Sales Management and Performance

By: Gary J. Salton, Ph.D.
Chief: Research & Development
Professional Communications, Inc.

SUMMARY
This research sampled 711 Sales and Marketing people from 193 different firms. The study found a statistically significant relationship between the “I Opt” style and hierarchical rank in a firm. The study also found significant differences between sales and marketing professionals as well as between types of sales (i.e., relationship vs. transactional). Practical guidance arising from these findings are outlined.

A brief video outlining the findings of this research is available on YouTube. Click the image on the right to view the video.


SAMPLE
The data for this study comes from the “I Opt” 70,000+ person database. People whose titles clearly indicated that they were involved in Sales and Marketing were used for this study. Table 1 shows the sample arranged by organizational rank.

Table 1
SALES AND MARKETING SAMPLE

The significant sample size and the wide variety of organizations strongly indicate that the data is representative of the Sales and Marketing profession.


BASIC FINDINGS
The “I Opt” database was used to determine the mix of strategic styles being used by each person at each level. These scores were then averaged to get an overall profile of the strategic styles actually being used.

Graphic1 shows a pattern that literally jumps off of the page. There is a virtual “stair step” relationship between rank within the firm and the mix of strategic styles being used. This kind of stair step indicates that something fundamental is in operation. “Something” is causing certain styles to be favored and others to be devalued with changes in rank.

Graphic 1
STRATEGIC STYLE COMMITMENT

Strategic styles (e.g., RS, RI, HA, LP) are names given to different ways of processing information. Different styles produce predictable behaviors that effect job performance. This is easy to see.

For example, ignoring detail means that precision will be lost. It does not matter how anyone feels or what they believe. The information that precision requires is just not there. Similarly, spontaneous decisions gain speed at the cost of more mistakes. Not considering all relevant circumstances make this condition a virtual certainty.

The same phenomenon happens with every other strategic style. Every style carries with it a corollary vulnerability. It is reasonable to assume that the “stair step” is the result of different jobs that make use of particular style strengths and are relatively insensitive to their corollary vulnerabilities.


STRATEGIC PROFILES BY LEVEL
A strategic profile is just a particular mix of styles. Graphic 2 groups strategic styles by level. It immediately shows that different profiles (i.e., mix of style strengths) are being used at each level.

Graphic 2
STRATEGIC STYLE BY
ORGANIZATIONAL LEVEL

The LP (methodical action) and HA (analysis) are favored at the professional sales level (group on left). This makes sense. Persistence is the lifeblood of sales. The disciplined LP is nothing if not dogged. The logical, reasoned analytical HA addresses the other sales key—answering objections. This style mix is well suited to the demands of the average sales position.

The job changes at the executive level. Executives must plot a course over an uncertain future. Their creative RI strategy produces ideas, options and alternatives. These can be slotted in and developed as the future unfolds. It is ideal for meeting unexpected and/or changing conditions.

Executives also have another job. They settle issues that cannot be resolved at lower levels. These are typically fraught with uncertainty and which have no clear answer. The executive’s secondary RS (decisive action) style is an ideal complement to the dominant RI style. The RS shares the outline knowledge used by the RI but adds a decisive, action oriented component.

Managers (mid-level executives) have a foot in both executive and professional camps. They guide the professional while contributing to the executive’s strategic perspective. Their relatively balanced profile (middle of Graphic 2) is ideal for bridging the gap between professional and executive levels. Effectively, they have a foot in both camps.

With this reasoning, “I Opt” offers a simple explanation of why different level favor different strategic styles. Different levels do different things. These different things require different kinds of information. A solid “reason” gives confidence that the findings are not transient. They are "built in." They are here today and will be present as long as the sales/marketing structure exists. It is worth investing to optimize performance with the expectation that these conditions will persist.

What does this mean on a practical level? For one thing it means that excellence at one level does not imply excellence at another. For example, one way to negatively effect sales is to take the professional sales person and make them a manager or executive without preparation.

The loss could be more than the sales they were generating. If they apply the styles that were successful in their sales capacity they can compromise their new function. This can further erode sales—both short and long term. The value of effective training and development that addresses the style issue is obvious.

Other things also “jump out” of this finding. For example, tension is built into the relationships. What seems obvious at one level can be viewed as irrelevant at another. Management systems ignore this reality at their own peril. A small investment in tension management can go a long way.


STATISTICAL SIGNIFICANCE
Are the style differences shown in Graphs 1 and 2 are just a matter of chance? If the study were redone using a different sample would we get the same results? This is a question statistical significance was created to answer.

