Chapter 5

Study of Risk Factors in Competitive Intelligence Decision Making: A Cognitive Approach¹

5.1. Decision making and decision problems

5.1.1. Introduction

Decision making is a process undertaken by a person, a group of people, several groups, or a company. It is a “living” process: any form of decision making has consequences that determine the success or failure of other actions [WAN 04]. This process may be simple (personal decisions) or complex (decisions involving large organizations or governments); the weight attached to these decisions, expressed in the form of a risk, varies in the same way. Human knowledge is enriched through experience and reasoning capacities that allow us to bring order to a mass of available information. In this context, it is imperative that the decisions resulting from this process be controlled [ONI 08a]. The capacity for decision, which we might call “decisionability”, is determined by numerous factors including judgment, experience and, in particular, cognitive capacities. As decisions are taken based on information, the mode and methods used to obtain this information are as important as the decision itself [TOD 00, RED 98]. Our perception of the world is limited by the lack of a suitable lexicon, by imprecision, and by the incompleteness of the “measurements” we carry out [SIV 07]. The formulation and implementation of the decision process has a significant impact on the result of the decision; this can be linked to the cognitive capacities of the decision maker (DM) and to risk factors (RFs). Decision making, with the aim of improving organizational performance, is the focal point of competitive intelligence (CI).

According to Martre [MAR 94], CI is defined as a group of coordinated research, data processing, and information distribution activities that allow actions to be carried out and decisions to be made. The author indicates that these actions are carried out legally, with the aim of protecting company assets, within optimal quality, cost, and time limits. Revelli [REV 98] defines CI as a process of collection, processing, and diffusion of information with the aim of reducing uncertainty in a strategic decision process. CI has been defined, in a given organizational context, as the capacity of the DM to exploit new or recently acquired knowledge and experience to solve a new decision problem. Another vision, put forward by [KAR 03], considers CI as the global process through which DMs obtain a clear understanding of the environment in which they operate.

By synthesizing these views, we can easily identify recurring themes: “information”, “actor or user”, and “DM”. These aspects form the basis of CI, while the cognitive capacity of the DM determines the possibility of RFs in the decision process.

In the following section, we will look at the question of decision making and decision problems using typical examples of the CI process.

5.1.2. Fundamental aspects of the decision problem

There may be multiple reasons for a given decision, but we will use two main categories:

– Decision based on a choice between alternatives: the decision is seen as a study, then identification and choice of alternatives based on the values, and preferences of the person concerned [HAR 98]. Therefore, there are several possible choices in the decision process. This implies not only that several alternatives should be identified but also that a choice between these alternatives must be made to find the “best solution” based on given desires, aims, values, lifestyles, and so on.

– Decision based on a reduction of choice: we have already highlighted factors we consider to be important in the process. In this case, we must reduce incertitude and doubt concerning possible alternatives as far as possible to facilitate selection of the best option. It is important to note that very few decisions are made with absolute certitude, as complete understanding of all possible alternatives is rarely attainable. For this reason, we talk of “reduction”, and not “removal”, of doubt.

We may also consider a decision problem in relation to different types of decision. While all living things make decisions, humans are the most developed, creating complex operations with the aim of reaching a logical conclusion. Figure 5.1 gives some examples of decisions, based on linguistic expression.

The “what” decision corresponds to a choice of one or more alternatives from a set of possibilities. In most cases, the selection results from the level of satisfaction of several predefined criteria. The “if” decision is simply Boolean: yes or no. It requires consideration of arguments for and against a choice. A “contingent” decision is, in fact, a provisional decision, conditioned by an exterior element. The decision is considered to have been made, but it “waits” for the completion of the associated condition [HAR 98]. For example, “I’ll go back to university if I can save enough money to pay the fees”. In actual fact, human beings often have decisions “ready” to be made, but must wait for an environment, a trigger, or an opportunity to validate this decision.

