Cognitive Style in IS: Early Research and the Shift to Flexible System Design

In the earlier days of computing, when personal computers (PCs) became common, individual managers started using them to help make decisions. A lot of focus was placed on creating systems that could support decision-making. Out of all the personal factors that affected how people used these systems, cognitive style became one of the most important in Information Systems (IS) research. This is because cognitive style deals directly with how people handle and process information—how they take in and understand it.

Cognitive styles fall into two broad categories: systematic (or analytical) and intuitive. A systematic person processes information by carefully examining it, step by step, in a logical manner. On the other hand, an intuitive person looks at things as a whole and forms a “gestalt” or general impression. They don’t break things down piece by piece, but instead grasp the big picture all at once.

For example, a systematic person would likely prefer detailed data reports with tables and graphs. An intuitive person might prefer stories or narratives that give an overall sense of the information. So when designing systems, one question is: what kind of information should the system present, and how should it be displayed? Should it provide structured data like graphs and tables for systematic thinkers? Or should it give a more narrative-based, big-picture view for intuitive thinkers?

Cognitive style was the foundation for much of the early IS research, particularly at the University of Minnesota. This is where the famous “Minnesota experiments” took place. These experiments focused on decision-making, and the main goal was to find out what led to better decisions. Researchers tested different factors, such as how information was presented, the decision-making task, and the cognitive style of the decision-maker.

In these experiments, students were often given tasks and various types of information, such as graphs or tables. The researchers then measured which kinds of information helped the participants make better decisions, taking into account their cognitive styles. The idea was that aligning the information presentation with a person’s cognitive style would lead to better decision-making.

These experiments were the foundation of much of the MIS research that followed. People like Fred Davis, who developed the famous Technology Acceptance Model (TAM), were part of this tradition.

Then, along came George Huber, a well-known figure in the field, who made a bold claim: much of the research on cognitive style was a waste of time. His paper argued that trying to design systems around cognitive style wasn’t worth the effort, and it upset many people who had spent their careers working on experiments like the ones at Minnesota.

Huber gave several strong reasons for his argument:

1. Inconsistent Findings: Research on cognitive style produced conflicting results. Some studies found that cognitive style was important for decision-making, while others did not. This inconsistency made it hard to draw clear conclusions.
2. Poor Measurement: Many of the tools used to measure cognitive style were flawed. For example, some of the tests that were supposed to measure cognitive style were actually measuring cognitive effectiveness—how well people were able to solve problems—rather than their thinking style itself. This mix-up made the research unreliable.
3. Limited Practical Significance: While some experiments showed statistically significant results, the amount of variation they explained in decision-making was very small. So even if a study found a connection between cognitive style and decision-making, the actual impact was minimal.
4. Too Many Individual Differences: Cognitive style is just one of many personal traits that affect decision-making. People also have other characteristics, such as risk-taking tendencies, personality types, and emotional traits. Trying to design a system that accounts for every individual difference is nearly impossible, especially since these traits might contradict each other.
5. Manager Acceptance: Even if you managed to design a system tailored to a manager’s cognitive style, it’s unlikely they would accept or use it just because it matches how you think they process information. Managers don’t blindly accept such systems without questioning them.
6. Flexible Systems: Instead of creating systems tailored to specific cognitive styles, Huber argued it’s better to create flexible systems that adapt to different users. This is exactly what we see today with modern systems, such as Microsoft Office, which allow users to customize how they use the software based on their preferences. Rather than designing a system for one particular cognitive style, systems should be adaptable to many different styles.

In conclusion, Huber’s paper was a turning point. After it was published, the Minnesota experiments stopped, and researchers moved away from focusing on cognitive style in system design. The paper showed how to make a powerful argument that can influence the direction of an entire field. Huber provided six clear reasons why cognitive style research wasn’t effective, and his arguments were so convincing that they changed the course of research in the field.

While cognitive styles are stable traits and people tend to use them across different contexts, Huber’s argument was that systems should be flexible enough to adapt to different styles, rather than being designed for just one. Modern systems have embraced this flexibility, making them more useful for a wide range of users.