Table 2
STATISTICAL SIGNIFICANCE BETWEEN
ORGANIZATIONAL LEVELS

Table 2 shows that all but one style difference between levels is statistically significant. The lone exception is the Reactive Stimulator (RS) scores between professional and managerial levels. The difference between professionals and mangers on the RS dimension may just be random noise. However, the RS style is not a major influence at either level and can be safely ignored for the purposes of this study.

But in 87.5% the tests (seven out of the eight) of significance equal or exceed the academic standard of p < .05 (i.e., 95 chances out of 100 that result is not due to chance). These significance tests tell us that these findings are no accident. Different organizational levels are using different profiles. The sales and tension effects noted in the previous section are real and pervasive.


SALES AND MARKETING DIFFERENCES

Sales and marketing are combined in this study because that this is the way the real world works. At the managerial and executive levels the functions are often merged. For example, the title Vice President, Sales and Marketing is among the most common titles at the VP level.

However, it is reasonable to ask whether the two functions differ in cases where the functions can be disentangled. Are sales people are fundamentally different than those in marketing? Again, statistical significance is the way to answer the question. Table 3 does just that.

Table 3
STATISTICAL SIGNIFICANCE OF DIFFERENCE
BETWEEN SALES AND MARKETING
(using Student’s-t test)

In 10 out of the 12 test dimensions there is no difference between sales and marketing people at the same organizational level. The two functions seem to be cut from the same cloth but with a bit of a difference in pattern.

The difference in pattern is seen in two strategic styles at only one organizational level—the professional rank. Graphic 3 highlights the difference.

Graphic 3
SALES AND MARKETING PROFESSIONAL
LEVEL STRATEGIC STYLE COMMITMENT

Graphic 3 shows that the marketing professional favors the analytical HA style. Marketing’s mission is assessment. The higher HA style makes sense. The sales mission is getting buyer commitment. Providing the buyer with options and alternatives directly supports that mission. The strategies being used by both functions make sense for their different jobs.

An implication of this finding is that movement from one function to the other would benefit from some training and education. Since these two functions tend to combine at the managerial level and up, this is not an inconsequential observation. Managerial development would benefit from recognizing and preparing upcoming sales/marketing executives for this condition.


STYLE FLEXIBILITY IN RANK

The tendency favoring RI (ideas) and RS (decisive action) as a person rises is not absolute. Firms exist in many niches. Each of these can contain unique drivers. These niches create opportunity for any strategic style preference to advance.

For example, executives in stable commodity based firms typically have higher levels of LP (methodical action). High tech executives put more stress on an RS (decisive action) strategy. Graphic 4 exemplifies this condition. It focuses only on the LP style but illustrates a general case.

Graphic 4
EXECUTIVE LEVEL LOGICAL PROCESSOR (LP)
STRATEGIC STYLE COMMITMENT
(n = 97)

Graphic 4 shows that 18% of the executives (i.e., VP and up) use high levels of the LP (disciplined action) style. These levels more typical of professional levels. Yet they exist at the most senior ranks. The same condition holds for all other strategic styles.

This means is that there are organizational niches for every style. However, the odds favor people using strategies identified in the stair step chart (i.e., Graphic 1). There are more of these opportunities than there are specialty niche openings.


STYLE FLEXIBILITY BY SALES MISSION
This study treated all sales as the same. This is an over-simplification. Selling fleets of aircraft is a lot different than selling of television sets in a big box store. Does this kind of difference influence strategic style being favored?

To test this proposition a subset of 62 professional sales people from 27 different firms were extracted from the data. The data was divided into two classes. One group sold products that involved continuing involvement. This was labeled “relationship selling.”

The other product of the other group required only episodic involvement. Once the sales effort was over, there was unlikely to be further dealings. This type of selling was labeled transactional sales. Table 4 shows examples of the type of sales by category.

Table 4
TYPICAL SALES TYPE BY SALES CATEGORY

Graphic 5 shows that only one strategic style difference rose to the level of statistical significance. Transactional selling (i.e., episodic, “one time”) finds more value in the idea-oriented RI style. Relationship selling appears to put more emphasis into welding the relationship with dependable action (LP) and insightful analysis (HA).

Graphic 5
STRATEGIC STYLE COMMITMENT
TRANSACTIONAL vs. RELATIONSHIP SALES
(n = 62, Firms Represented = 27)

Once again, this condition makes sense. Buyers who expect continuing interaction are likely to put more emphasis on dependability and insight. New ideas are welcomed but of less value since they can confound the structure of the interactions. The absence of such penalty for the transactional buyer means the net value of new ideas is higher for them.

