The critical point--the psychology of project termination
Much research has been done on the reasons for project failure but despite this many companies still find it difficult to terminate failing projects. In this research, optimism about the business benefit of the completed project deliverable as an escalation driver is investigated. Prior research attributed the inability of decision makers to terminate failing projects to a number of mostly psychological reasons. Optimism about business benefit is a largely unexplored reason for escalation of commitment and this research shows strong support for this notion.
A large body of research exists on Escalation of Commitment (EoC) to a failing course of action (Bobocel & Meyer, 1994; Fox & Staw, 1979; Ross & Staw, 1993; Staw, 1976, 1997). Most of this research investigates the psychological determinants that affect the decision maker. More than 30 determinants have been identified, but the determinants that are most commonly associated with EoC are sunk cost, project completion, self-justification, and optimism bias.
Sunk Cost Effect
Sunk cost in the context of projects is defined as: “a cost that has already been incurred and which should not be considered in making a new investment decision” (Amos, 2007, p. A.11). Northcraft and Wolf (1984) posit that most psychological research into sunk cost focused on situations in which explicit information about the sunk costs and negative financial situation of the project is available, but revenue information of the project is not. What appears to be throwing good money after bad could in fact be a good investment.
Arkes and Blumer (1985) present a number of experiments to show the propensity of subjects to favor investments with a high sunk cost compared with investments with a lower sunk cost. This notion is supported by Whyte (1986, 1993) who specifically notes the roles of decision framing in escalation.
From this research we find strong evidence that there is a correlation between the willingness to spend more on a project that will in all likelihood not make any money for the organization as the sunk cost increases.
Staw (1976) investigates the effect of personal responsibility on EoC in situations in which a decision maker had a choice between alternatives and has, at a later stage, the opportunity to make further decisions to influence the situation (i.e., the decision maker can allocate additional resources or withhold resources). He argues that a decision maker may, instead of changing his or her behavior, cognitively distort the negative consequences of a prior decision to make it appear to be a rational and favorable decision.
The decision maker should, however, have committed to a situation that cannot be easily changed (Brehm & Cohen, 1962) and feel that he or she would be held personally responsible for the negative consequences of the decision (Carlsmith & Freedman, 1968; Cooper, 1971).
In an analysis of research results of EoC prior to 1992, Brockner (1992) concludes that self-justification is one of the primary reasons for EoC.
In the existing research, there is a strong argument that decision makers will primarily consider their own prior decisions when deciding to invest more resources in a failing project.
Conlon and Garland (1993) argue that project completion and sunk cost are cofounded and that prior research may have over emphasized the effect of sunk cost as a reason for EoC. Research by Garland, Conlon, and Rogers (1990) shows that the opposite of the sunk cost effect may occur (i.e., less is invested as the incremental cost of the project increases). On a typical project, the amount of money spent is correlated to the progression of the available time to complete the project. This relationship is not linear, and graphing the incremental project cost over time typically takes the form of an S-curve (Project Management Institute, 2013). A decision maker would usually be aware of the time progress of the project when decisions are made and the approaching end of the project. The approaching end of the project could entrap the decision maker and may lead to goal substitution, whereby decision makers shift their focus away from the goal the project has to achieve in terms of its deliverables to the goal of just completing the project.
Jensen, Conlon, Humphrey, and Moon (2011) posit that decision makers will increasingly conceal negative information about a project as the project approaches completion.
Optimism and Illusion of Control
Humans have a tendency to be overly positive when we evaluate ourselves, and overstating our own ability to master or control situations appears to be part of our normal thought processes (Lovallo & Kahneman, 2003; Tali Sharot et al., 2012; Taylor & Brown, 1988, 1994).
The effect of this behavior on projects is that a decision maker may select a project based on an overestimation of the value that the project will add to the organization or an overestimation of their ability to influence the outcome of the project (Bazerman & Samuelson, 1983). Once the project is selected, the decision maker may feel that he or she can control the outcome of a failing project in a positive way, even if all the supporting evidence shows otherwise, due to the illusion of control.
Sharot (2012), a neuroscientist, specifically shows that interfering with the physical functioning of specific areas of the brain through Transcranial Magnetic Stimulation decreases optimism bias, suggesting that optimism bias is a biological attribute of humans.
