COGNITIVE STYLES AND THINKING PATTERNS

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Chapter 2: Cognitive Styles and Thinking Patterns

Introduction

Mental or cognitive models are powerful thinking tools or metaphors. When mental models are understood they can enhance communication, teamwork and decision-making, which can again enhance effective problem solving (Lumsdaine et al, 1999: 49). Flexible, critical and creative problem-solving skills are necessary in a rapidly changing world in order to cope with and find solutions for its many problems (Lumsdaine & Lumsdaine, 1995: 4). Making a decision on whether to start or not to start a venture is an example of such a problem-solving situation faced by the entrepreneur.
According to Ucbasaran & Westhead (2002: 6), habitual entrepreneurs may have a unique mindset that allows them to identify not only more opportunities but also more innovative ones. These cognitive processes include a greater reliance on entrepreneurial heuristics (see Chapter 3), which allow entrepreneurs to have at their disposal greater cognitive resources, which in turn facilitate higher levels of innovative activity.
The following three mental or cognitive styles / models are of specific interest for this study:

• Cognitive style
• Patterning system for understanding thinking
• The Whole Brain thinking model of Ned Herrmann (thinking preferences)

Cognitive style

Brigham & De Castro (2003: 44) attempt to provide an overview of the construct of cognitive style. These authors argue and quote Sadler-Smith & Badger (1998) that the cognitive style construct is widely recognised as an important determinant of individual behaviour. Cognitive style can be defined as an individual’s preferred and habitual approach to organising, representing and processing information (Streufert & Nogani 1998); a built-in and automatic way of responding to information and situations (Riding & Rayner, 1998); individual differences in the way people perceive, think and solve problems, learn and relate to others (Witkin, Moore, Goodenough & Cox, 1977); and individuals’ characteristics modes of perceiving, remembering and problem-solving (Messick, 1984) as quoted by Brigham & De Castro (2003: 44).
According to Brigham & De Castro (2003: 44), cognitive style is a higher-order heuristic and can be conceptualised as the way the individual’s brain is “hard-wired”. It leads to a consistent approach that people employ when they approach, frame and solve problems. They also quote Sadler-Smith & Badger (1998) who postulate that cognitive style has certain common characteristics:

• It is a pervasive dimension that can be assessed using psychometric techniques.
• It is stable over time.
• It is bipolar.
• It describes different, rather than better, thinking processes.

Brigham & De Castro (2003: 47) quote Rayner (2000) who argue that the contemporary field of cognitive style can be traced to basically three areas in psychology: perception, cognitive controls and processing. “Style” refers to various aspects of an individual’s performance, cognition, behaviour, motivation, learning, teaching, and organisational behaviour. Table 1.1 acknowledges the previous studies, not only in order to understand the foundations of cognitive style, but also to indicate the wide number of distinct labels and models that exist in the field.
Although certain dimensions of an individual’s cognitive style will remain stable over time (Allison & Hayes 1996; Kirton 1980), the style demands which a new venture makes on the entrepreneur will vary as the venture grows (Brigham & De Castro 2003: 50).
The term cognitive style has become widely used and many models and descriptions fall under the classification of cognitive style. For the purposes of this study, De Bono’s patterning system and the Herrmann Brain Dominance Instrument for thinking preferences, both which are cognitive styles, are further explored.

Patterning

Pattern recognition

Cognitive scientists have developed a method of studying pattern recognition, which means recognition of complex patterns of stimuli against a background of extraneous noise. This may help to provide new insights into the nature of opportunity recognition. To apply this to the entrepreneurial cognition domain, it can be argued that opportunities come into existence in the external world as a result of unrelated changes in technology, markets and government policies or regulations. However, these opportunities remain only a potential until someone “connects the dots” and perceives a pattern among them (Baron & Ward, 2004: 559).
According to Baron & Ward (2004: 559), the above issues regarding patterning should not be seen as exhaustive in any way. According to Krueger (2003), many other issues have not yet been examined in detail by entrepreneurial cognition researchers, for example:

• Do entrepreneurs show different patterns of creative thought from other individuals?
• Do they differ from other individuals with respect to the kind of tacit knowledge they possess in memory?

Recognising opportunities may involve perceiving connections between seemingly unrelated changes in technological, economic, political and social factors – a kind of pattern recognition. In order to perceive such links, however, individuals must possess knowledge structures that permit them to do so (Baron, 2003). In addition, they must access that knowledge in ways that lead to original and practical business ideas (Baron & Ward, 2004: 569).
In order to understand the concept of patterning as referred to above, the next section will explore patterning in more detail, on the basis of the work done by De Bono (1993).

