Introduction

The complexity and difficulty of problems faced by technical managers in today's global manufacturing environment are staggering. It is no longer enough to be an expert in the appropriate technical domains. Driving productivity and finding the right answers to the right problems at the right times require special skills in facilitating team problem solving and fostering collaboration among project stakeholders. These, in turn, require an increased understanding of how to manage both enabling and potentially disruptive cognitive differences among team members that can impact product design and market release dates.

A new generation of problem-solving leaders understands the need to be more than just conversant in key aspects of behavioral science. They recognize the importance of having a fundamental, yet rigorous working knowledge of human behavior that helps them identify and manage the effects of cognitive differences between team members and the strong impact those differences can have on project outcomes. These cognitive differences can be described and explained in terms of cognitive gap [Kirton, 2003], a psychological phenomenon that can appear in any problem solving endeavor. Here, we begin with a definition of cognitive gap, followed by some practical examples and suggestions for ways to manage it.

Cognitive Gap: A Definition

At a fundamental level, all humans use the same basic problem solving (cognitive) process, as we all have the same "model" brain [Kirton, 2003]. In other words, everyone is outfitted to be creative and solve problems. However, individuals carry out the steps of this basic problem solving process with different mental capacities and stored resources (i.e., diverse cognitive levels) and using different characteristic strategies or approaches (i.e., diverse cognitive styles). Cognitive gap can be used to describe these differences in ability and style among individuals; as illustrated in Figure 1, it appears in two general forms [Jablokow & Booth, 2006], as follows:

(i). Person-Problem gaps: differences between the nature or difficulty of a specific problem and the cognitive resources of the problem solvers who must solve it (e.g., G_1A and G_2A);

(ii). Person-Person gaps: differences between the cognitive abilities and approaches of the problem solvers themselves (e.g., G₁₂).

Figure 1. Cognitive gaps: between the problem and the problem solvers (G_1A, G_2A);
between problem solvers (G₁₂) [Jablokow & Booth, 2006]

In general, most technical managers understand and handle gaps in cognitive level quite well; they routinely assemble problem solving teams by matching the knowledge and expertise of the team members to the problem at hand. They are generally less familiar with cognitive style, however, so their management of gaps in style is less effective. In the best case, they may realize that there are differences in the ways people approach problems (i.e., their styles), but they don't have an accurate model for understanding and coordinating those differences. In the worst case, a manager may mistakenly assume that all problem solvers have the same basic problem solving style that he does, and that these other problem solvers will ("should") all arrive at the same solution, i.e., the one he thinks is best. This assumption is seriously flawed; the contrary is closer to the rule. Individuals with different problem solving styles are likely to arrive at different solutions to the same design or manufacturing process challenges, and these differences can be dramatic. Because cognitive style is generally less familiar to technical managers, and because it is so often confused with cognitive level, we will briefly describe cognitive style and its measurement.

Describing and Measuring Cognitive Style

According to the Adaption-Innovation (A-I) theory of M. J. Kirton [Kirton, 2003], the way in which a person prefers to manage structure when problem solving is at the heart of cognitive style. These different individual preferences or styles can be mapped along a bipolar continuum that ranges from strongly adaptive on one end to strongly innovative on the other (see Figure 2). In general, more adaptive individuals prefer more structure as they solve problems, with more of this structure consensually agreed, while more innovative problem solvers prefer less structure and are less concerned about gaining consensus around the structure they use. It is important to emphasize that the distinction between adaptive and innovative styles is not a dichotomy, but a full spectrum, the significance of which is best understood when the characteristics of different individuals are compared, rather than looking at the absolute position of any one person along the spectrum. So, it is better and more accurate to discuss people as "more innovative" or "more adaptive" than one another, rather than labelling them as "adaptors" or "innovators". In practice, A-I cognitive style is measured using the Kirton Adaption-Innovation inventory or KAI, which is one of the most highly validated psychological instruments currently available. The KAI must be administered by a certificated practitioner; more information about the KAI and the worldwide network of KAI practitioners can be obtained through the web site of the KAI Distribution Centre in the United Kingdom www.kaicentre.com.

Figure 2. Adaption-Innovation bipolar cognitive style spectrum

Some Practical Examples of Cognitive Gap

The impact of cognitive gaps can be experienced in any discipline and in any aspect of problem solving within that discipline. In engineering design, for example, cognitive gaps in level (e.g., knowledge, skills) between the requirements of a design problem and the mental resources of the designated designers can lead to certain failure of the project if they are not bridged through the expertise and know-how of new team members, outside consultants, or through additional training and education of the original team. Gaps in cognitive style requirements between a design problem and its designers can manifest in a poor understanding of the problem (e.g., more adaptive problem solvers seeking for clarification that is not available because the problem is ill-defined by nature) and/or solutions that don't satisfy the original design criteria (e.g., more innovative problem solvers redefining the problem in order to pursue a solution that fits their own personal preferences, but which doesn't solve the original problem).

