Monday, October 22, 2007

How Styles Affect Promotion Potential

By: Gary J. Salton, Ph.D., Chief R&D and CEO
Professional Communications, Inc.

"I Opt" research has revealed a statistically significant connection between "I Opt" strategic styles and organizational rank (e.g., manager, VP, CEO). The research is also able to reveal why this condition exists. The connection between style and rank is not a mere association. It is causal in the sense that X causes Y. The only way the relationships will change is if information flows change.

The implications for Leadership Development are clear. The current stress on skill sets and techniques is necessary but not sufficient. Prospective leaders must master a sequence of processing patterns suitable to the level to which they aspire. What works at one level will be suboptimal for another. There is no "one" leadership strategy suited to all levels

The statistics used were developed using a database in which rank can be associated with "I Opt" strategic style scores. This study includes the following elements:

124 CEO/Presidents
________This is the single individual who either
________________________owns the firm or is responsible only to
________________________the Board of Directors.

460 Vice Presidents________These are corporate level Vice
________________________who can make policy for the entire
________________________organization. It includes people identified
________________________as Presidents of subsidiaries. Nominal
Vice Presidents who are responsible for
departments, plants or other subsets
of the larger organization are classified
under Manager/Directors.

2,090 Managers/Directors____These are people who are responsible for a
________________________function within the firm. They are
typically accountable for multiple
supervisors and/or for the deployment
________________________of significant assets.

295 Supervisors____________These are first level supervisors who
typically manage people who perform
a particular activity within the firm.

6,028 Non-Supervisory_____These are people responsible for an activity.
It includes high level professionals (e.g., MD,
Ph.D., CPA, etc.) as well as unionized hourly
workers. Most people in this category are
professional level salaried employees.

The subjects of the study come from all parts of the United States. There is also a substantial international component (e.g., Europe, South America, Asia, Australia, Middle East, etc.). The firms in the sample include manufacturing, health care, finance, banking, non-profit, universities, government and many others. It is a reasonable basis from which to draw general conclusions.

"I Opt" Strategic Styles are strategies people use to resolve issues that arise in their lives. The dominant style is the first one a person is likely to try if it is not precluded by the nature of the issue. The four strategic style possibilities are:

Reactive Stimulator____The RS strategy uses unpatterned (e.g.,
spontaneous) input and action output.
It is characterized by rapid reaction
_______________________using expedient means.

Logical Processor______The LP strategy uses structured (e.g.,
planned, logical, systematic, etc.) input and
an action output. It is characterized by
exacting detail, high efficiency and
_______________________reliable execution.

Hypothetical Analyzer__The HA strategy uses structured input
and thought (e.g., plans, assessments,
recommendations, etc.) output. Well-
reasoned thought, exhaustive research
_______________________and complete understanding characterize
_______________________this approach.

Relational Innovator
___The RI strategy uses unpatterned input
and thought output. It is characterized
_______________________by an ability to identify
_______________________relationships and quickly generate
_______________________unexpected options.

The four strategic styles are NOT merely names attached to statistical associations. They are derived from theory and have been statistically verified along all eight dimensions of validity. They represent stable categories that can be relied upon.

Chart 1

Chart 1 compares rank to the dominant strategic style. It shows an explicit and consistent relationship. The higher the rank, the more likely the person is to use the RS and RI styles. The “stair-step” pattern suggests that the underlying cause is systematic. It appears to affect every level and every style in a methodically progressive way.

While the RS and RI styles are favored, they are not exclusive. At every level each style has at least some representation. This can have several explanations. First, styles say nothing about intelligence. You can be a genius using any style. Intelligence is a key competitive factor in the competition for rank. It is likely that this factor is reflected in the data.

Another factor is the nature of the firm. There are firms in the sample where exacting standards, high efficiency or methodical execution are keys to success. Here the LP style might be favored (e.g., food packaging, regulatory agencies and sub-assembly manufacturing). In other firms mastery of complex systems or avoiding error can give the HA style the edge (e.g., insurance, chip manufacturing and safety engineering).

Still another factor affecting strategic style representation is the stage in the life cycle of the firm. For example, about 40% of the CEOs in Chart 1 who have a dominant RS style were founders of their firm. This risk-taking, action-oriented style is well suited to launching a firm.

While there is room for everyone at every organizational level, there is nonetheless a clear bias toward the strategies using unpatterned (i.e., spontaneous) input—the RS and RI—at higher levels. This relationship is statistically significant at levels that fully meet academic standards (p<.001 ). This is not a statistical accident. Something is causing these results.


The dominant style analysis is categorical. It only tells you which style is dominant. It says nothing about how much the strength of one style exceeds another. For example, the strength of RI style may only be microscopically higher than the RS style. The categorical analysis would classify person as 100% RI.

Fortunately, “I Opt” can determine the exact strength of each style within a person’s behavioral repertoire. Since there are only four possible styles, the strengths can be stated as a percentage.

Chart 2

Style strength in Chart 2 shows the same relation between rank and style as did the categorical analysis. Once again the relationship is statistically significant (a free copy of the statistical analysis is available upon request). This finding is not a random event. It has an objective cause.

The stair step pattern within the “I Opt” strategic styles argues for a systematic cause. Every step up the ladder must involve increasing levels of whatever is driving the relationship. The one factor that varies directly with rank is information flow. It happens in every organization, industry and culture. It is a universal that can explain the finding across the broad range of industries sampled in this blog.