De Bono on patterning

According to De Bono (1993: 49), the human brain works as a self-organising system in which incoming information organises itself into patterns and sequences. The author also postulates that a huge difference exists between “passive” or externally organised information systems, where information is laid out passively and has no activity of its own, and self-organising systems, where information is used and moved around. Our traditional information systems of thinking belong to the active self-organising systems.
In a remarkably simple manner, the nerve networks in the brain operate as a self-organising system that allows information to be organised into sequences. It seems (according to De Bono, 1993: 49) that the brain is designed to make sense of the world around us by forming routine patterns of perception from incoming information, and not to be creative. The result is that 90% of our lives are governed by established routines and patterns, and that 100% of our perceptions are the result thereof.
De Bono (1993: 171) further postulates that, for the first time in human history, we have begun to understand the difference between traditional passive information systems, in which information is moved about by a processor, and self-organising, active information systems, in which information organises itself into sequences and patterns. He points out that there is nothing sinister about this, and it can be linked to very simple ways in which nerve networks act as self-organising systems. De Bono suggests that once one understands the way in which self-organising systems create asymmetric patterns, we can understand why every valuable creative idea must always be logical hindsight.
Information forms the basis for any decision and can be seen as the oxygen of business. In his work De Bono uses the Four Wheels of Human Thinking metaphor to explain information processing in the brain. Figure 2.1 illustrates a series of funnels representing the patterns already established by the self-organising nature of human perception in our minds, meaning that whatever we see can only be perceived through these patterns. When one perceives a new idea, one has to speculate, imagine or hypothesise it first in order to find the already established pattern (De Bono, 1993: 34).
In a study done by Uchasaran & Westhead (2002) on the differences between novice and expert entrepreneurs, these authors argue that experts are able to manipulate incoming information into recognisable patterns and then match the information more strongly and transform it into appropriate actions. They also quote Hillerbrand (1989), who postulates that this capacity reduces the burden of cognitive processing and may have the advantage that information is more easily encoded in memory (providing further cognitive resources). This may lead to spotting of opportunities far more often, because of the experts’ ability to recognise complex information in their environment. Entrepreneurs’ greater information-processing capacity, due to increased cognitive resources, may lead to the identification of more novel and innovative opportunities. De Bono refers to this as cognitive resources patterning.
It seems that the main purpose of most people’s thinking is in fact to abolish thinking in an attempt to make sense out of confusion and uncertainty. De Bono (Tyler & De Bono, 2003: 12) say that the mind works to recognise familiar patterns in the outside world. Through patterning the mind is trying to find a familiar pattern and follow the already known route. This then makes further thinking unnecessary. An example of this phenomenon is driving a car. The moment you find a route known to you, you do not need to use a map or compass or ask for directions. Finding your way happens without your really thinking about it. In a way our thinking is an ongoing search for these familiar roads that make thinking unnecessary. The purpose of perception is to allow patterns to form and then to use them. The purpose of thinking, as we have said, is to find familiar patterns and so remove the need to think any more (Tyler & De Bono: 21).
In summary, we can say that patterning is the arrangement of information on the memory surface of the mind. A pattern is a repeatable sequence of neural activities. In practice a pattern is any repeatable concept, idea, thought or image. The pattern may also refer to an arrangement of other patterns, which together make up an approach to a problem, a point of view, a way of looking at things.
There is no limit to the size of the pattern and the only requirements are that a pattern should be repeatable, recognisable and usable (Tyler & De Bono: 26).
If one looks at the elements of entrepreneurial thinking, it appears that an entrepreneur uses unique patterning and preferences in the decision-making process. An entrepreneur is normally a positive person who asks why and how things work, sees possibilities, creates many ideas and handles ambiguity with ease.

Herrmann’s Whole Brain metaphor

Background

While De Bono uses the Four Wheels of Human Thinking metaphor (see Figure 2.1) to explain information processing and patterning in the brain, Herrmann also worked on human brain patterns and came up with the Whole Brain metaphorical model, consisting of four quadrants for determining thinking style preferences. The following section explores the thinking style preferences (patterning) as developed by Herrmann (1996).
While patterning and the use of patterns are normal functions of the brain, they differ from the creative and innovative thinking normally associated with entrepreneurs. Ko & Butler (2002: 2) quote Shaver & Scott (1991), who argue that some people discover opportunities because of their superior information-processing ability, search techniques and scanning behaviour. They also refer to Koestler’s (1976) theory that ideas exist in interrelated matrixes (groups of patterns). In normal thinking, one idea leads to another idea within the same matrix. Such information processing involves linking elements within the same matrix and thus produces no novelty.
When creative thinking is needed, however, one must move from one matrix to another. Such matrices of information include a number of alternative viewpoints and strength of believe related to amongst others, resources, customers and markets (Ko & Butler, 2002: 2).
It has long been recognised that people have different styles of knowing and thinking and that the left brain deals with the analytical, systematic and logical aspects, and the right brain with creativity and artistic and intuitive information (Lumsdaine & Binks, 2003: 47). However, it was Ned Herrmann, a scientist with a degree in physics who worked in the Human Resource Department of General Electric who, after years of research into creativity and the human brain, realised how specialised the brain is in its functions (Lumsdaine & Lumsdaine, 1995: 75; Lumsdaine & Binks, 2003: 49).
According to Herrmann (1995: 1), the brain is specialised physically and mentally and can be organised into four separate and distinct metaphorical quadrants, each with its own language, perception, values, gifts and ways of knowing and being. These four quadrants represent the four thinking structures of the brain. People are all unique mixes and these preferences result in different expressions of behaviour (Lumsdaine & Lumsdaine, 1995: 76). Herrmann then adopted a four-quadrant model of thinking which enabled a clearer understanding of how people think. Although the four quadrant thinking model was based on the divisions in the physical brain, it is a metaphorical model showing the brain’s complexity and versatility when involved in the simplest thinking task (Lumsdaine & Binks, 2003: 49).