Conceptual Framework and Hypotheses
Projects that are late and/or over-budget are not by default candidates for termination. There is a real space between the point where the project starts to exceed its planned investment and the point where the business benefits are eroded by the investment in the project. There is also a perceived space between the point where the project starts to exceed its planned investment and the point where a decision maker will actually decide to terminate the project. This space is a major dilemma for decision makers, since waiting for a project's investment to erode all the forecast business benefit before deciding to terminate the project is not an optimal decision. A decision maker would prefer to terminate the project once he or she is certain that the business benefit, or some threshold towards that benefit, will be eroded.
This situation is further illustrated through the definition of the following variables and equations. The following assumptions are made:
a. Project success is measured as the Return on Investment (ROI) from a project and not only the project's achievement of its time, cost, and quality objectives; unless the achievement of these objectives is directly tied to the ROI of the project (Meyer, 2012). ROI is calculated as follows:
b. Decision makers consider the achievement of business benefits in the form of ROI when evaluating the feasibility of terminating a project.
Bp = the planned business benefit from the project's product at the project's start.
Ba = the actual business benefit from the project's product after project completion.
Be = the estimated business benefit from the project during project implementation.
I p = planned (budgeted) investment at the end of the project to develop the product.
I pt = planned investment in the project at any particular time in the project.
I a = actual project investment at the end of the project.
Iat = actual project investment at a particular point in time before the end of the project.
Ie = the estimated total investment at the end of the project.
These variables all have the same unit of measurement, which is deliberately not defined in monetary terms since both the investment and the business benefit could have a non-monetary value for its stakeholders.
At the start of a project Iat = 0 and as the project progresses Iat increases according the planned investment in the project. On a perfectly planned and executed project Iat = Ipt at any stage during the project's life and at the end of the project the business benefit Bp will be realized.
If Iat > Ipt at any stage of the project it could be the first indication that the investment in the project will be more than what was planned; however, the fact that Iat > Ipt does not mean that actual investment (Ia) in the project will exceed the planned investment (Ip) and Ia could be more, less, or equal to Ip.
Project decision makers can continuously re-estimate the total investment for a project (Ie) and determine the business risk and ROI. This is possible by using information about the project's performance received by the project manager and the project management team. Both these parameters significantly influence the decision of executives to select a particular project (Meyer, 2012). Earned value management (EVM) techniques are commonly used to report on past project performance and to forecast future project performance (Anbari, 2003; Lipke, Zwikael, Henderson, & Anbari, 2009); in this particular case, the EVM parameter estimate at completion (EAC) would be used as Ie.
Therefore, if Ip < Ie < Bp the decision makers know that they will invest more in the project than originally planned, but this is not yet a bad investment since the estimated investment does not exceed the planned benefit. Only once Ie ≥ Bp would the decision maker know with certainty that the project will not be a success and should be terminated. This is, however, not the only cut-off point but the definitive point where decision makers know the best option is to terminate the project. As the project approaches this point, there may be other projects in the investors' portfolios that would be better investments, which may be a good reason for terminating the project before this point is reached.
Decision makers can also revise their estimates of the business benefits (Be) from the project due to changes in market conditions. The planned business benefit, Bp, is calculated before the project starts. The estimated benefit of the project, Be, changes over the life of the project and could increase or decrease as the project progresses.
The decision makers' challenge is that both Ie and Be are estimates that are subjectively adjusted, depending on the biases of the estimator.
There are two important definitions from this analysis:
a. We refer to the point where Be = Ie as the Critical Point (CP). This is the point where the decision maker realizes that the investment will have no return or a negative return.
b. We refer to the area where Ip < Ie < Be as the Cone of Confusion, because this is where decision makers grapple with the uncertainties of project termination. See Exhibit 1.
Decision makers are now dealing with the originally planned ROI (ROIp) and the revised estimated ROI (ROIe). From these values we can calculate the Criticality Index (CI):
When CI = 1 the project is on track to meet its investment and business benefit targets, if CI < 1, ROIp will exceed ROIe, and if CI > 1 the ROIe will exceed ROIp. It is clear that when CI > 1 the decision makers expect to get a better return from the project than originally planned. Note that since Ie and Be are subjective, CI is also subjective.
Measuring Investments and Benefits
The calculation of ROI is quite simple if the investment is measured in monetary terms. The reality is, however, that investments and benefits could be measured in non-monetary terms. In order to get to a sensible assessment of investment and return, both the investment and benefits should be measured in a way that makes it possible to compare them. To this extent we define that resources are required for an investment and the unit of measure for any resource that contributes to the investment as:
I = wr
I = investment
w = relative weight of the contribution of resource r to the investment.
r = any resource that contributes to the investment.