We have presented a number of definitions and concepts that are useful in decision making. Goals, alternatives, and incertitude have been identified and used to define the notion of decision making. We have also discussed three types of decisions: “what”, “if”, and “contingent”. We will now consider the different components of the decision-making process.

5.1.3. Decision and cognitive capacity

Instinct, conscious or subconscious beliefs, values, and intuition have been identified as major elements having an effect on modes of decision making [BAR 06]. Figure 5.2 shows different modes and the factors that affect them.

Decisions based on instinct. This method of decision making has its origins in our genetic makeup. It is mostly associated with problems affecting survival. A typical example is the suckling and crying reflex observed in newborn babies. Distinctive properties of this type of decision include actions that precede any deep reflection or decisions taken based on recognizable past experiences (and which maintain an internal stability or balance). An individual may be said to have little control over these decisions; it is the decision that controls the human being.

Decisions based on subconscious belief. This decision context is similar to that described above, the difference being that the decision is founded on personal memories. Once again, action precedes thought and is an expression of emotional factors that may be negative (blame, competition, rivalry, mistrust) or positive (openness, trust, etc.) [BAR 06].

Common examples of decisions based on subconscious beliefs include tears of joy, the feeling of pride, and feelings of spontaneous happiness. Main characteristics of these decisions include the fact that action precedes thought, that decisions are based on past experience, and that the DM has no direct control over their actions and behavior, that is, emotional or rational stability. Decision-making operations of this kind occur at the first three levels of consciousness [HAR 98].

Decisions based on conscious belief. The subconscious does not allow rational decision making, necessitating passage to a conscious mode of decision making. This means that there will be a time lapse between the construction of meaning and the decision itself. This period is used for reflection, reconsideration of ideas, and the implementation of a logical and thought-out process, in the aim of understanding the “work in progress”. In this mode, thought precedes the decision, and time is set aside for discussion before the final decision is made. There is, however, a similarity between decisions based on subconscious beliefs and those based on conscious belief: both are founded on information acquired from past experiences (that which is “known”), reused to deal with new and future problems.

Value-based decisions. In this case, we are once again dealing with conscious decisions, but with a certain input from emotional attachment that may obstruct the decision process. A major question raised in this type of decision is: “Is this decision rational, and does it correspond to our values?” The response to this question may lead to reconsideration of the problem or to a final decision being made. A decision that runs contrary to the values of an organization will reveal a lack of integrity. In a similar manner, a decision that does not reflect the personal values of the DM will lack authenticity. In both cases, there is a noticeable absence of cohesion. Value-based decision allows decisions to be made concerning a preestablished “mission” and differs in this respect from decisions based on conscious belief; the construction of meaning is less important. Value-based decision means our behavior is guided by our values, but not by our beliefs. Values, considered to be universal, transcend all contexts, whereas beliefs are local and contextual [JUN 04].

Decisions based on intuition. This type of decision is described through particular characteristics. The collection and processing of data are carried out as usual, but the judgment stage is absent: there is no in-depth reflection, whether conscious or subconscious. Reasoning and beliefs are not involved; the mind delves into a collective subconscious and thoughts emerge, reflecting knowledge, wisdom, the common interest, and deeply rooted values with long-term applications.

There is therefore an important contrast between analytical decisions (where attention is given to details) and intuitive decisions (where attention is focused on broad themes). Consequently, instinct, conscious and subconscious beliefs, values, and intuition determine the mode of decision making [BAR 06]. Several concepts have been identified [HUN 89]: the DM is seen as a stable individual with beliefs, predispositions, competences, and experience that define and describe their personality. The decision task (DT) itself merits special attention: first, it falls within a particular decision situation (DS) associated with contextual, conceptual, and circumstantial factors. It is based on the decision process (DP) and the decision produces a result or decision object. The aim of this study was to show the relationships between the characteristics of the DM and the DP, with an ill-defined DT. Figure 5.3 illustrates these relationships.

