Decisions about whether or not to kill an ongoing project of an IT portfolio may be critical. A bad decision to continue with a project with a high probability of making things worse, in exchange of a small chance of avoiding a large loss, often turns manageable failures into disasters. However, these decisions can be negatively impacted by cognitive biases of decision makers, such as the loss aversion, which are enhanced when uncertainty is substantial and information incomplete. As a result, it is important to reduce the biases in these decisions. This paper introduces an ongoing research that aims to create an ontology to improve the understanding of these cognitive biases. It can be used as part of a strategy to reduce biases in a risk and value analysis.
Decisions over the project portfolio are often referred to as the point where strategy is
put into action and is therefore crucial for the companies in order to reach their strategic
goals. This is achieved through the successful selection and execution of an appropriate
mix of projects, through a process known as project portfolio management (PPM). As
the dependency on information technology (IT) for organizational performance
increases, organizations must use IT portfolio management techniques to ensure that the
IT projects are aligned with the organizational strategic objectives. Making a portfolio
decision is far from trivial. This decision process is characterized by uncertain and
changing information, dynamic opportunities, multiple goals and strategic
considerations and interdependence among projects
In this research proposal, we focus on making Go/Kill decisions on individual IT
projects on an ongoing basis
These decisions can be negatively impacted by cognitive biases
Therefore, to improve the decision making, it is important to better understand
how these biases occur, how to deal with them, and how to help to reduce their negative
consequences. In this sense, a considerable number of studies have been done in
strategic decision making
How can we (de-bias) mitigate or reduce the negative consequences of the cognitive biases (loss aversion, risk seeking and framing effects) in the Go/Kill decisions of ongoing individual projects in an IT portfolio?
This paper is structured as follows. Section 2 presents behavioral economics.
Section 2.1 details the heuristics and biases and how it occurs; Section 2.2 shows the cumulative prospect theory and how it is used to predict the decision making. Section 3 defines IT portfolio concepts. Section 4 presents one ontology of value and risk. Section 5 presents the design science research as the research method used in our research proposal, and section 6, our proposed ontology.
Behavioral economics (BE) is a descriptive theory that focuses on how decisions are
actually made while a normative decision theory is about how decisions should be made
in order to be rational. One of the main foundations of BE is the dual process theory. It
aims to explain the decision-making process from the view of decision makers through
a general framework comprising two distinct systems of thinking: System 1 and System
2. System 2 corresponds to reasoned thinking, that has effortful mental activities;
System 1 corresponds to intuitive thinking and operates automatically and quickly, with
little or no effort, and no sense of voluntary control
To reduce difficult mental tasks, such as assessing probabilities and predicting values,
decision makers often use heuristics. These procedures include computational shortcuts
and editing operations, such as eliminating common components and discarding
nonessential differences for arriving at satisfactory solutions with modest amounts of
computation
Therefore, value and risk analyses are subject to several cognitive biases, because they are human-centric activities that require estimating likelihood of occurrence and potential impact, under uncertainty (NOYES et al., 2012). The significant role cognitive biases play in risk management has been acknowledged by both ISO31000:2018 and COSO:ERM 2017, that are important risk management guidelines (SIDORENKO, 2018).
In this research proposal, we use the cognitive bias typology of
CPT is a descriptive theory and extends prospect theory to uncertain as well as to risky
prospects with any number of outcomes while preserving most of its essential features
Three cognitive features, all operating characteristics of System 1 (intuitive
thinking), are the foundation of cumulative prospect theory
One of the core achievements of CPT is the fourfold pattern of risk attitudes: risk
aversion for gains and risk seeking for losses of medium and high probability; risk
seeking for gains and risk aversion for losses of low probability
Project portfolio management (PPM) consists of two different parts or phases: (1) project portfolio selection and (2) portfolio management. The first phase is to select projects for the pipeline. This phase includes a structured process to deal with project proposals and how they can be evaluated. The second phase is to maintain the project pipeline. Once the selected projects are being executed, they need to be monitored and evaluated to ensure that they still fulfil the conditions of the original selection criteria. If the conditions change it needs to be reconsidered if the project should remain in the portfolio or be terminated (LEVINE, 2005). Our focus in this research proposal is on this last decision that is called Go/Kill decision.
