropriate option from available alternatives. Similarly, Merriam Webster Dictionary (2018) states that decision-making is the act of making decision particularly with a group of individuals. Tiwary (2013) describes decision-making as the method or strategy adopted by an enterprise to actualise set goals. Reese & Rodeheaver (1985, cited in McFall, 2015, p7) suggest that decision- making deals with the core processes, which a decision or indecision is made from competing alternatives, and might be or might not be attributed to behaviour. Inaction, like resisting responding or dodging a stimulus can become a chosen alternative for execution and likewise behaviour may start from the decision-making process despite the absence of alternatives to consider; In this instance, the behaviour seems simple and automatic (McFall, 2015). Redish (2013) portrays these apparently automatic behaviours as decisions, even though the decisions are reached through less cognitive effort , also known as reflex actions or heuristics. Furthermore, Redish contends that reflex actions and automatic responses have the potentials of been altered through cognitive system, an illustration of this can be seen in an individual that places a palm on a hot burner and resists the urge to remove it because of an expected reward.
2.5 Historical Perspective of Decision Making
Decision-making is an act as old as humankind and the ancestors of modern humans made daily decisions based on interpretations of dreams, smokes, divinations and oracles (Buchanan and O’Connell, 2018). According to Gigerenzer (2011), modern decision-making dates back to the seventeen century; when Descartes and Pointcarre invented the first calculus of decision-making. Buchanan and O’Connell (2018) attributes the popularity of modern decision- making to Chester Barnard in the middle of the twentieth century; for importing the terminology “ decision-making” which was mainly a public administration concept to the business sector to substitute restrictive narratives like policy making and resource allocation. William Starbuck, a professor in Oregon University acknowledges the positive impact of Chester Barnard’s introduction of decision- making on managers by explaining that policy-making and resource allocation are never ending acts, while decision denotes the conclusion of a discussion and start of an action plan (Buchanan and O’Connell, 2018). In addition, Gigerenzer (2011) suggests that the contemporary view of decision-making involves the use of heuristics and human information processing; which is the revolutionary work of Herbert Simon. Heuristics are mental short cuts, cognitive tools and rules of thumb developed through experiences, to enable individuals make judgements and arrive at decisions quickly (Gigerenzer and Gaissmaier, 2011).
2.6 Decision theory
Decision theory is a divergent field because of the different perceptions held by researchers about decisions (Hansson 2005). Decision theory also known as the theory of choice is the study of the rationale behind the choices made by an agent (Stanford Encyclopaedia of Philosophy, 2015). Decision theory deals with goal oriented behaviour in the presence of alternatives (Hansson, 2005). Decision theory can be broken into three branches namely; normative, descriptive and prescriptive branch (Vareman, 2008). Normative theory deals with how to make accurate decisions in a scenario of uncertainty and values, descriptive theory, examines the possibility of imperfect individuals making decision and prescriptive theory is a combination of descriptive and normative theories to achieve the best decision at any given situation (Vareman, 2008). However, there is no universal agreement on a standardized classification on the theories and therefore many researchers have classified the theories as either rational or non-rational (Gigerenezer, 2001; Hansson, 2005; Oliveira, 2007). In differentiating the rational from non-rational theory, Gigerenezer (2001) identified four attributes for rational theories as Optimization, normative, omniscience and internal consistency. In the same vein, non-rational theories are identifiable to posses attributes such as non-optimization, descriptive, search, ecological rationality and cognitive building blocks like emotions, imitation, and social norms. According to Ahmed et al. ( 2014 ),some of the theories that have gained popularity in the context of decision-making are as follows; the “Subjective Expected Utility Theory” by Savage in 1954; which suggest that a decision maker chooses between alternatives (or strategies) in the presence of risk. Savage capitalized on the assumption that the decision maker will always tend to seek pleasure and avoid pain. However, Slovic and Tversky (1974) demonstrated that people do not believe in Savage axioms because of the limitation of this theory which is the assumption that the decision maker will seek to reach