Since we have multiple resources that contribute to the investment, each with its own relative weight we have:
Benefits are reflected in the gains derived from the project. Some of the gains may be resources that can be reinvested in new projects, but not all the gains are in the forms of resources. For example, increased morale from a successful project cannot be reinvested in a project to produce benefits. We define the business benefit as:
B = benefit.
w = relative weight of the contribution of gain g to the benefit.
g = any gain that contribute to the benefit.
An investment and a benefit could be measured in the same unit but the weight assigned to a resource (r) for an investment may differ from the weight assigned to a gain of the same unit when including it in the benefit of the same project.
Once decision makers move into the Cone of Confusion they continuously evaluate the estimated investment in the project and the estimated benefits. The nature of projects is such that these two parameters cannot be continuously re-measured due to the estimation effort that is required to re-estimate. The assessment of the parameters is therefore based on the subjective evaluation of the decision maker.
In their ongoing assessment of the benefits (Be) and the investment (Ie) the decision makers could make three types of adjustments:
a. An adjustment to the weight assigned to the resource or gain;
b. An adjustment in the magnitude of the resource or gain; and
c. The inclusion of new gains or resources or the exclusion of some resources or gains.
This assessment by the decision maker could have the following outcomes:
a. The assessed investment could increase/decrease;
b. The assessed benefit could increase/decrease; or
c. Both the investment and benefit could increase/decrease simultaneously.
The assessment by the decision maker is affected by the psychological determinants and the assessments are prospects that behave in the manner described by prospect theory (Kahneman & Tversky, 1979) and for a portfolio of projects according to cumulative prospect theory (Tversky & Kahneman, 1992).
Past research has not conclusively excluded any of the determinants, and the results from the various studies show clearly that the effects of sunk cost (Arkes & Blumer, 1985), self-justification (Brockner, 1992), project completion (Boehne & Paese, 2000), and optimism (Lovallo & Kahneman, 2003) are not mutually exclusive. Humphrey et al. (Humphrey, Moon, Conlon, & Hofmann, 2004) show how levels of commitment change over the duration of a project. In the same manner it is plausible that the effect of other determinants would vary over the life of the project.
Based on this argument we have the first proposed hypothesis:
Hypothesis 1. Multiple determinants are considered by decision makers when assessing the termination of a project in a portfolio.
Moving Benefits Target
Humans have a built-in bias to be optimistic about future events. This bias is difficult to control and decision makers who are aware of this bias are very unlikely to actually control or alter their behavior (Lovallo & Kahneman, 2003; Tali Sharot et al., 2012). It is therefore plausible to reason that decision makers will be overly optimistic about the future benefits of a project, hence CI > 1. To this extent, the following hypothesis is proposed:
Hypothesis 2. Once the Critical Point is reached or passed (i.e., Ie ≥ Be) the decision maker will assess Be > Bp when Ie > Ip and will continue to invest in the project.
Employees and contractors from a South African mining company took part in the experiment. The participants attended a nine-day course on their company's standards for capital project management. The course specifically covered the financial evaluation of projects, including ROI and payback period. All the participants were members of project management teams (e.g., project managers, planners, financial controllers, etc.) and the reasons for performing capital projects in their organizations were covered in detail in the course they attended.
The project values were presented to participants in South African Rands (ZAR) because this is the currency used in the company for estimating costs and calculating benefits of projects. The project values used are typical of a medium-sized project for the particular company.
This sample population was chosen since the group was homogenous in terms of their knowledge of capital investment projects and the given scenario contains investment values that are reflective of the value of projects in their organization.
Assessment Design and Decision Problem
A two-group posttest-only randomized experiment jointly varied invested amount and project progress (low vs. high).
The scenario was designed to follow previous experiments that tested for EoC, where a low project completion and low cost scenario are compared with a high completion and high cost scenario (Conlon & Garland, 1993; Garland & Conlon, 1998; Jensen et al., 2011; Moon et al., 2003; Tversky & Kahneman, 1981). This design allows for comparison to previous research in this area.
Participants had to indicate whether or not they would continue with a project given the scenario below (high values are shown in brackets). The financial information provided in the scenario is sufficient for participants to calculate the ROI prior to the project and at the specific assessment point in the scenario. Participants were told that they could do any calculation to arrive at their answer and that they could use pocket calculators for this purpose.