Studies have been carried out examining the consequences of this on managerial decision making [ROB 81]. Aspects concerning the collection and evaluation of information have also been the subject of study [MCK 74], concentrating on the characteristics of DMs: some are essentially analytical (collection, analysis of sensitivity) [OSO 07] while others work in a more intuitive manner (filtration of data). This allows us to better define the cognitive characteristics of actors and to see how these traits are used in the process of solving a decision problem.

5.1.4. Decisions in the context of CI

DMs may rely on intuition to either solve their problem or rationalize the problem. Normally, the decisions we find in CI are linked to financial considerations, the importance of which determines the size of the challenge facing the DM.

In this context, we use a well-defined process, beginning with the identification of the decision need. The watcher then assists the DM in transforming this need into an information problem (IP), before carrying out the necessary research. This requires precise mutual understanding of the problem and the stakes involved. The final decision is based on the information obtained in this way. The process is, therefore, relatively robust and theoretically takes account of all elements that are useful in making an effective decision.

CI is thus presented as a coordinated research and information use action, which aims, in time, to enable strategic decision making [MAR 94, REV 98]. Bouaka and David [BOU 04] and Thiéry and David [THI 02] present a model that combines the context of the problem with a representation of the DM and the stakes involved in the decision problem. The aim is, on the one hand, to facilitate identification and representation of the problem and, on the other hand, to prepare for the information-seeking process that follows. This is made possible by the identification of user characteristics and by evaluating the stakes involved in the problem.

A proposal aiming to assist the DM in defining the problem was established by Bouaka and David [BOU 04] with particular emphasis on data linked to the environment, to the organization, and to actors. The goal of this proposal was essentially to clarify relationships between actors to know who asked a given question and why the question was asked. In this case, we presume that a relationship of trust has been established between actors.

Figure 5.4 shows the actors, information, and processes implemented in the context of CI. David and David [DAV 01] present a model that allows the process to be adapted to the actors. It includes a stage for understanding the process, leading to the production of interpretable indicators useful in decision making and constructed using available information. The authors highlight the importance of considering risks and threats posed by the decision itself, leading to more precise consideration of the profiles of two key actors: the DM and the watcher (seen as an information seeking specialist). We propose a new information seeking system, named METIORE, based on a double filtration process. The first stage of this system consists of capturing research aims, formulated using natural language. Following this stage, the user may construct simple or complex requests, based on a set of available research functions. In this situation, we presume that the user already has an idea of the information required.

In terms of decisions, another approach focuses more specifically on management issues and presupposes the existence of a data repository [DUF 05]. This work highlights the importance of data quality and looks at all stages of information processing: the identification of useful data, processing and the inclusion of data in the database are considered first, followed by reflection concerning its effective use.

The DM must choose how to act based on relevant indicators connected to their particular problem(s) [THI 02]. The consequences of any decision are seen as expected benefits. The sense of judgment and other cognitive factors play an important role in characterizing the “capacity” of a DM to make the right decision [TOD 00]. Insufficiency in these respects may have significant consequences.

We have given a brief overview of decisions and decision problems, which take account of the cognitive capacities of the DM in the context of CI. We will now look at the concepts of risk and RFs, based on possible interactions between different elements of CI.

5.2. Risks and RFs in CI

5.2.1. Introduction

Risk is generally seen as the existence of an unwelcome occurrence triggered by a threat and a level of vulnerability [ALL 07]. A risk is not necessarily a danger, but the indication of a dangerous situation that may result from an action, voluntary or otherwise, applied to a weakness of the system in question. Coras [COR 00] offers a platform for the analysis of security risk in critical systems. This context is, however, different from that of CI, which mixes information systems with decision theory. The work identifies two types of risks: business risks, associated with the organization, and technical risks, which relate to the information processing practices used in the organization (often those which pertain to the operation of a database). The associated risk model may be two-dimensional, with both structural and behavioral axes [MOU 06]. In this section, we will explore notions of risk in the context of CI.