The Input Variable
The decision horizon moves outward with increases in rank. The information available becomes less concrete. There are no “facts” for what has not yet occurred. There are only inferences. The longer the time horizon, the less certain are those inferences.

The LP and HA styles use structured input (e.g., planned, logical, systematic, etc.) to excel at lower levels. They can take full advantage of the depth of information available. They are efficient and effective. The RI and RS styles use unpatterned input (e.g., spontaneous, naturally visible, unplanned, etc.). The random elements in this stream result in a loss of efficiency and effectiveness. The result is that the LP and HA have a natural competitive advantage at activities typical at lower levels.

The RS and RIs use of unpatterned inputs gives them an advantage at higher levels. They are better able to “see” possibilities that lie outside the scope of present conditions, assumptions or expectations. They are more comfortable with the uncertainty and vagueness. It gives them a competitive advantage at higher levels.

The Output Variable
Both the RS and RI use unpatterned input. The fact that the RI tends to exceed the RS at the upper levels (i
.e., VP and CEO) must be due to something other than input. That “something” is output elections.

The RS is an action-based strategy. It is a quick, decisive posture that seeks to directly cause the world to be different. Since action only makes sense in the relatively near-term, a natural shorter-range focus is created. In addition, the RSs tendency to use of expedient means increases risk. The cost of error grows as more assets are put into play or as strategic directions of a firm are changed. This caps the value of the RS at the highest levels. There is a limit to how much risk any organization can take.

Like the RS, the RI strategy uses unpatterned input. Unlike the RS, it is a thought-based strategy. It does not require action to be satisfied. It is focused on new ideas, novel relations, unexpected theories and other change-oriented initiatives. Since they are not forced into quick action, lower levels can vet these propositions before implementation. But, since they arise at a high level they cannot be dismissed out of hand. The possibility will be considered. There is no capacity limit in organizations to ideas. This gives the RI a competitive advantage over RS as rank increases.

Other Factors
There are other factors that can create advantages between the various styles. Visibility is one. Decisive action and unusual ideas get more attention than consistent performance. A natural affinity with people already at a high level is another. High level executives promote lower level ones. Talking the “same language” as higher level people helps promotional chances.

These and many other such factors operate in the real world. However, none of them would be able to completely offset the advantages or disadvantages created by information flows. If one were to use these other devices to gain high position, inferior performance would eventually be noticed. The likely result is replacement. Other tools might be able to get you to high position but they will not keep you there.

The findings reported in this blog expose a gap in current leadership development programs. A focus on techniques, methods and practices is valuable but insufficient. Candidates for leadership must be taught how to adjust their information processing profile to match the level that they are targeting.

This is not a one-size-fits-all prescription. Because RI is favored at the CEO level does not mean that aspiring leaders should be guided toward the highest level of that strategy. This would insure failure. To survive the lower levels leaders have to have at least threshold levels of LP and HA. Remember that you have to get through the lower levels to reach the higher.

The above observation highlights an interesting aside. The data used in this study focuses on success. It neglects to count the people who have suffered the consequences of failure in their attempt to rise up the hierarchy. For every RS and RI executive who has risen to VP or CEO there are many who have been fired, demoted or sidelined. The price of leadership development that neglects strategic styles is not only the failure to develop the talent in the LP/HA pool. It is also the unnecessary attrition of the RS and RIs.

It is beyond the scope of this blog to delve into changing strategic styles. Suffice it to say that we all have some capacity in each style. The more we use a style, the more skilled we become at it. The greater the skill, the more likely we are to use it again. It sounds easy. It is not. However, it is doable and it is an obligation of a complete Leadership Development program to provide the knowledge that is necessary to navigate the necessary transitions.

Tuesday, August 21, 2007

Adding Motivation to the Kolb Learning Model

By: Gary J. Salton, Ph.D., Chief R&D and CEO
Professional Communications, Inc.


All learning involves change. Any change requires energy. Motivation describes the amount of energy that a person is willing to spend. Without motivation, learning will not occur. With high enough motivation learning cannot be stopped.

Research was conducted on five classes in three cities in Texas, Arizona and North Carolina. It involved 184 learners participating in 5 classes. The result is the identification of two markedly different kinds of motivation that required different strategies to initiate and that differ markedly in power. These were measured and integrated into the Kolb Learning Model using "I Opt" technology (see for an outline of the validated technology and for its theoretical and operational foundations).

In addition, a new concept of "motivated learning" and a means to measure it was revealed. Applying this new concept disclosed a previously unrecognized learning issue. The concern involves a trade off in the depth and distribution of knowledge that can be controlled using the motivated learning concept and measurment.

Human motivation has two interrelated aspects, the physical and the mental. The mental domain is controlled by rational processes These are methods that use logic, reason and/or analytical thought (Cambridge Dictionary, 2002). The “pleasure principle” of utilitarianism can be used to estimate rational motivation. People seek pleasure and avoid pain. Motivation is calculated by subtracting costs (i.e., pain) from benefits (i.e., pleasure). The more benefits exceed costs, the greater the rational motivation.