You are a manager in your company and part of a committee responsible for motivating and selecting projects in your company.
Your company is conducting a typical stay-in-business project with a planned cost of R350 million and a planned duration of 11 months. Before the project was started, the estimated Return on Investment (ROI) was 25%. The project is now at the end of month 3 (9), and the project manager has reported that the project is likely to take four months longer (i.e., 15 months) to complete. The actual cost to date for the project is R100 million (R285 million) and the project manager estimates that the project will cost R100 million more than originally planned (i.e., R450 million).
At a project review meeting a decision must be made whether to continue with this project or not. If the project is terminated now, approximately R40 million (R100 million) of the investment can be salvaged in the form of equipment and material. The remainder is a sunk cost attributed to labor and consulting fees.
After making a choice for the continuation or termination of the project, participants had to indicate to what extent they agreed with the following statements in support of their continuation/termination decision. Participants could choose from the following Likert-type scale of agreement: “Not at all” = 0, “Slightly” = 1, “Moderately” = 2, “Considerably” = 3, and “Completely” = 4
a. This project has already cost us a lot of money and we cannot afford to spend even more on it.
b. Even if we do not get business benefit from this project, there are enough other projects in the company to balance it out.
c. We have invested a lot of time on the project and are close to the end, we cannot stop now.
d. We usually get better than expected returns from projects over their useful life due to changes in market conditions and will eventually recover the investment.
e. We have already invested a lot of money in the project and cannot let the investment go to waste.
f. The benefit from the project is not only monetary and there are many other potential spin-offs that should be considered.
g. Even though the project is going to cost a bit more, it is unlikely that we will lose the benefit; we will just make a bit less.
h. A different decision could limit my future career opportunities or promotions in the organization.
Any score greater than 0 for a statement therefore shows some support for the given statement.
Over a period of 4 weeks, 84 surveys were completed. Three of the returned surveys had missing data and were discarded. Of the remaining 81 surveys, 37 were the low case scenario and 44 the high case.
Of the 44 high case scenario respondents, 34 (77.3%) respondents chose not to terminate the project while 10 (22.7%) chose to terminate.
Of the 37 low case scenario respondents, 31 (83.8%) respondents chose not to terminate the project while 6 (16.2%) chose to terminate.
Of the total sample (81 respondents), 65 (80.2%) respondents chose not to terminate the project while 16 (19.8%) chose to terminate.
The level of support for the given reasons for terminating or continuing with the project is analyzed in the tables below:
A two tailed t-Test shows the mean preferences of the participants who chose to terminate versus those who chose to continue with the project (Exhibit 4).
Test of Hypothesis 1
Descriptive statistics of the results of the experiment are presented in Exhibit 2. The combined means of the supported statements show that decision makers recognized that at least five factors influenced their decision to escalate commitment to the project (Exhibit 2 – Statements c, d, e, f, and g). This is consistent with H1.
Test of Hypothesis 2
To test H2, the support for statements that indicate an overly optimistic assessment of benefits (statements d, f, and g) between participants who chose to terminate and those who chose to continue with the project, are compared, Exhibit 4. Statement d (t = 6.885, p < 0.01), statement f (t = 8.674, p < 0.01), and statement g (t = 8.402, p < 0.01). These results are consistent with the hypothesis that decision makers who choose not to terminate, assessed Be > Bp when Ie > Ip even if Ie ≥ Be.
Considering the low case scenario, we expect to get an ROI of 25% (i.e., the project will pay back R437.5 million, which is a profit of R87.5 million. In the given scenario, R100 million was spent. The manager would have to spend another R350 million in order to finish the project. Since the salvage value of the project at this stage is R40 million, the manager would lose R40 million for sure if the project was stopped right now. Completing the project requires an additional R350 million to be spent and the expected return is only R437.5 million; hence, the manager is going to lose R12.5 million, which is an ROI of -2.9%. The difference lies in the perception of the losses and gains (Tversky & Kahneman, 1992). The manager is facing an immediate certain loss of R40 million versus a possible loss of R12.5 million sometime in the future. At face value, the R12.5 million loss may be more attractive than the R40 million loss, but the manager must first invest another R350 million on the project that he or she knows will be a failure.