Practical implications flow from this finding. For one thing it means that the type of product being sold will influence the choice of the ideal sales “style.” The overall finding of the favored sales LP/HA applies to general sales. If you knew nothing about what was being sold to whom, this would be your best choice. However, if you do know the facts of a sales situation there may be better ways of developing a sales staff.

One way of identifying an optimal sales profile for a particular product in a specific market might be to identify the high sales performers. Commonalities in their “I Opt” profile could signal an optimal strategic match. Training and management development programs could then be tailored to move the general sales force in that direction. It could be an idea worth some thought.

This finding also suggests a sales seminar strategy. Sales seminars tend to produce “tidbits” of valuable insight. But they seldom “hit the mark” across the board. The reason is that many seminars use a “one-size-fits-all” approach. This research shows that seminars could be tailored to the specific product and market niches. The strategy is likely produce consistently better learning and sales results. Given the importance of seminars in both learning and motivation, this is an option that may be worth investigation and experimentation.

Fitting the Leader into the Matrix

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


ABSTRACT
University and corporate educators prepare people to lead. They typically focus on the leader and the group being lead. Little thought is given to how the new executive fits into the existing management structure. Yet leaders must mesh their efforts with other leaders to be effective. This research blog looks at the leader-to-leader “fit.”

“I Opt” is used to measure the fit. Psychology may apply in a specific situation. Information processing always applies. “I Opt” is part of an information processing based technology. The way information is processed determines how people “understand” issues. A common understanding eases integration while differences can impede it. “I Opt” strategic styles are not the only thing determining “fit.” But they are an important thing.

This research blog uses the “I Opt” profiles of 529 undergrad, MBA and EMBA (i.e., Executive MBA) business students and 3,907 executives. The fit between students and executive levels are measured from both a corporate and university perspective. The implications for leadership education are then specified.


STUDENTS
Chart 1 shows that different levels of business education have different “I Opt” profiles (i.e., information processing patterns).

CHART 1
“I Opt” STRATEGIC STYLES BY STUDENT LEVEL
(111 Business Undergraduates, 293 MBAs, 125 EMBAs)

“I Opt” profiles reflect the ways students acquire and assess new knowledge. For example, an undergraduate will tend to “understand” new knowledge in structural terms—the “how” and “why.” The EMBA will see the knowledge more in terms of opportunity—the “what” and “when.” Effectively, students at different levels will “understand” the same knowledge in different ways.

This finding has teaching implications. The best way to teach an undergrad is not the best way to teach an EMBA. Teaching the same course the same way between levels is a formula for suboptimized learning. Instructional design should know about and adjust for these differences.

Instructors themselves are also affected. Instructors who are effective at one level may not be at another. Delivery as well as content must be adjusted if learning is to be optimized. Knowing when and how to adjust is a necessary component for optimized learning.

Many educators know of these differences from experience. This finding tells them what exists, its direction and its magnitude. Knowing these things opens the door to improved learning. This can be far more effective than waiting for each educator to “discover” the differences for themselves.


EXECUTIVES
Executives also come in gradations. Three categories can be identified. The 1st Level includes titles like supervisor and leader. Mid-level encompasses managers, directors and similar titles. Senior executives include VP, General Manager and the various “C” level titles. The “I Opt” profiles of these categories are shown in Chart 2.

CHART 2
EXECUTIVE LEVEL DISTRIBUTION OF “I Opt” STRATEGIC STYLES
(473 1st Level, 2,850 Mid-Level, 574 Senior Executives)

A “stair step” is again visible. This suggests that the demands of the position—not the field within which it is applied—is the driving factor. In other words, the scope and nature of the different management levels is causing particular “I Opt” profiles to be favored.

The executive “stair step” reflects that of the students. Lower level positions favor structure (the how and why). This posture is oriented toward the present. Higher level positions are more opportunity oriented (the what and when). This is a future oriented stance.

It makes some sense that leaders are more future oriented. Their job is to chart a path. The job of those being lead is to keep the machine running. If this is not done a path to the future is useless. Both qualities are needed in a successful organization. They just reside in different proportions at different levels. Corporate educators would do well to recognize these distinctions.


MATCHING EDUCATION TO MANAGERIAL LEVELS
Chart 3 matches education level with a level in management. Almost all of the undergraduates in the sample are full-time students earning entry-level credentials—the 1st level of management.

The MBAs are a mixture of full and part-time students. Many hold or have held professional positions. Their likely match are the mid-management levels.

The EMBA typically is being groomed for a senior position. Most already hold significant roles in their firms. One of a firm’s motives for sponsoring their education is to ensure a “fit” with their soon-to-be peers. Those peers are likely to be senior executives.