The physical element is controlled by biochemistry. “Feelings” are used to describe the emotional state that is being experienced. For example, a person can feel depressed, anxious or elated. These states can influence the attention a person is prepared to devote to a subject. They also affect a willingness to dedicate energy to learning.

Rational Motivation
Rational motivation is the “what’s in it for me” element. It was measured by asking class members two questions several days prior to the class:

Table 1

Both questions use ordinal (rank ordered) scales. Subtracting ordinal scales has no crisp mathematical meaning. However, fuzzy logic inference can help. Subtracting “a little” from “a lot” does suggest that there will be something left of whatever there was a lot of. Subtracting costs from benefits seems to make logical, if not mathematical, sense.

Effort was made to insure a compatibility of scales. The two questions were asked at the same time and in the same context. The learner probably used the same mental calculus in answering both questions. This gives some justification for netting the two responses. The calculation is rough but was accepted as a measure of the rational motivation of the learner.

Emotional Motivation
Emotional motivation is a biochemical event (a feeling) not a rational calculation. It is a judgment of desirability of this “feeling.” It appears as a full-blown state that may or may not have a rational basis. It was measured by asking:

Table 2

The scaling is again ordinal. There is no assurance that the distance between the categories is consistent. The statistics used to assess the results must be able to handle this inexactness.

Rational and emotional motivation are the two aspects that an instructor has to work with in increasing the energy the learner is willing to devote to learning. However, before proceeding it is worth testing whether they are really two different things in practice as well as theory.

Rational and Emotional Motivation are different things
The two forms of motivation can influence each other. Feeling gloomy (an emotion) can increase sensitivity to costs and diminish the value of benefits. Similarly, rational benefits can rise to a level that overwhelms whatever "feelings" you might have.

A professional mathematician, Robert C. Soltysik, was engaged to do the mathematics. He used a Multiresponse Permutation Procedure (MRPP) to test the hypothesis that the two kinds of motivation are different. The result was an estimated probability that the two types of motivation were drawn from the same distribution as p<.0000000001. Rob crosschecked the result with a Monte Carlo approximation. After 1000 iterations, it came up with p < .001 . Rob was able to state that " there is strong evidence that emotional and rational do not come from the same distribution." It is a safe bet that emotional and rational motivations are two different things in practice as well as theory.


The 5 classes in the study all used the same well-established program intended to show the value of teamwork. The test was whether the class had long-term, visible effect on performance. The procedure was to wait 4 months and then ask supervisors to rate their subordinates on the improvement they actually witnessed. The following scale was used to gauge performance:

Table 3

Unfortunately, the firm underwent reorganization during the study period. Some people were laid off and others no longer reported to the same supervisor. This resulted in reduction of sample size from 184 to 89 people. The loss was disappointing but enough people remained to warrant proceeding with the study.

The 89 people reported to 58 different supervisors with positions of Vice President to Assistant Account Manager. The large number and the different levels of supervisors minimize the probability of evaluator bias. The measure is probably a fair estimate of the value the class contributed to performance.

Comparing the motivation with the supervisor’s judgment of improvement requires that the rational and emotional scales be set to a common standard. Rob did this by subtracting the median value from each motivation data point. He then divided that total by the sum of the absolute values to create a standardized measure used in his calculations. This is a variant of a well recognized and accepted statistical technique called P-Standardization.

The broader research of which blog is a part, used multivariate optimal discriminant analysis (ODA) to predict the rank order position of each student in a class. Rob applied ODA to construct a model that used 5 variables including emotional and rational motivation. This multivariable model was able to predict the highest and lowest achievers in a class with 82.1% accuracy.

Unfortunately, Rob’s 100-iteration Monte Carlo simulation yielded a significance level of p< .15—about 1 chance in 7 that this result was produced by random events. This fell short of the academic p< .05 standard (one chance in 20) but does perform better than pure chance. The multivariable model was accepted for use in this study as a crosscheck.

Rob also created a model that compared the stand-alone effect of motivation on performance. This was called the univariable (i.e., one variable) model. The univariable model looked at how motivation affected performance while ignoring any influence other factors might have on the motivation-performance relationship.

Applying the standardized variables to the single and multivariate model produces coefficients. These measure the degree that motivation affects the performance outcome (e.g., Performance = Coefficient x Standardized Motivation Measure). Since the variables were standardized, the ratios of these coefficients serve as a measure of the relative power of both forms of motivation (see Table 4).

Table 4

Both of Rob’s models give the same direction. Depending on which you accept, emotional motivation is between 3 and 6.8 times more powerful than its rational counterpart. Just to confirm these results, Rob also calculated a statistic called Kendall's tau using the univariable model. Tau is a measure of "goodness" of fit. The ratio of taus also measures relative power. The result of this was:

Table 5

The tau calculation indicates that emotional is about 4 times as powerful as rational motivation. It is in the right direction and within the same range as the other two calculations. It is probably safe to say that emotional motivation is significantly more powerful than its rational counterpart.

This research used job improvement as a measure of learning. This means that course content will always be the single most important variable. Courses on conflict resolution will not yield any performance improvement if there is no conflict to resolve. The more relevant the course is to actual work issues, the greater will be measurable learning effect. "Needs Assessment" will always be the Holy Grail of Training & Development. There is no substitute for teaching the right course.