80.2% of the respondents decided to continue with this project and their reasons for continuing shed some light on this behavior. From Exhibit 2 we see that the three best supported reasons are related to the expectation that something will happen in the future to improve the outcome.
a. Statement c (M=2.29) shows clear support for the project completion hypothesis as a reason for EoC.
b. Statement d (M= 2.66) shows support for optimism bias and one could question why the better than expected results are never reflected in the initial business case.
c. Statement e (M=2.55) supports sunk cost as a reason for EoC.
d. Statement f (M=2.88) shows support for optimism bias and could again question why the additional benefits are not reflected in the ROI. One could also question how participants would know about the additional benefit but the person who developed the business case does not.
e. Statement g (M=2.66) shows support for optimism bias and it is clear that the project will lose benefit.
The study used a specific group of people from one organization. The advantage of this group is that the participant profiles were very well controlled and all the participants were on a training course, which ruled out the possibility that they did not have the theoretical knowledge to determine the ROI of the project. The risk in using a homogenous group from one organization is that the exhibited behavior could in part be due to the work environment of the participants and the culture of the organization. This problem can be eliminated by performing the experiment with a sample from multiple companies and comparing the results with the results from this experiment.
A smaller than expected number of participants chose to terminate the project. The sample for participants who terminated is therefore quite small and statistical analysis of this group would be limited. This problem could be eliminated through a larger sample size.
No control group was set up to test the level of support for the given statements for a project that is not in trouble. Prior research on escalation of commitment performed similar experiments without a control group of an investment that is not in trouble (Jensen et al., 2011; Staw, 1976; Tversky & Kahneman, 1981) but specifically tested for the difference between decision makers who are presented with a low cost and project completion scenario versus a high cost and project completion. The present research therefore tests for the relative change in behavior between the low and high positions. This problem could easily be addressed in future research if the sample group is large enough to allow for multiple scenarios.
Future research in this area should consider the above study limitations in the design of the experiment (e.g., a sample over a spectrum of participants that involves multiple industries and multiple nationalities).
Further research should also attempt to determine to what extent optimism bias has power over the CP and CI, and how far a decision maker will go beyond the CP before terminating a project. From the present research it appears as if there could be two CPs. The first CP is calculated from the financial information of the project and is the actual point where the ROI becomes negative (given the estimated investment of the project). The second CP is a psychological CP where a decision maker realizes that the investment is not feasible and the best decision is to terminate the project.
The results of this experiment show support for two of the traditional factors that contribute to EoC (i.e., sunk cost and project completion). The experiment also shows very strong support that the decision makers are optimistic that the product from the project will somehow perform better than what they initially (before the project started) assessed it to be.
This research makes three important contributions to existing research:
a. Confirmation that decision makers consider multiple determinants when considering escalation on a project;
b. Optimism bias about the value of the project outcome is a significant contributor to EoC and should be considered in future studies in this area; and
c. The CP and CI are useful methods for quantifying when a project becomes an unfavorable investment. But it also highlights that decision makers have hardly any regard for this point and will continue to invest in a project beyond this point. This research shows that decision makers have a strong belief that the project will have greater benefits than originally anticipated with no such evidence present. This behavior strongly indicates that decision makers are suffering from optimism bias. This behavior, however, highlights that there may in fact be two CPs, the calculated CP from the project's financial performance and a psychological CP, which is determined by the decision maker's susceptibility to the influence of EoC determinants.
Amos, S. (Ed.). (2007). Skills and knowledge of cost engineering (5th ed.). Morgantown, WV: AACE International.
Anbari, F. T. (2003). Earned value project management method and extensions. Project Management Journal, 34(4), 12–23.
Arkes, H. R., & Blumer, C. (1985). The psychology of sunk cost. Organizational Behavior and Human Decision Making, 35, 124–140.
Bazerman, M. H., & Samuelson, W. F. (1983). I won the auction but don't want the prize. Journal Of Conflict Resolution, 27(4), 618–634.
Bobocel, D. R., & Meyer, J. P. (1994). Escalating commitment to a failing course of action: Separating the roles of choice and justification. Journal of Applied Psychology, 79(3), 360–363.
Boehne, D. M., & Paese, P. W. (2000). Deciding whether to complete or terminate an unfinished project: A strong test of the project completion hypothesis. Organizational Behavior and Human Decision Processes, 81(2), 178–194.
Brehm, J. W., & Cohen, A. R. (1962). Explorations in cognitive dissonance. Hoboken, NJ: John Wiley & Sons Inc.
Brockner, J. (1992). The escalation of commitment to a failing course of action: Toward theoretical progress. The Academy of Management Review, 17(1), 39–61.
