The Decision Maker's Cognitive Load

The Decision Maker's Cognitive Load

Lehan Stemmet (Faculty of Business and Information Technology, Manukau Institute of Technology, New Zealand) and M. Daud Ahmed (Faculty of Business and Information Technology, Manukau Institute of Technology, New Zealand)
DOI: 10.4018/978-1-4666-5888-2.ch635
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The development of innovative technologies has reduced manual and labour intensive operations, however, in many instances this has simultaneously increased the cognitive load placed on decision makers due to the collection of large amounts of heterogeneous data used for decision-making. The cognitive framework and limitations of the human mind has been researched extensively, but the application of this knowledge within the business context has not received a lot of attention. This article reviews cognitive load, memory, the effects of information overload and pressure on psychological and physical health, and subsequent impact on the decision-making ability. By combining various schools of thought, a Task-Information-Cognitive Load (TICL) framework is proposed to combat the effects of cognitive load. It then explored how the TICL framework can be aligned with the theories and practices of information systems (IS) and decision support systems (DSS) for the development of a Knowledge Management System (KMS) that helps to reduce the decision makers' cognitive load.
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Manufacturing and organisational processes have been transformed from manual and labour intensive operations to automated computerised systems during the past few decades. Innovative information systems, robotic, and automated technologies have been the leading drivers and catalysts for new product development and real-time service delivery. Information systems (IS) aim to support the operational and organisational processes and functions ranging from manufacturing to marketing to human resources to accounting and finance. However, with the new technology at our disposal becoming a more integral part of our work and personal lives, we experience a rapid growth in information flow that needs to be captured for decision support. Information is only useful and powerful when it enables real-time decision making with as little effort as possible. We largely fail to recognise cognitive engineering as having an effect on mental and psychological workload and having an impact on the decision makers’ efficiency and effectiveness, and eventually having an effect on organisational performance.

Decision makers work not only within complex globally connected entities including suppliers, customers, competitors, regulatory agencies and professional bodies, but they also deal with an abundance of information generated from these processes. Their cognitive load is much higher than that is considered from a pure process and mechanical perspective. Every decision maker has limited knowledge processing and absorption capacity, but increasing demands are being placed on individuals. During the last three decades, a considerable amount of research has been undertaken on the effects of job characteristics on the well-being of individuals, and the Job Demands-Resources model, in particular, has received much attention in this regard (Bakker & Demerouti, 2007).

Numerous organisational and personal factors, such as work pressure, role ambiguity, emotional demands, social support, autonomy, engagement, organisational commitment, performance feedback, and also job-related learning can influence psychological health (see Bakker & Demerouti, 2007). This article focuses on the impact of cognitive load on psychological health and the subsequent impact on making effective decisions.

Decision makers rely on the availability of useful information which they gather through directed and/or undirected knowledge discovery techniques using push or pull or both methods. Push data are generally used for directed discovery, which is generated or made available from internal activities such as transactions, meetings, communication records, and key performance indicators (KPIs) and benchmarking. Pull data are extracted from various sources, including the Internet and social media for undirected knowledge discovery. Information collected from various sources is not useful until it is restructured, cleansed, validated, combined, processed and presented in a useful manner. We often fail to recognise that many of these sophisticated systems may have reduced physical labour at the expense of increased cognitive load. This could even lead to making rushed decisions based on incomplete information. Individuals’ decision-making ability would be poor if information is cluttered, disorganised, inaccessible, inaccurate, incomplete, irrelevant, unreliable, complex, outdated and non-validated.

The human brain is by nature inclined to economise and use schemas and heuristics whenever it can, and often these are based on past experiences, and other mental shortcuts in order to cope with the amount of information flowing through the system (e.g. Winston, 2004). People become mentally exhausted as much as they become physically tired. Their brain processing diminishes with overstimulation, just as much as their body struggles when there is too much strain on it (e.g. Arnsten, Mazure & Sinha, 2012). In many instances, this seems to be ignored by many organisations and individuals for various reasons, including not being aware of the issues and their impact. Cognitive load is not directly comparable to the number of hours an individual spends at work, but exhaustion is related to mental fatigue that could lead to burn-out (Demerouti, Bakker, Nachreiner, & Ebbinghause, 2002).

Key Terms in this Chapter

Schemas: Organised cognitive patterns or frameworks of thought or behaviour to help individuals interpret the world around them, and reduce the effort required to do so.

Limited Capacity Assumption: The verbal/auditory and visual/pictorial channels have limited capacity.

Cognitive Load: A multidimensional construct representing the load that performing a task has on an individual’s cognitive system.

Dual Channel Assumption: Individuals extract information from the environment via a verbal (auditory) and visual (pictorial) channel.

Job Demands-Resources Model: Job demands refer to the physical, psychological, social or organisational aspects of a job that require physical, psychological effort and skills and are, therefore, associated with certain psycho-physiological costs. Job resources refer to the physiological, psychological, social or organisational aspects of the job that are functional in achieving the goals associated with the job, achieving personal growth or reduce the impact of job demands.

Information Systems: A package of software and hardware systems that support data-intensive applications.

Heuristics: Experience-based learning and problem-solving techniques. The solutions generated through the use of heuristics are not guaranteed to be optimal, but may be amended when an individual learns a new way to deal with a problem. Experienced individuals will have well-developed heuristics to deal effectively with problems they encounter.

Framework: A design pattern that organises a complex subject in abstract form, provides methodologies and guidelines for application development, and defines relationships among the components.

Cognitive Fit Theory: Performance is superior when there is correspondence between the information presented and the task that must be performed.

Active Processing Assumption: Selecting verbal/auditory and visual/pictorial input places demands on the cognitive system.

Decision Support Systems: systems/tools that affect the way people make decisions. In our present context, these are defined as systems that increase the intelligence density of data and support interactive decision analysis.

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