Motivation does not affect the way a course is taught. That is the realm of the Kolb Learning model. But motivation does affect how well Kolb will work—the higher the motivation the more that will be learned. You can read the strength of the connection between motivation and performance directly from the tau. Table 5 shows that emotional motivation accounts for about 13% (tau coefficient of .1328) of the performance improvement. Rational motivation was about 3% (tau coefficient of .0334). Next to course content (i.e., the subject being taught), emotional motivation is the single largest factor in teaching success .

It is worth noting that the 13% effect of emotional motivation is NOT based on information acquired or retained. It is a 13% improvement in the information applied to the benefit of the organization that is funding the learning. Effectively, it is a 13% improvement in productivity in the subject matter area of the class. This is an advantage of using job performance rather than conventional "tests" as a measure of teaching success.

Motivation is not a constant. It can be changed. But to do so effectively requires that instructors, designers and management understand the differences in its two components.

It Emotional motivation is based on biochemistry. To affect it you must effect the biochemistry of the learners. This means it has to have a physical impact. Three ways stand out as possible avenues:

Table 6
  • Humor: Laughter is a physical response. The body moves. Chemistry is altered as this occurs. Laughter establishes rapport (a type of bond) and this increases receptivity. It is no accident that most speeches begin with a joke.
  • Empathy: Observing another person’s emotional state causes a system of mirror neurons to fire in the brain. Wincing when seeing another person's mishap is evidence of this human capacity. You don't choose to feel the other person's pain, you just do. Displaying enthusiasm, passion or similar emotions tap into this aspect of human physiology. They can help setup a positive learning state. Anything that shows the emotional state occurring can trigger the mirror neurons. Instructors, students or even videos can be used to cause the mirror neurons to fire. All that is needed is a situation.
  • Activity: These are planned events that involve energetic actions or movements. They can involve things like role-playing, ropes events or any other undertaking that generates an emotional response in the people participating. Unlike empathy, the direction of the emotion is harder to control when using activities. For example, a "ropes" exercise can generate fear as easily as excitement. Caution is always indicated when designing an activity intended to generate an emotional response.

Empathy, humor and activity are universal options. Initiatives focused on generating curiosity, building confidence or creating a sense of pride are among the other options. In general, anything that affects “feelings” can be an emotional motivational tool.

Rational motivation focuses on "what's in it for me." It is an intellectual calculation involving netting attraction (benefits) and aversion (costs). The more that benefits exceed cost, the more rational motivation.

The costs of attending learning events are largely personal. The choice of the date, the location and current workload are examples of things that can affect aversion. These are all local to an individual. Things that can be done but generally aversion is difficult to influence.

Benefit is easier to effect. The first thing is to get them noticed. Showing that benefits are wide in scope, long in duration, high in certainty and quick to realize magnifies attraction. Table 7 gives a useful formula for maximizing the benefit component of rational motivation.

Table 7
  1. State a feature of the learning. For example, “you will learn to direct potential conflict into productive channels.”
  2. Assert an objective result. For example, “you will learn to reduce the stress on yourself and everyone around you.”
  3. Specify an advantage arising from the result. For example, “you will develop a reputation as a person who can handle difficult situations and make a silk purse out of a sows ear.”

A previous blog demonstrated that the Kolb Learning model is an applied subset of the “I Opt” information-processing model. “I Opt” adds exact measurement to the Kolb skeleton. This means that individual learning profiles can be specified. Examples of different learning profiles of actual people participating in this study are shown in Graphic 1.

Graphic 1

An "I Opt" learning profile defines “how” but not “how much” a person wants to learn. The measures outlined in this blog can be used to estimate the desire to learn. The 2D learning profile of Graphic 1 can be expanded to 3D to show this. The height of the profile describes the degree of motivation (see Graphic 2).

Graphic 2

Individual motivation does not affect the teaching strategy. All of the learning quadrants are equally affected. However, when applied to groups, things change.

Graphic 3a (left) combines the learning profiles shown above. Overlaying the two-dimensional learning profiles defines the best way to teach. In this case, stressing the “I Opt” Changer (Kolb Accommodating) and “I Opt” Performer (Kolb Diverging) learning styles about equally will maximize total knowledge transfer.

Graphic 3

Individual Graphic 3b adds motivation to the 2D graphic. The person favoring the "I Opt" Changer style is more motivated. Putting more stress on her favored Changer (Kolb Accommodating) style would result in more information being available to the firm. The Changer person would absorb more. The person favoring the "I Opt” Performer (Kolb Diverging) style would learn but would not be optimized.

This illustration shows a largely unrecognized trade off inherent in teaching. If you use learning styles and ignore motivation, you optimize the scope of knowledge transfer. You will distribute knowledge broadly. If you use motivation along with learning style, you optimize the amount (i.e., depth) of information transferred. The cost is that this knowledge may be resident in fewer people.

There is no “right” or “wrong” in the choice. There are situations where either strategy might be best. For example, you may be teaching a team who will work together regularly. In this case having the knowledge available in someone is what counts; its distribution is secondary.

If, however, you are teaching people from a variety of departments your goal is probably the dispersal of knowledge. In that case the 2D choice of using the learning styles alone would be best. Less information would be installed but it would be more widely dispersed.

What can be done with two people can also be done with larger groups. “I Opt” technology is also not confined to illustrations. Actual calculations are possible. Graphic 4 shows the technology applied to an actual class of 7 people in a Time Management class.