Carlsmith, J. M., & Freedman, J. L. (Eds.). (1968). Bad decisions and dissonance: Nobody's perfect. Chicago: Rand McNally.
Conlon, D. E., & Garland, H. (1993). The role of project completion information in resource allocation decisions. The Academy of Management Journal, 36(2), 402–413.
Cooper, J. (1971). Personal responsibility and dissonance: The role of foreseen consequences. Journal of Personality and Social Psychology, 18(3).
Fox, F. V., & Staw, B. M. (1979). The trapped administrator: Effects of job insecurity and policy resistance upon commitment to a course of action. Administrative Science Quarterly, 24(3), 449–471.
Garland, H., & Conlon, D. E. (1998). Too close to quit: The role of project completion in maintaining commitment. Journal of Applied Social Psychology, 28(22), 2025–2048.
Garland, H., Sandefur, C. A., & Rogers, A. C. (1990). De-escalation of commitment in oil exploration: When sunk costs and negative feedback coincide. Journal of Applied Psychology, 75(6), 721–727.
Humphrey, S. E., Moon, H., Conlon, D. E., & Hofmann, D. A. (2004). Decision-making and behavior fluidity: How focus on completion and emphasis on safety changes over the course of projects. Organizational Behavior and Human Decision Processes, 93, 14–27.
Jensen, J. M., Conlon, D. E., Humphrey, S. E., & Moon, H. (2011). The consequences of completion: How level of completion influences information concealment by decision makers. Journal of Applied Social Psychology, 41(2), 401–428.
Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47, 263–291.
Lipke, W., Zwikael, O., Henderson, K., & Anbari, F. (2009). Prediction of project outcome: The application of statistical methods to earned value management and earned schedule performance indexes. International Journal of Project Management, 27(4), 400–407.
Lovallo, D., & Kahneman, D. (2003). Delusions of success: How optimism undermines executives’ decisions. Harvard Business Review.
Meyer, W. G. (2012). Project selection and termination: How executives get trapped. Paper presented at the PMI Global Congress Proceedings, Vancouver, Canada.
Moon, H., Conlon, D. E., Humphrey, S. E., Quigley, N., Devers, C. E., & Nowakowski, J. M. (2003). Group decision process and incrementalism in organizational decision making. Organizational Behavior and Human Decision Processes, 92, 67–79.
Northcraft, G. B., & Wolf, G. (1984). Dollars, sense, and sunk costs: A life-cycle model for resource allocation decisions. Academy of Management Review, 9(2), 225–234.
Project Management Institute. (2013). A guide to the project management body of knowledge (PMBOK® guide)— (5th ed.). Newtown Square, PA: Author.
Ross, J., & Staw, B. M. (1993). Organizational escalation and exit: The case of the Shoreham nuclear power plant. Academy of Management Journal, 36, 701–732.
Staw, B. M. (1976). Knee-deep in the big muddy: A study of escalating commitment to a chosen course of action. Organizational Behavior and Human Resourses, 16, 27–44.
Staw, B. M. (1997). The escalation of commitment: An update and appraisal. In Z. Shapira (Ed.), Organizational Decision Making (pp. 191–215): Cambridge University Press.
Tali Sharot, Ryota Kanai, David Marston, Christoph W. Korn, Geraint Rees, & Dolan, R. J. (2012). Selectively altering belief formation in the human brain. Proceedings of the National Academy of Sciences of the United States, 109(42), 17058–17062.
Taylor, S. E., & Brown, J. D. (1988). Illusion and well-being: A social psychological perspective on mental health. Psychological Bulletin, 103(2), 193–210.
Taylor, S. E., & Brown, J. D. (1994). Positive illusions and well-being revisited: Separating fact from fiction. Psychological Bulletin, 116(1), 21–27.
Tversky, A., & Kahneman, D. (1981). Framing of decisions and the psychology of choice. Science, 211(4481), 453–458.
Tversky, A., & Kahneman, D. (1992). Advances in prospect theory: Cumulative representation of uncertainty. Journal of Risk and Uncertainty, 5(4), 297–323.
Whyte, G. (1986). Escalating commitment to a course of action: A reinterpretation. The Academy of Management Review, 11(2), 311–321.
Whyte, G. (1993). Escalating commitment in individual and group decision making: A prospect theory approach. Organizational Behavior and Human Decision Processes, 54(3), 430–455.
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Originally published as a part of 2013 PMI® Global Congress Proceedings – Istanbul, Turkey