Cognitive gaps in level and/or style between problem solvers, when small, can often be managed through healthy doses of appreciation and respect; they may not even be noticed in the short term. Larger gaps, however, especially when experienced over long projects, may lead to difficulties in communication, overly critical evaluations of others' work, and increased stress. When large gaps in style occur between problem solvers, they are often misinterpreted as gaps in level (i.e., "high in my case, low in yours") or other unrelated variables. In such cases, it is imperative that the differences are "unpacked" accurately, carefully, and quickly so that simple misunderstandings don't escalate into project-stopping crises. Once again, the recognition, definition, and appreciation of different styles and abilities is the best place to start.

Managing Cognitive Gap

One enabling effect of style and level differences is the increased robustness of solutions to the changing nature of problems: the greater the differences, the more robust the solutions can be. Exploiting this enabling quality depends on the team's ability to manage their differences effectively, however; if they cannot manage their problem-solving diversity, its benefits will never be applied to the problem at hand. Kirton describes this situation succinctly in terms of Problem A and Problem B [Kirton, 2003]: Problem A is the problem which the team has originally come together to solve; Problem B is the management of their individual differences. Successful teams spend much more time and energy on Problem A than on Problem B; teams that cannot manage the differences between themselves and/or the differences between their cognitive resources and the requirements of Problem A are bound to fail.

As a manager, you routinely assign project-critical tasks to those who have the right skills and experience in the right areas to ensure the success of the project. Likewise, task assignments should also be made based on preferred individual problem-solving styles in order to achieve the type of product design outcome desired. The fast pace of manufacturing and product development today demands that all team members - especially those in leadership roles - acquire the knowledge and skills needed to leverage all the aspects of problem-solving diversity in order to obtain robust design solutions. Acquiring this knowledge and these skills is a serious investment, but the results - i.e., cross-functional team members who know how to manage their knowledge and communication boundaries effectively in the solution of challenging and complex problems - are certainly worth it.

References

• Jablokow, K. W., and Booth, D. E. (2006). "The Impact and Management of Cognitive Gap in High Performance Product Development Organizations," to appear in The Journal of Engineering and Technology Management (Elsevier).
• Kirton, M. J. (2003). Adaption-Innovation in the Context of Diversity and Change, London: Routledge.

About the Authors:

Kathryn Jablokow, Ph.D.,is an Associate Professor of Mechanical Engineering at Penn State University, where she currently serves in the Great Valley School of Graduate Professional Studies (Malvern, PA). She is also the founding Director of Penn State Great Valley's Research Center for Problem Solving in Science & Engineering. Dr. Jablokow holds an advanced certification in Kirton's Adaption-Innovation inventory (KAI) and has worked closely with Dr. M. J. Kirton since 1997. Her research and consulting are focused on problem solving and creativity in science and engineering, robotics, and computational dynamics. Dr. Jablokow has trained in-house engineering and management staff of Fortune 500 companies in problem solving theory and its applications, and is currently investigating the relationship between invention and cognitive style. Dr. Jablokow obtained her Ph.D. in Electrical Engineering from The Ohio State University in 1989 and has won numerous awards for her teaching excellence, including the Penn State Outstanding Teaching Award, the Penn State Collaborative Teaching Award, and the W. M. Keck Foundation's Engineering Teaching Excellence Award (a national award). Dr. Jablokow is a Senior Member of IEEE and a Member of ASME, ASEE, Phi Kappa Phi, and Sigma Xi.

Dave Booth, PE is a principal of InvenGen Engineering and is experienced both as a technical leader and manager skilled in medical device development, manufacturing implementation, and applied product R&D program management. His career as an engineer and engineering manager has spanned over 25 years in the industry, during which he has created state-of-the-art product designs and developed manufacturing processes for OEM automotive & aircraft parts, commercial electronics, and over 20 medical devices involving multiple million dollar sales volumes annually. As an inventor, his intellectual property includes both disposable and reusable medical devices, as well as production equipment patents and manufacturing process trade secrets.

He holds two Master Degrees (business & engineering) from Penn State University, and a Bachelor of Mechanical Engineering from Villanova University. He is a registered professional engineer in the Commonwealth of Pennsylvania and a certified manufacturing engineer through the Society of Manufacturing Engineers. Additionally, Dave has published and presented his work in artificial intelligence systems, engineering organization, and design management. He is also certificated in the Kirton Adaption-Innovation inventory (KAI).