5.2.2. Actors and their interactions in CI

CI is made up of a set of processes used in series for decision assistance [EDE 94, RUB 90]. The main users of this system, known as actors, are the DM and the watcher (although other categories of actors exist). These users interact to identify a decision problem, reinterpret it as an IP, process and present the results obtained, and, finally, make a decision based on this newly available information. Although this sequence of operations allows us to build a robust decision assistance process, a certain number of risks, linked to data quality in particular, must not be ignored. The issue of data quality has attracted the attention of a number of researchers; for the moment, no definitive solution to the problem has been found, and the number of different actors involved does not make things any easier [TOD 00, RED 98].

Decision making, based on the processes described above, requires interaction between the actors involved. The DM is confronted with a decision problem that must be solved through the use of relevant indicators. The DM is responsible for the identification, analysis, and deductions they produce before reaching a final decision [DUF 05]. Tasks may be shared: a watcher deals specifically with questions linked to the provision of information from various different systems, both within and outside of the organization.

Figure 5.5 illustrates the relationships and interactions between CI users along with the risks involved. These risks arise from the need to make a decision based on observation and interpretation of the real world (made up of the organization and its environment). To model this world, we use entities, that is, objects or events represented symbolically by identifiers and the values of their attributes. In this, we presume that these values relate to the symbolic representation of things, events, states, and environmental factors which must be taken into account and which may change the context in which the decision must be made, and/or the results of operations, and/or the actions required for a successful implementation of the resulting decisions [ONI 08a].

5.2.3. Risks and RFs

The identification and quantification of consequences of a risk is not easy. Duffing, David, and Thiery [DUF 05] show several modes of occurrence, the existence of which means that a unique and inflexible approach is not suited to the changing context of CI. Considering the importance of cognitive style in the decision process and the notion of risk in CI presented in [ONI 08a], we note that the effectiveness of a decision is a measurement of the level of risk attributable to this process. To determine the level of risk inherent in a particular decision, we make use of another concept: RFs [ONI 08b].

A RF is a concept, thing, circumstance, or factor, which increases the level of vulnerability to a threat and to its consequences in a given situation. This RF may be inherent or be a consequence of non-habitual actions. A RF may, therefore, be defined as any individual action, interaction, or process capable of reducing the performance of an operation. An RF may be the result of action or inaction, of a process already described or of an activity carried out in the life cycle of processed information [ONI 08b]. These RFs are linked to “information” and are therefore represented by the symbol R_i; RFs linked to the watcher are R_w, to coordinators or other actors are R_cor, to interactions between the watcher and the coordinator are R_wcor, and to the DM are R_dm, as set out in Figure 5.5. We may consequently define every risk using the following variable parameters:

– R_dm risk:

– R_w and R_im risks:

– R_im, R_w, and R_wim risks:

From Figure 5.5, we also note the following:

– R_i: risk involved in the acquisition of information, heterogeneity of sources, their representation, extraction, or use. These RFs are linked to the extraction, transformation, and loading process. Examples include date format: dd-mm-yyyy or mm-dd-yyyy.

– R_w: risk involved in the way the watcher links the decision problem to corresponding IPs. For example, with the problem “we need a car”, what other properties might be used to describe the car?

– R_cor: useful for representing potential maladaptations resulting from the activities of the coordinator, constituting a risk for the DM. Example: availability of information at the right time.

– R_wcor: represents the potential risk arising from collective action (or inaction) on the part of watchers and coordinators. Examples: presumption, omission, and delegation.

– R_dm: risks created by the definition and presentation of the decision problem, to its interpretation and to the final inference process — for example, faulty observation and presentation of the problem.