well-reasoned decisions based on consideration of all possible known alternatives i.e., decision maker is always rational. However, human decision-making is more complex and can be irrational (Stanovich, 2011). Furthermore, in other to correct the inherent limitations of the subjective expected utility theory, Kahneman and Tversky (1979) came up with the “Prospect Theory” which precisely describes how people actually go about making their decisions and the theory predicts that decision makers tend to be risk averse in a domain of gain. Similarly, people lean more towards the outcomes obtained with certainty than those obtained by mere probabilities and although the prospect theory overcomes the paradox of choice stemming from subjective expected utility theory, it still has a number of limitations such as the axiomatic basis of prospect theory that presents a challenge during validation (Ahmed et al., 2014). When reference points are changed, the same vague conclusions might stimulate different perceptions of gains or losses and consequently because of these dissimilar perceptions, choices might be more difficult to predict (Larichev, 1999; Oliveira, 2007). Furthermore, Simon (1957) advanced the concept of “bounded rationality” where the decision-maker has limited information, time and intellectual ability to make a decision. Instead, the decision-maker work with limited and simplified knowledge, to reach acceptable, compromise choices (‘satisficing’), rather than pursue ‘maximizing’ or ‘optimizing’ strategies in which one particular objective is fully achieved (Marshall, 1998). The word “satisficing” goes contrary to the notion of optimization and optimization does not exist in real world; instead, we have ‘good enough’ alternatives (Simon, 1957). Williams (2002) analyse the concept of bounded rationality and states that the search for the best solution may be indefinite and one will not wait for eternity hoping to find a solution that just fits and completely covers all the areas. The more information searched, the higher the collective cost; but cost minimization is limited up to the point of discovery of a compromise (Ahmed et al., 2014). An individual interested in the purchase of a set of upholstery chairs would rather do research on few manufacturers and then settle for an acceptable product with a good price than travelling the length and breadth of a large market to obtain the best price and quality (Ahmed et al.,2014). “The rational choice theory” focusses on how people reach decisions based on their ability to take full advantage of their own benefits; through individuals’ socially constructed reality, the rational choice can be predicted across an aggregate pattern of others choices, such that the act of decision-making is consistent in choosing the self-determined best choice of action (Tversky and Kahneman, 1996). Critics of rational choice theory say the model makes assumptions that may be unrealistic because often people have an imperfect data set of relevant information. Another criticism of the rational choice theory is that it denies the existence of any other kind of action other considered as purely rational choice (Browning 2000). “The classic model “for understanding decision making in terms of choice between options is that humans combine all the alternatives in order to reach a ‘logical’ conclusion (Taylor, 2012b). Likewise, The classic decision making model emerged because of the rationality assumptions that assumes the intelligence activity, design activity and choice activity are the conditions that are certain in the classical decision-making framework (Nichols, 2005), (Li, 2008). In addition, Hucaynski and Buchanan (2002) compares the rationality concept with “scientific reasoning, empiricism and positivism; which utilises decision criteria of evidence, logical arguments and reasoning. This classical model is the cradle of the rational-analytic approach to decision making with minor variations, which makes it less difficult to understand, and appeals to the belief in rationality (Ahmed et al., 2014). Despite the popularity of the classical model among managers, it does not reflect the reality of decision making since it assumes the causal linkages are predictable and it does not represent how people make decisions in the organization (Robbins & Coulter, 2003). Similarly, Nichols (2005) confirms that, the classical mode does not reflect the iterative nature of developing clarity, formulating a viable course of action and developing commitment to that course of action. Furthermore, classical model lacks the political aspects of decision-making, and disregards intuition or instinct (Nichols, 2005). “The military model “is an intervention made by the U. S. Army War College and is a variation of the classic model; which begins with settings the objectives of the decision-making, development, evaluation and choosing the best alternative (Ahmed et al., 2014). In addition, the military model dictates the organizational goals and objectives as a driving force in decision-making and emphasizes the importance of execution or following through to make the decision happen (Nichols 2005). Unfortunately, the military model model encounters the same limitations as the classic model; it is static, unrealistic and does not accommodate the dynamism of the problem situation because it ignores other aspects of decision-making such as, politics, intuition, consensus and the ability to spot pattern within the decision-making framework (Ahmed et al., 2014). Kurtz and Snowden (2003) developed a more innovative non-sequential decision making model; known as “Cynefin” or habitat; which means an evolutionary perspective of complex systems characterized with uncertainty and this model attracts research from various disciplines ranging from complex adaptive systems theory, cognitive science, anthropology and evolutionary psychology. Kurtz and Snowden (2003) also mentioned that cynetin is concerned with how people perceive and make sense of situations in order to make decisions and the objective of the framework is to reach consensus that reduces the unknown domain. Nichols (2005) confirms that the cynefin model tend to proffers solutions to problems encountered by decision makers, reflects on management theory, thinking and practice. However, cynefin model challenges some basic assumptions, such as, certainty and perfection on earth, absolute rationality of individuals, and that all actions are results of underlying intentions and that situations are never circumstantial (Nichols, 2005). “The Multi-Criteria Decision Analysis” model uses procedures that analyze complex decisions based on distinct, conflicting criteria ; which samples the most preferred to the least preferred option; this model consists of a series of techniques such as weighted summation, concordance and analysis that facilitate the scoring, ranking, or weighting of decision-making criteria based on stakeholder preferences (Suedel et al, 2011). These techniques ideally operate within a transparent framework that encourages informed decision-making by providing opportunities for genuine, substantive participation in decision-making and the best available scientific knowledge that recognises uncertainties in an honest, rigorous and consistent manner (Suedel et al., 2011). Montibeller and Franco (2010) initially proposed the use of multi-criteria decision analysis for strategic decision-making and pioneered its implementation in the context of strategy workshops and later within organizations. Similarly, Ram et al, (2011) proposed a six-step framework that used multi- criteria decision analysis in the evaluation of strategic options. Montibeller & Franco (2011) suggest that in multi-criteria decision analysis, the alternatives have scores based on stipulated criteria normally on an interval or ratio scales. Thereafter, assignments of weights to the criteria and computation with an appropriate process is necessary to account for value or utility functions, goal programming, outranking or descriptive/multivariate statistical methods to determine the rank of the alternatives (Ram et al., 2011). However, one of the greatest challenges associated with multi-criteria decision analysis is how to compare and combine different metrics (Suedel et al., 2011). The multi-criteria analysis model have other limitations like the problems of multi-criteria choice, multi-criteria ranking and multi-criteria sorting (Vassilev, Genova, & Vassileva, 2005). The problem of choice essentially entails finding the relevant technique among the various methods in application and this breeds the classification problem where there is no universal agreement on a standard approach (Vassilev, Genova, & Vassileva, 2005).
2.7 Risk Management
Risk is an inevitable part of decisions made; the risks associated with the daily choices of individuals are apparently trivial compared to those made in the corporate world where the implications of the opportunity cost or forgone alternative could be catastrophic to a project if not properly managed (Buchanan and O’Connell, 2018). Risks are developments and occurrences that can cause a reduction of project objectives (Schutt Figg, 2013). Risk management is an essential element of project success; it entails decisions involving risk perception, risk assessment, and risk mitigation because projects are prone to uncertainty that disrupts the progress of projects (Carvalho & Rabechini Junior, 2015). Consequently, poor risk perception and unproductive risk management methods used at the different phases of the project are valid reasons for the increase in unsuccessful project; organizations should have the expertise to perceive, mitigate the attendant risks, in other to make better choices in the management of projects (Carvalho & Rabechini Junior, 2015).