In the information technology (IT) context, IT portfolio management provides
the discipline of balancing risk against expected returns, evaluating the performance and
utilization of existing systems, analyzing and assessing alternatives and trade-offs, and
removing waste resulting in significant efficiencies and cost savings. To be able to make
this complex decision, experienced IT-projects portfolio managers use many methods to
quantify risks, costs, value, and performance of the IT projects
Sales et al. (2018) have presented an ontological analysis of risk which makes explicit the deep connections between the concepts of value and risk. They have also proposed a concrete artifact, namely the Common Ontology of Value and Risk, formalized in OntoUML. They formally characterized the process of ascribing risk as a particular case of the process of ascribing value (in the sense of use value).
This ontology considers three perspectives: (1) an experiential perspective; (2) a relational perspective; and (3) a quantitative perspective, which projects value and risk on measurable scales and it is our focus in this research proposal.
Risk and value have the following similarities: (a) goal dependency; (b) context
dependency; (c) uncertainty and impact in the conceptualization of both: the
computation of the likelihood of an event times its impact on one’s objectives and
preferences fits the quantitative analysis of both risk and value. The impact for the value
is related to achieving goals. This opens the possibility of applying methodologies and
techniques developed for value analysis in marketing and economics to the case of risk
analysis, and vice versa. Risk analysis is traditionally accepted as a complex and critical
activity in various contexts, such as strategic and project planning, finance, engineering
of complex systems, and software development
We selected design science research (DSR) as the research method, because of its
strength in solving a real problem using applied research
We will evaluate the ontology by focus group analysis. These groups are formed by IT portfolio decision makers and DSS analysts. We will use the pragmatic perspective to evaluate the perception of utility of the ontology in a de-biasing strategy.
De-biasing requires the decision support systems (DSS) analyst to understand the
mechanisms underlying the particular bias that is subject of change. Each bias can have
a different de-biasing approach, and this makes de-biasing difficult in practice as it
requires significant effort from the analyst
To answer the research question, we propose an ontology representing the Go/Kill decisions associated to IT projects and their values, risks and cognitive biases. The proposed ontology extends the Common Ontology of Value and Risk (SALLES et al., 2018) because value and risk analyses are at the core of the Go/Kill decisions and it uses cumulative prospect theory (CPT) as the decision making theory under uncertainty to support the value and risk analysis. We use CPT because it may allow a better understanding of the cognitive biases considered in our research problem.
Furthermore, the true semantics of what is value and how the cognitive biases
sometimes negatively affect decision making are sometimes not well understood. Its
semantic essence can be better explored and represented to identify other potential
associated elements. In the literature review, we found just one ontology
In particular, we plan to extend the ontology of Salles et al. (2018) by, initially,
(i) including the concepts of decision making according to CPT to measure value and
risks relative to a reference point (rather than absolute outcomes), according to the
pattern of risk attitudes; (ii) taking into account cognitive biases related to value and
risk; and (iii) adapting the concepts of risk assessment of a cognitive bias from the
ontology of Lortal et al. (2014) to loss aversion, risk seeking and framing effects for IT
portfolio Go/Kill decisions. Most decision makers are unaware that they use reference
points, so they are likely to decide without realizing how it can bias their choice of
action
Our proposed ontology aims to improve the understanding by the analysts of
these decision biases and the situation in which they occur. This ontology can be used as
part of a strategy to reduce the biases in the Go/Kill decisions of an IT portfolio. More
specifically, our focus is on developing DSS as a de-bias strategy (LARRICK, 2004).
We use the debiasing process of