We may then define the risk R:

[5.1]

The above risk may be interpreted as a conditional probability, that is, the information seeking process will be valid (B) if information acquisition (A)

[5.2]

The context of CI is characterized by complex strategic decisions linked to performance demands. The ability to identify and model possible RFs in an appropriate manner should allow us to determine the effectiveness of a decision.

We have now presented the notions of risk and RFs and seen how their effect on the decision performance of the DM may be determined. As DMs are somewhat risk averse, we made proposals concerning the notion of RFs, with emphasis on the fact that risk only becomes clear when vulnerability is involved. We will now attempt to show the importance of cognitive capacities in the decision process.

5.3. Cognitive capacity, a risk, and decision factor

5.3.1. Introduction

One fundamental question that needs to be asked is whether the quality of a decision should be evaluated on the basis of the process involved in its construction or by looking at its results (and their consequences). Following the process approach, “almost all decisions are made amid uncertainty”. Thus, a decision is a “bet” that must be taken, considering its stakes and odds, and not just looked at in terms of its results. However, it is widely accepted that a good decision structure should include possible results, in that these results affect the attainment of the aims of the DM (consequentialism). A typical example used to demonstrate this idea is that of a surgeon talking of an operation as successful in spite of the fact that the patient died. This is clearly unsatisfactory for all concerned, as the final consequence is much more important than the fact the process went smoothly and according to plan [BUL 03]. In this section, we will demonstrate the importance and the effect of individual cognitive capacities in knowledge collection operations [ONI 08b].

5.3.2. Cognitive capacity and its effects on decision making

Decision making is one of the cognitive processes associated with human behavior; it involves the choice of options or of series of actions from a set ofalternatives established from criteria [MOU 06]. This decision-making process is one of 37 fundamental cognitive processes, according to the layered reference model of the brain (LRMB). There are two main categories:

– Descriptive theories are based on empirical observation and on experimental studies of choice behaviors.

– Normative theory supposes that a rational DM makes decisions based on clearly defined preferences, which conform to certain axioms of rational behavior: the utility paradigm and Bayesian theory.

Figure 5.6 shows the relationship between the act of deciding and other processes in the LRMB [RUB 90]. The cognitive capacities of DMs may refer to different capacities depending on their level of exposition, the environment, and other factors. However, the basic cognitive processes of the human brain share and demonstrate similar and recursive characteristics and mechanisms. The complexity of definition of a problem is a result of differences in the interpretation of each actor in a given situation. This produces different levels of understanding of an event and different explanations of influences between events. This is at the base of a concept known as “knowledge collection” [ONI 08b]. Differences in interpretation should be connected to the personal values of individuals. In the same way, explanations of influences are linked to the life experiences of individuals which allow them to construct different belief systems [EDE 94].

The bulk of work concerning decisions is normative and dominated by rationalist perspectives. Commonly studied decisions are said to be programmed and structured. There are decisions for which the notion of process itself is not relevant, implying that the decision is the result of an immediate mechanical connection between behavioral results and certain environmental conditions [HUN 89].

Practical decisions based on formal models and methods applied to complex and poorly structured situations, forms of representation of users in a situation and their aims and interests, always constitute a relevant aspect for the solution of a problem [ABR 08]. Thus, most decisions and information seeking systems that support these aspects are largely characterized by different forms of incertitude seen as risks [OSO 07].

Figure 5.7 shows quite clearly that the act of decision is far from being uniquely mechanical. It is evident that this process, which results from the capacity for resolution of the problem, proceeds from two factors, involving the following:

i) the processes of understanding, qualifying, and quantifying the decision to be made; a false representation of one of these processes will certainly produce bad judgments or perceptions, leading to failure;

ii) subsequent research, representation, and memorization processes.

As users are classified as actors and each has particular tasks to carry out, stage (i) above may be carried out by different people. The variety of personalities, individual intuition, and experiences, coupled with organizational factors, biases, and other misinformation risks, unequivocally demand a reliable knowledge collection process for use by all actors involved.