The word rationality is an ambiguous terminology and there is no generally acceptable answer or criteria for what is rational (Osbeck, 2009).Therefore, the current literature on rationality presents divergent views on rationality by various researchers (Mele and Rawling 2014). According to Stanovich (2011), the lexicon definition of rationality implies the quality of being in tune with reason and this notion became popular by Aristotle’s assumption of man being a rational animal. In reality, rationality and reason are words used interchangeably, to describe mental capacity, faculty, or power (Audi, 2004) (Korsgaard, 2009). Furthermore, Hodgkinson & Healey (2011) defines rationality a systematic, analytic, rule-based, and explicit mechanism for decision-making. (Individuals preferring rationality follow a step-by-step decision-making process, which includes identifying and formulating the problem, thoroughly assessing pertinent information, generating a set of alternatives, evaluating the costs and benefits of these alternatives, and ultimately making a logical choice based on conscious deliberation (Elbanna, 2006; Janis & Mann, 1977; Schwenk, 1984). Given its systematic and structured nature, rational decision making can be slow, time-consuming, and effortful, and thus not always appropriate to deal with the time pressure, complexity, and uncertainty of innovative decision-making (Dane & Pratt, 2007). Despite the general acceptance of humans as rational animals, human choices most times appears irrational because these choices tend to deviate from the acceptable norms in the society because human decisions of irrelevant information (Kahneman, 2011). Likewise, Individuals succumb to circumstantial variables and tend to rationalize wrong decisions (Danziger et al., 2011) (Harmon-Jones & Mills, 1999). Besides, a great number of these irrational biases starts swiftly, easily, and without human consciousness, which implies that even though people are aware of these biases, the awareness never stops the bias (Santos & Gendler 2014)
According to Taylor (2016), prior to‘ modern society’ and the ‘information age’, people had to consider a myriad of social and contextual factors in making judgements about how best to ensure that they were fed and clothed, and avoided being harmed by wild animals, other humans and accidents. Most times these judgements require feasible ways called heuristics (Mental shortcuts) to process the most essential information fast to make rapid daily judgements (Chow, 2015). In addition, Arrington (2013) affirms that when individuals engage in decision-making or judgment it is often necessary to use heuristics to help process the information that they encounter. Heuristics are generally characterized and synonymous with rules of thumb (Chow, 2015). Veermans, van Joolingen, & de Jong (2006) view heuristics as rules of thumb individuals use to make decisions across a range of circumstances. Likewise, Abel (2003) asserts that the rules of thumb that are heuristics are cognitive frameworks developed through experience and implemented during problem solving. The perspective of Gigerenzer and Gaissmaier, (2015), infer heuristic as a strategy that ignores part of the information with the goal of making decisions more accurately, quickly and frugally (i.e. with fewer pieces of information) compared to more complex methods. Consequently, heuristic models of decision-making principle relies on the notion that human beings—including professionals—may act rationally even if they do not weigh up all the factors according to the logic of an expected utility model. Rather, the rationality may consist of selecting and using simple decision rules related to particular types of social environment or problems that are faced (Brighton and Gigerenzer, 2015). On the contrary, Dunbar (1998) and Fodor (2008) mention that heuristics are procedures that can produce good outcomes but incapable of appropriate solutions. Ashcraft (2002), further mentions that the feature of a heuristic approach to problem solving is partial effectiveness of solutions; it is unlikely to proffer correct answers to problems all the time. This negative perception of heuristics held by some researcher results from the fact that there is consequently an adverse effect for taking shortcuts or cutting corners (Chow, 2015). Furthermore, heuristics may prove disadvantageous in decision-making when the settings necessitate analytical and extended reasoning rather than the quicker pace of heuristics (De Neys, 2006). However, on a neutral point of view, Griffin and Kahneman (2003) emphasises that heuristics are multifaceted cognitive mechanisms that allow individuals successfully process large amounts of information, which might result in error occasionally but these error would not reduce the benefits achieved from the use of heuristics.
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