Graphic 4

Graphic 4 shows a 3D graphic of the 7 people with the percentage allocation of each teaching style indicated. This percentage was obtained by calculating the volume of each person's 3D learning profile in each quadrant. Adding the volume of the 7 profiles gives a measure of "motivated learning" preferences in each quadrant. Proportioning these preferences indicates how much attention should be given to each learning style.

The above calculations relaxed mathematical purity. The exact (i.e., ratio) scales of "I Opt" were multiplied by the ordinal (rank order) scales of motivation to get volume. This is a bit of a mathematical leap. However, the direction is probably right if not precisely accurate.

Motivation has long been recognized as a component of learning. This blog advances prior work by showing how it can be systematically managed. It then shows how it can be seamlessly integrated into the well-accepted Kolb learning model. In this process, a poorly recognized tradeoff between scope and depth of knowledge transferred has been highlighted. While not the final word on the subject, it is hoped that this will be a step forward.

Wednesday, June 06, 2007

Optimizing the Kolb Learning Model

Gary J. Salton, Ph.D.


The Kolb learning model (Kolb & Fry, 1975) is the dominant paradigm in adult learning. It holds this position because it is a practical, easily understood taxonomy that works to an acceptable degree in field settings.

Kolb has its limits. Most importantly, it is based on psychology—the science of the individual. Most instruction occurs in a classroom setting. This is the realm of sociology—the science of groups. Kolb provides no set way of getting from the individual to the group. This is a serious shortfall for those interested in large scale training efforts.

This research blog estimates the size of the shortfall. Data was collected from 185 actual adult classes involving 3,116 individuals in 5 different organizations. Classes were held in cities in the United States, Asia and Europe. While not random, the sample is broad enough to suggest a level of credibility.

"I Opt" technology was applied to the data to bridge the psychology-sociology gap. "I Opt" is a theoretically founded, fully validated and tested knowledge base (see
for "I Opt" theoretical underpinnings or for operational tools). "I Opt" is an information-processing tool. Since information processing is common to both individuals and groups is a natural bridge between the individual and group.


Both Kolb and "I Opt" use a four-axis model. Each axis of the "I Opt" model is a combination of input and output elections. Some of the behaviors produced by these interactions can be seen as the "orientations" that Kolb uses in his taxonomy (i.e. classification system). In other words, the Kolb conceptual model can be seen as a subset of the broader "I Opt" paradigm.


Graphic 1 contrasts Kolb and "I Opt". Kolb is a conceptual scheme whereas "I Opt" is a measurement grid. The axes on the "I Opt" model are called "styles" whereas Kolb calls his conceptual variables "stages." The difference is more than just names. Kolb's model rests on MBTI psychology. There is no way to measure his stages except by asking people for their subjective assessment. There is no way to know if what one person means by "sometimes" is the same as what another person means. You can add Kolb's scores but you could be adding apples and oranges. You would never know.

The "I Opt" input and output elections are measured on an absolute rather than subjective scale. The absolute scale is what allows the elections of individuals to be combined into groups. This ability to "add up" people is what gives "I Opt" the power to bridge the individual-group gap.

"I Opt" can combine its style "building blocks" to produce different things (see here for list) . For example, the "I Opt" Hypothetical Analyzer style uses structured input and thought (conceptual) output. Structured input (a method that uses some form of reasoning to select input) combines with conceptual output (logical combination of particulars). This produces systems, theories and explanations. These products are all various levels of abstraction and are roughly consistent with Kolb's abstraction category. Kolb's other conceptual variables can be similarly derived from the "I Opt" model.

The match between "I Opt" and the Kolb is not perfect. However, it is sufficiently aligned to allow the data collected within an "I Opt" framework to be applied to the Kolb model.


Kolb sees a circular or spiral sequence of stages. Without measurement Kolb's model cannot tell you how much emphasis to give each stage. Rather, he suggests "touching all the bases." Without measurement there is no way of defining how hard the bases are to be "touched." Applying "I Opt" measurements from 3,116 people allows us to define what Kolb cannot.

Percent of Students at Various Commitment Levels: "I Opt" RI and RS Styles

Graphic 2 reveals that 81% of people do not heavily rely on the Concrete (1) or New Situations (4) stages ("I Opt" RS and RI styles). Fully 48% of people prefer very low levels. Instruction that exceeds these levels will appear irrelevant and difficult to assimilate.

Percent of Students at Various Commitment Levels: "I Opt" LP and HA Styles
Graphic 3 shows that Kolb's Observe & Reflect (2) and Concept (3) stages operate in almost the reverse fashion. Fully 42% of people would prefer medium-high to very high levels of this instruction. Falling short of this standard will leave people feeling inadequately prepared.

Graphic 4 shows a cumulative distribution of the four Kolb stages. Extending a line form a desired coverage level shows the level emphasis needed. The line on the graph shows that targeting of 75% coverage would require different emphasis on the various Kolb stages.

"I Opt" is not confined to Kolb's ordinal (e.g., low, medium, high) measures. The actual strength of each style (Kolb stage) in Graphic 4 can be measured. Graphic 5 shows a pie chart distribution of these strengths. Because the I Opt scores are not simple categories but rather are the result of information processing elections, the pie chart can also tell you how to design instruction in terms of input-output structure.