CHART 3
MANAGEMENT vs. EDUCATIONAL LEVEL COMPARISON

There appears to be a rough consistency in match proposed above. Looking a bit deeper into the data can help identify the opportunities imbedded in each level.


EMBA vs. VP Levels
The EMBAs are usually marked by their firms as candidates for senior positions. Chart 4 suggests that part of this judgment may have been based on their management “fit.” Overall, the EMBA’s profile strongly resembles that of senior management.

CHART 4
SENIOR MANAGEMENT vs EMBA STUDENTS

(125 EMBAs, 574 Senior Executives)

While the profiles are highly similar, they are not identical. Educators can help close this gap by accenting the value of idea generation (i.e., options and alternatives) in their teaching. This leverages the EMBA’s substantial existing skills. Sensitizing them to the value of this skill will probably to cause them to hone this skills to higher levels. The gap will close.

Reducing the tendency to approach issues with analysis, assessment and evaluation (HA) is also warranted. Knowing how to judge the analysis of others is more important than knowing how to do the analysis. Educators might want to stress “thumbnail” evaluation techniques, critical factor methods and consistency tests. The integrity of the decision input can be reasonably assured without the high cost of detailed analysis.

Educators do not have to give much attention to the disciplined (LP) or spontaneous (RS) action tendencies of the EMBA. These are already well matched to the senior executive level. In other words, these capacities are already present at about at the right level.

Overall, the EMBA is already well positioned to work on a common plane with other senior executives. But the refinements above will help the EMBA navigate the final steps. The reputation of the teaching institution will also benefit. Its students will seem to “fit” better than those of other schools. The small effort involved will pay everyone dividends.


MBA vs. Mid-Management
Chart 5 shows a looser fit between the MBA profile and that of the executive rank to which they aspire.

CHART 5
MID-MANAGEMENT vs MBA STUDENTS
(293 MBAs, 2,860 Mid-Level Executives)

Classroom experience will not change “I Opt” profiles. But, the way they are deployed can be changed. The prescription is the same as given for the EMBA—just in greater strength. Less analysis (HA) and more emphasis on generating options and ideas (RI) will serve the MBA well.

The wider gap means that sensitizing the average MBA will be more of a challenge. It is likely that instruction will have to be repeated from different angles to have a lasting effect. However, the size of the gap also suggests that the return on this effort will be much higher than with the EMBA.

In addition, the MBA will tend to put about 8% (not shown on the chart) too much value on the deliberate, exacting action (LP). They will also undervalue decisive, spontaneous action (RS) by about 13%. Since the gap in these action dimensions is smaller, the teaching challenge will not be as great. Exercises showing that the cost of exacting action can exceed the value it returns may be enough to make the point.

Overall, the MBA is too focused on structured (i.e., patterned) methods. Guiding them to appreciate the value of spontaneity in both thought (RI) and action (RS) will pay dividends. They will be more “in tune” with fast paced demands of their mid-management target.


Undergraduate vs. 1st Level Management
Undergraduates follow the MBAs in their “fit” with the level to which they likely aspire. Chart 6 shows the pattern is the same but the difference is less severe.

CHART 6
1st LEVEL MANAGEMENT vs UNDERGRADUATES
(111 Undergraduates, 473 1st Level Executives)

Some undergraduates may be early in their careers and preparing for professional non-management positions. To test whether this would cause a change in diagnosis an added test was run. Undergraduate profiles were matched to those of 527 non-managerial professionals. The results are shown in Chart 7.

CHART 7
NON- MANAGEMENT
PROFESSIONALS vs UNDERGRADUATES

(111 Undergraduates, 527 Non-Management Professionals)

The fit between undergraduates and professionals is closer but the pattern is the same. The students will tend to over stress structural approaches (LP and HA—the how and why) and devalue spontaneous methods (RI and RS—the what and when).

Overall, the educator’s return for adjusting their strategy will be less than realized for the MBA. But there will still be a positive return. Students sensitized to the value of unpatterned methods will tend to integrate better with their peers as well as those at a higher level (i.e., mid-management). It’s an effort worth making.


CORPORATE TRAINING IMPLICATIONS
Corporate educators do not work with students. They focus on preparing employees to assume higher levels of responsibility. The educational challenge is the same. Only the subjects change.

Chart 8 shows “I Opt” styles for various levels of management. “I Opt” profiles for 527 non-management professionals have been added since they are in the corporate educator’s mix.

CHART 8
"I Opt" STYLES BY MANAGEMENT LEVEL

The “stair step” pattern is again visible. What this means is that increasing responsibility will involve both growth and decline. And both happen at the same time. The spontaneous, unpatterned strategies of RS and RI grow. The disciplined, structured strategies of HA and LP decline. Education is a matter of change, not just “growth.”