5.3.3. Cognitive model of RFs

Most human decisions are not mechanical but are based on judgment, implying and explaining the different steps proposed in CI processes. That which we call a “decision” is, in fact, a set of fairly complex processes that are not directly observable (i.e. cognitive and symbolic processes) through which actors develop and share models of their own realities, which they then apply to particular cases to organize their actions. Bullen and Sacks [BUL 03] state that cognitive style is the way in which individuals process and organize information to reach a judgment or a conclusion based on their observations [EDE 94].

Several reasons have been put forward in defense of the idea that a decision and its qualities are difficult things to measure. They are seen to be too vague and ill-defined, preventing the definition of a systematic or concise manner of processing them. Moreover, decision making is multidimensional, and the resulting classes require different judgment criteria. Finally, we should highlight the existence and the importance of a decision that may be made in relation to another decision or a decision of the “second order”. We may then pass recursively to higher levels [EDE 94].

The fact that DMs do not always base their decisions on the exact reality (as this is hard to comprehend and never totally known) means that a decision is a complex problem. DMs operate using available representations of reality. We can, therefore, state that a DS involves an inventory of that which is known to the DM (data) and that which must be discovered (information). Afterward, rules of reasoning (knowledge) must be applied to guarantee optimal effectiveness [HUN 89, JUN 04]. To attempt to demonstrate the importance of this phase of knowledge collection in the decision process, we offer a causal model taken from [ONI 08a] in the form of an Ishikawa diagram.

In a “fishbone” diagram (Figure 5.8) (which makes no claim to be exhaustive), each “bone” corresponds to a cause that may affect the quality of the result of the following step. The multiplicity of occurrences of these RFs is due to imprecision, which is itself linked to the difficulty of adequately transforming the decision problem — as perceived and presented by the DM — into the equivalent IP. It is this IP that is used in the information seeking process. One commonly accepted quality problem associated with data is “actionability”, that is, the degree of credibility accorded to these data from which the DM may wish to act [BUL 03].

The presentation of decision problems to other actors by the DM, with the aim of reducing risk in the entire process, should allow us to guarantee an acceptable level of information action among participating entities. Based on Figure 5.7, we have aimed to integrate both tangible factors (problems linked to database techniques) and intangible factors (bias) to demonstrate the importance of the knowledge collection process in decision making. Up until now, these two types of factors have been dealt with in isolation, and it was difficult to understand their joint effects on the quality of data produced by these processes. Their treatment as a pair will facilitate a balanced approach to risk detection and management, thanks to the identification and reduction of RFs. This is made possible by an appropriate integration of cognitive and environmental factors with organizational needs, the technical constraints of information systems, biases, and problems associated with faulty information.

5.4. Conclusion

We have presented a useful model for the identification of RFs in interactions between actors involved in the CI process. We highlighted the importance of the cognitive capacities of the DM, based on the capacity to adequately discern events requiring a decision. Globally, we have presented a cognitive architecture of RFs for decision making in the context of CI, with the idea of identifying and formatting tangible and intangible components that are the sources of risk for the DM.

A popular French saying states that “deciding not to decide is not the same thing as indecision”. However, it is important to know what factors lead a DM to not want to make a decision or to know what factors may affect their decision. Although a certain aversion to risk is understandable, it is evident that risk-free projects rarely produce great benefits! Consequently, we have concentrated on the decision process, taking account of the reasons behind a decision and associated risks. In the context of CI, we enumerated a number of possible RFs and the point at which they occur in different CI concepts. This allowed us, in particular, to cover data quality issues in the form of RFs.

We feel that the introduction of a cognitive perspective into the study of RFs linked to decision making in CI will not only facilitate determination of decision capacity but also act as a guarantee of quality for the decision, thus providing precious assistance to actors needing to make strategic decisions.

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¹ Chapter written by Olufade F.W. ONIFADE, Odile THIERY, Adenike O. OSOFISAN and Gérald DUFFING.