Graphic 5 shows that the designer should give about equal attention to thought and action—the "how" and "why" of the subject. However, in terms of input a 60-40 rule seems optimal. About 60% of instruction should be structured—a “this causes that” or “do this, then do that” sequencing. This is knowledge in a firm framework.

The remaining 40% should be unpatterned input. This is more spontaneous and less predictable knowledge flow. It typically involves decisions by students. For example, students might be asked to provide examples of applications or be asked to apply the principles to a subject. Their choice of examples or methods of applying principles will vary. This is knowledge in a "fuzzy" framework.


Kolb recognized that people learn by combining his stages. He calls these combinations of stages “learning styles.” These are shown as diagonals in Kolb’s basic model. I Opt also has diagonal interconnections. I Opt uses the term “patterns” to describe these interactions. Graphic 6 compares the two approaches. The parallels are obvious.


Visual similarity does not mean identity. “I Opt” technology has exact measurement. The Kolb learning styles can never be measured. This is because the scales used for each stage do not have a common denominator. "I Opt" is different. Each axis has an input or output election in common with an adjacent axes. This commonality along with identical axis scoring methods means that diagonals can be quantified. This means learning styles can be measured exactly.

Graphic 7 shows how measurement is done. The profiles of two real individuals from the 3,116-person research base are shown in Graphic 7A. Deciding how to teach these two people together would be a challenge if you were limited to the Kolb taxonomy.


“I Opt” offers a solution. "I Opt" profiles are not mere illustrations. They are exact measurements of information processing preferences. They can be superimposed. This creates a “common area” that describes an information flow acceptable to both people. The common area for Dave and Teresa is shown in white in Graphic 7B. The percentage of common (i.e., white) area in each quadrant describes the amount of that kind of instruction that will jointly serve the people involved. Clearly, touching all bases equally is a poor solution for this pair.

What can be done for 2 people can be done for an entire class. Graphic 8 shows the profile of the class in which Dave and Teresa participated. The gray area is the common area of a majority of the 16 people in the class (i.e., at least 9 people fall in every point in this area). This majority area is where most people share a common learning preference. If your purpose is to teach as many people as much as possible, this is majority area defines your optimal strategy.


The yellow dots are in Graphic 8 are the centroids of each person in the class. A centroid is the single point that best represents a person’s whole profile (i.e., Cartesian Average). Centroids help give a quick picture of how individual people fit into the group as a whole. For example, this one shows that both Dave and Teresa are likely to be a bit dissatisfied with the group strategy. This is another advantage of “I Opt.” You can see who will need to be compromised and by how much in order to serve most people in the class. Taxonomies can never give this kind of information.


Individual classes vary widely in their learning preference. Graphic 9 shows a random selection drawn from the 185 classes in which the 3,116 people used in this research participated.


Actual classes conducted within the 185 class sample

All classes have at least some representation in each learning style (i.e., I Opt pattern). This means that almost any teaching strategy will work to some degree. However, the differences between classes illustrate that no instructional strategy that will optimize all of them. What ever you do will be a compromise. The issue is whether your compromise is optimal.

Instructional Designers are concerned with optimizing the learning of groups, not individuals. This means focusing on the learning styles (rather than stages) of the actual classes that will be taught. Graphic 10 shows the distribution for each of the four learning styles among of 185 classes measured in this research.

Applied to the profiles of 185 classes
Graphic 10 also shows the midpoint of the each of the Kolb learning styles (i.e., "I Opt" Patterns). The midpoint (median) is where half of the classes fall on either side. A strategy of targeting the midpoint would minimize the degree that a learning style will exceed or fall short in a typical class. Instruction designed to hit this point would minimize the deviation an instructor would have to adjust for in particular classes. It is a viable option.

Graphic 11 compares the "touch all styles equally" and the midpoint design strategy. There is a 70% overlap in the two learning profiles. However, it also shows that designers should plan to give about twice more instruction in the Assimilating (B) than the Accommodating (D) learning styles. The difference between a 70% and 100% overlap can mean a lot.

Based on 185 Classes
This blog uses a large and diverse research population. However, the other blogs in this series show that there can be systematic differences by firm, function or organizational level. The safest course in designing instructional policy is to sample the actual population being taught and use it as a standard. In final analysis there is no substitute for exact measurement.


The research has also shown that improvement in the Kolb model is possible. "I Opt" technology allows its concepts to be extended to groups. It can be used to measure the systematic biases in their learning preferences. It can show you exactly which individuals will be "short changed" when using an instructional design that is best for the group. Finally, it illustrates that reconciling psychology and sociology can magnify the reach and power of the learning professional.

Kolb. D. A. and Fry, R. (1975) 'Toward an applied theory of experiential learning', in C. Cooper (ed.) Theories of Group Process, London: John Wiley.

Sunday, April 15, 2007

Structural Barriers to Change and Innovation in Nursing

Beatrice J. Kalisch, Ph.D., RN, FAAN, Titus Professor of Nursing, University of Michigan
Suzanne Begeny, MS, RN, Ph.D. Applicant, University of Michigan

This blog is based on an article published in the peer-reviewed academic journal Nursing Administration Quarterly in October/December 2006 (Vol. 30, No. 4, pp. 330-339). It is available for purchase at the journal’s website under the title Nurses Information Processing Patterns: Impact on Change and Innovation. This blog summarizes and extends the findings reported in that article. Information on the “I Opt” tools used can be found on Information on the intellectual underpinnings of “I Opt” technology can be found on

The focus of this research was the ability of nurses to accept, adopt and sponsor change in an era of high volatility in health care. The research was conducted at two geographically separated hospitals and involved a total of 578 nurses.