Overall, the prescription for corporate and university educators is the same. The higher the level being targeted, the greater the emphasis on unpatterned strategies—in both thought and action (RI and RS).

But don’t overdo it. The objective is to target the level being addressed not some ultimate level. Each level has its own “optimal” profile. Both university and corporate educators have to be smart enough to recognize this. There is no single, universal leadership profile. There are many.

But there is a universal. That universal is the need for continuous change as responsibilities change. There is no right or wrong. Different levels just confront different issues that demand different ways of seeing the world.

If leaders are to be fully successful they must be prepared to make these transitions at the appropriate time. Too early and they may compromise their ability to attain the role to which they aspire. Too late and they may have trouble keeping the job or progressing further.


A BIG CAVEAT
This research blog used averages. Averages apply to groups, not to individuals. To illustrate this Chart 9 shows the “I Opt” strength distribution of the 473 Senior Executives that were averaged for this study.

CHART 9
SENIOR MANAGEMENT
STYLE STRENGTH DISTRIBUTION
(473 Senior Executives )

The senior executive ranks include people using a variety of strategic profiles. What this means is that there is no “silver bullet” profile. However, there are odds. Those odds will favor people who subscribe to “I Opt” strategies that are proven at the managerial level in question. The “most proven” of these tend to center around the average for the level being targeted.

Used with understanding and caution, the results of this research blog can be helpful to an individual. The results can be offered as a probability assessment rather than as a prescriptive formula. Used this way it may instill a level of flexibility and tolerance for change. This will serve them well throughout their career.

Adding Motivation to the Kolb Learning Model

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

INTRODUCTION
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 www.iopt.com for an outline of the validated technology and www.oeinstitute.org 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.


MEASURING MOTIVATION
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
MEASURING RATIONAL MOTIVATION

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
MEASURING EMOTIONAL MOTIVATION

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.

MEASURING LEARNING

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
MEASURING LEARNING

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.


CALCULATING MOTIVATION
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.


THE RESULTS
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
RELATIVE STRENGTH OF
EMOTIONAL AND RATIONAL MOTIVATION

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
RELATIVE STRENGTH USING KENDALL’S TAU

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.


MANAGING MOTIVATION

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.

Emotional

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
STRATEGIES FOR EMOTIONAL MOTIVATION

• 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
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
PROCESS FOR INSURING BENEFIT VISIBILITY

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.”
   
   

FITTING THE KOLB MODEL
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
INDIVIDUAL LEARNING PROFILES

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
3D LEARNING PROFILES

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
JOINT LEARNING PROFILES

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.


MOTIVATING GROUPS WITH THE KOLB MODEL
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
APPLYING THE 3D MODEL TO GROUPS

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.


CONCLUSION
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.

Optimizing the Kolb Learning Model

by:
Gary J. Salton, Ph.D.

FORWARD

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 www.oeinstitute.org for "I Opt" theoretical underpinnings or www.iopt.com 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.

ALIGNING THE KOLB MODEL

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 KOLB LEARNING MODEL AND "I OPT" GRID

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.

MEASURING THE STRENGTH OF KOLB’S STAGES

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.

GRAPHIC 2
KOLB’S “CONCRETE EXPERIENCE” AND “NEW SITUATIONS” VARIABLES

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.

GRAPHIC 3
KOLB'S "OBSERVE & REFLECT" AND "CONCEPTS" VARIABLES

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.

GRAPHIC 4 CUMULATIVE DISTRIBUTION OF KOLB VARIABLES

"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
MINIMUM LEVELS OF KOLB STAGES TO CAPTURE 75% OF STUDENTS

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.

HOW TO MEASURE KOLB LEARNING STYLES IN GROUPS

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.

GRAPHIC 6
KOLB LEARNING MODEL STYLES AND "I OPT" GRID PATTERNS

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.

GRAPHIC 7
MEASURING LEARNING STYLES OF GROUPS

“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.

GRAPHIC 8
LEARNING PROFILE OF AN ACTUAL CLASS

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.

THE “TYPICAL” CLASS

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.

GRAPHIC 9
KOLB LEARNING STYLE SAMPLE OF ACTUAL CLASSES
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 DESIGN OPTIMIZATION
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.

GRAPHIC 10
KOLB LEARNING STYLE DISTRIBUTIONS
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.

GRAPHIC 11
“TOUCH ALL BASES EQUALLY” VERSUS MIDPOINT STRATEGY
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.

SUMMARY

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.



BIBLIOGRAPHY
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.