CHAPTER ONE: BACKGROUND AND ORIENTATION TO THE PROBLEM
1.1 INTRODUCTION
1.2 PROBLEM STATEMENT
1.3 RESEARCH OBJECTIVES
1.4 DEMARCATION AND SCOPE OF THE STUDY
1.5 PROGRAMME OF INVESTIGATION
1.6 ORGANISATION OF THE STUDY
CHAPTER TWO: COGNITIVE STYLES AND THINKING PATTERNS
2.1 INTRODUCTION
2.2 COGNITIVE STYLE
2.3 PATTERNING
2.3.1 PATTERN RECOGNITION
2.3.2 DE BONO ON PATTERNING
2.4 HERRMANN’S WHOLE BRAAIN METAPHOR
2.4.1 BACKGROUND
2.4.2 PRINCIPLES OF THE HBDI
2.4.3 DESCRIPTION OF THE FOUR QUADRANTS
2.4.3.1 Quadrant A thinking
2.4.3.2 Quadrant B thinking
2.4.3.3 Quadrant C thinking
2.4.3.4 Quadrant D thinking
2.5 DIFFERENCES IN DOMINANCE
2.5.1 SINGLE DOMINANCE THINKING
2.5.2 DOUBLE DOMINANCE THINKING
2.5.2.1 Double dominant thinking (same hemisphere)
2.5.2.2 Double dominant thinking (cerebral or limbic)
2.5.2.3 Double dominant in opposite quadrants
2.5.3 TRIPLE DOMINANT THINKING
2.5.4 QUADRUPLE DOMINANCE
2.6 CONCLUSION
CHAPTER THREE: COGNITION, HEURISTICS AND BIASES
3.1 INTRODUCTION
3.2 DEFINITIONS OF HERISTICS AND BIAS, COGNITION AND ENTREPRENEURSHIP
3.2.1 HEURISTICS AND BIASES
3.2.1.1 Heuristics
3.2.1.2 Biases
3.2.2 COGNITION AND COGNITION PSYCHOLOGY
3.2.3 ENTREPRENEURSHIP
3.2.4 ENTREPRENEURIAL COGNITION
3.3 EXPLORING HEURISTICS AS A CONSTRUCT
3.3.1 MAJOR TYPES OF HEURISTICS
3.3.1.1 Availability heuristics
3.3.1.2 Representativeness heuristic
3.3.1.3 Framing heuristic
3.4 EXPLORING BIASES AS A CONSTRUCT
3.4.1 SPECIFIC BIASES
3.4.1.1 Overconfidence bias
3.4.1.2 The belief in the law of small numbers bias
3.4.1.3 Illusion of control bias
3.4.1.4 Planning fallacy bias
3.5 MISCONCEPTIONS
3.5.1 SPECIFIC MISCONCEPTIONS
3.5.1.1 Underestimating competitive response
3.5.1.2 Overestimating Demand
3.5.1.3 Misjudging the needs of complementary assets
3.5.1.4 Concept of fit
3.6 SELF-EFFICACY
3.7 RISK
3.7.1 TIME RISK
3.7.2 INVESTMENT RISK
3.7.3 TECHNICAL RISK
3.7.4 COMPETITIVE RISK
3.8 CONCLUSION
CHAPTER FOUR: ENTREPRENEURIAL PROCESS PERSPECTIVE
4.1 INTRODUCTION
4.2 TWO MODELS OF RELEVANCE
4.3 ENTREPRENEURIAL PROCESS
4.4 ENGAGING IN THE EXPLOITATION ACTIVITIES
4.5 CONCLUSION
CHAPTER FIVE: RESEARCH METHODOLOGY
5.1 INTRODUCTION
5.2 PROBLEM STATEMENT
5.3 HYPOTHESIS
5.4 RESEARCH METHODOLOGY
5.5 VALIDITY AND RELIABILITY
5.6 STATISTICAL ANALYSIS
5.7 OBJECTIVES, OUTCOMES AND CONTRIBUTIONS OF THE RESEARCH
5.8 CONCLUSION
CHAPTER SIX: FINDINGS
6.1 INTRODUCTION
6.2 FINDINGS 1
6.3 CONCLUSION
CHAPTER SEVEN: DISCUSSION OF FINDINGS
7.1 INTRODUCTION
7.2 HYPOTHESES
7.3 EMPIRICAL RESULTS
7.4 LIMITATIONS OF THE STUDY AND RECOMMENDATION
7.5 FINAL CONCLUSION
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