The research drew on the fully validated “I Opt” instrument that measures information processing preferences. It is based on the simple idea that the kind of information sought and accepted from the environment limits or facilitates certain types of behavior. For example, if you do not pay attention to detail, your responses cannot be precise. It does not matter how you "feel" about it. Similarly, if you choose to rigorously apply well-understood methods, you foreclose your ability to discover new, groundbreaking things. Closely following established methods blinds you to unexpected options. By governing the knowledge available a person’s "I Opt"
(Input-Output-Processing-Template) profile systematically governs the behavior that can and will appear.


The study was designed to address specific hypothesis with a high degree of academic rigor. Statistical significance testing used the academically accepted standard of p<.05 (95% probability that the result was not due to chance) or better. In the journal these findings are presented in a standardized academic format. Here they are presented in an operationally accessible manner more useful to executives and managers who are positioned to make use of them.

Finding 1: Nurses Differ Markedly from Non-Nurses
Nurses were compared to other functional areas. These areas included:

Customer Service Personnel (n=599)

Hourly Industrial Workers (n=71)

Plant Operations Management (n=593)

School Teachers (n=608)

Engineers (n=945)

Scientists (n=48)

Corporate Managers (n=721)

The findings indicated that nurses most closely resemble Customer Service Personnel. Customer Service is typically involved in authoritatively answering client questions by referring to approved information sources. There was no significant difference across all four “I Opt” Strategic styles between nurses and customer service personnel.

At the other extreme, nurses had significant differences along all four “I Opt” Strategic styles with Engineers, Scientists and Corporate Managers. These disciplines consistently scored higher in the unpatterned input strategies of Relational Innovator
(ideas) and Reactive Stimulator (decisive action) than their nursing counterparts. These are the adaptive styles that are able to initiate and accommodate change with relative ease.

In the middle zone, nurses were significantly less idea oriented
(RI) than schoolteachers and Plant Operations management. The nurses significantly exceeded these functions in disciplined action (LP). This suggests that these areas will find change and innovation more palatable than will nurses. Hourly Industrial workers significantly exceeded nurses in both disciplined action (LP) and analysis (HA) but equaled nurses in ideas (RI) and decisive action (RS).

The picture painted by this analysis is that nurses are among the least able to initiate and accept change among the functions reviewed. Their favored information processing strategy relies on certainty, precision and step-by-step operational knowledge. This posture forecloses information needed to initiate change
(unproven possibilities, unexpected associations, etc.) and accept it readily (e.g., a willingness to use tools that may not be fully specified).

Nurses can, of course, adapt to change. However, if the same methods as used for engineers, scientists and corporate managers are applied to nurses the road will be difficult. And, it will be difficult for everyone. Nurses are likely to feel emotional pain as they are forced out of their preferred approach to life. The sponsors of change will feel pain as they meet reluctance and resistance. Standard business school management strategies must be modified if they are to be fully effective in nursing situations.

Finding 2: Nursing Specializations are more Similar than Different
The nursing data identified the specialization within which the nurse respondents worked. These areas included:

Emergency Room
Intensive Care

The results of the research showed that there was no statistically significant difference between specialties in the disciplined action (LP) or analytical (HA) strategic styles. This means that all specialties are about equally committed to structured approaches.

There was some variation in unpatterned strategies (RS/RI). Nurses in Intensive Care and psychiatric were more idea oriented (RI) than the others. This seems to make some sense in that these areas are most likely to encounter cases that do not yield to standardized approaches.

The other nursing specialties scored significantly lower in either decisive action (RS) or novel ideas and approaches (RI). For example, surgical was significantly lower in spontaneous decisive action (RS). Again this makes sense. The operating room is a hierarchical system functioning under the direction of the surgeon. There is little room for independent nursing action.

This area of research has an implication. The commonality of disciplined action (LP) and analysis (HA) means that all of the nurses can be successfully approached using the same methods. This means that a uniform change management can be applied. The efficiencies available in a common approach can be captured.

Finding 3: Staff Roles are Different

The data collected identified the organizational role of the individuals sampled. This allowed direct comparisons of staff roles.

The first comparison was between Registered Nurses (n=344) and Nurse Aides (n=101). The Nurse Aides registered statistically higher levels disciplined action
(LP). In all other dimensions the RNs exceeded levels of the Nurse Aides. This suggests that the difficulties with change and innovation extend down the organization. The RN’s reluctance to change will be magnified by the Nurse Aides who support them.

The RNs were then compared to Unit Secretaries (n=40). Like the Nurse Aides, the Unit Secretaries are significantly more committed to disciplined action
(LP) than are the RNs. This means that there is still another layer of potential resistance to change that regularly interacts with and influences the RNs.

Finally, RNs were compare to Licensed Practical Nurses. The LPNs were significantly more committed to disciplined action
(LP) than are the RNs. Still another layer of change resistance is added below the level of the RN.

The general sense arising from this portion of the analysis is that the difficulty in accepting, adopting and sponsoring change is even more difficult than it first appears. The nurses rest on an organizational support system that is even more reluctant to change than they are. Since these support functions interact constantly with the RNs, they are positioned to magnify the RNs basic disinclination toward change. A difficult job has become more difficult.

The implication of this finding is that change initiatives must be organizationally sensitive. They must understand that addressing the RNs is not the end game. They must also address the levels organizationally below the RN. They must also understand that the RN will face a challenge when they ultimately accept change. They must then turn around and “sell” it to their support staff. This is likely to be as difficult as “selling” the change to the RNs in the first place.

Finding 4: Nursing Managers are Different than RNs

Staff nurses in all specialties (n=344) were compared to Nurse Managers (n=52). The Nurse Managers had much higher levels of RI
(ideas) and RS (decisive action). Overall, they resemble the average manager in other functional areas of society.

The implication of this is that Nurse Managers faces a challenge in sponsoring and guiding change initiatives. They do not think like the people that report to them. If they follow the “golden rule” they will do unto the staff as they would have done unto themselves. If they do this, the result will fall far short of optimal. The average RN does not want to be treated like a manager. They are sensitive to different variables, have different standards and are comfortable in different environments.

An analysis done outside of this study suggests that Nurse Managers strongly resemble managers in other industries. But there is a difference. In other industries the staffs being supervised are more diverse. Diversity means that there is likely to be someone relatively near the manager’s posture who can interpret matters in a way others can understand and accept. This creates “emissaries” to help managers “sell” their ideas and postures to the larger staff.

Nurse Managers do not have this luxury. Their job is more difficult. As shown in the next section, they face a solid wall of likely resistance. This means that Nurse Managers will need to be taught to lead people who are very different from themselves. Standard business school management education applies but must be supplemented if Nurse Managers are to be fully effective.


In addition to the distinctions cited above, another aspect of the nursing staff became visible in the course of the study. The nurses are tightly clustered. This condition is shown in Graphic 1. It shows the distribution of actual nurses in one section of a major hospital. It is representative of the general distribution encountered in both hospitals and across all areas.


Each dot on the graphic represents the centroid (point of central tendency) of the information processing profile a particular nurse. There is a clear clustering in the lower right hand quadrant. This is the “Conservator” pattern. It seeks to conserve or preserve things that are proven and known to work. People using it strive for excellence and precise execution. It is the information processing posture that has the most structural resistance to change.

The implication of this condition is that it magnifies the difficulty of change even further. The nurses are personally disinclined toward change and have a staff that is even more reluctant to change. In addition, there is now a coalition factor. Nurses are likely to magnify their reluctance to change in there interactions with each other. The tight clustering means that they tend to think alike and will see much merit in each other’s judgments. A very difficult job has just been made more difficult.

The overall picture painted by this research and outlined in this blog is that Change Management in nursing will present a unique challenge to all involved. The internal capacities of a hospital are likely to require external support if success is to be enjoyed. A possible general framework for approaching change in a hospital environment might be:


1. Clearly define the goals of the change initiative and the areas of the hospital affected.

2. Collect “I Opt” Survey data for all levels of the areas affected—from executives to Nurse Aides. There is no substitute for exact measurement of the current condition.

3. Plot and analyze the “I Opt” findings. Assess the difficulty likely to be encountered by areas affected. Develop an overall plan to initiate the change that will be common to all areas. Develop sub-plans that will address unique aspects discovered in the analysis.

4. Initiate context specific Leadership training for all managers affected. This is not generalized “Leadership” instruction. Rather each leader will be trained to guide the specific group for which they are responsible. This is performance based training rather than developmental. Tools like “I Opt” LeaderAnalysis™ can be used to support this process.

5. Apply the generalized program created in Item 3 above to the RNs and their staffs. Create and apply supplemental training appropriate to the education and role of each staff category
(e.g., RN, LPN, NA, US). Use the data collected in Item 2 above to design the supplemental material to exactly target the people involved.

6. Initiate a team based program to support the effort. This will help mitigate the coalition effects by focusing the people involved on their immediate interests and responsibilities. Tools like “I Opt” TeamAnalysis™ can be applied to initiate the effort.

7. Expect this process to take time. The Logical Processor style can and will change. But their information processing style demands a linear, step-by-step, exacting approach. The global Change Management programs currently popular in other industries are unlikely to yield any enduring gains.

8. If money is an issue, consider a viral strategy. Implement the above program in a relatively narrow area. People in other areas will see the success. At minimum, this will cause them to be more amenable to accepting the program. At best it will cause them to demand that they also get the benefits of the program. In this implementation the program is designed to act as a virus “infecting” other parts of the hospital in a positive manner.


This research has proven to be eye-opening. Specific measurement has replaced opinions. Fully grounded, validated tools have replaced “on the fly” surveys. A well-defined “what causes what and why” theory has displaced speculative guesses. Most importantly, “I Opt” technology actually works when it is used in field settings. It is an excellent mix of academic rigor and operational utility.

One mark of productive research is that it gives rise to more questions. This was the case here. As a result of this study we are now involved in another study. This study will trace the causes and implications of the high level of commonality found among the nurses. Even at this point we are able to see the outlines of the system that has created the current condition. But for now, the above is a step in the right direction.