Friday, May 18, 2018

Organization: A Fact-Based Profile

By: Gary J. Salton, PhD
Chief: Research and Development
Professional Communications, Inc.

This study considers the entire “I Opt” database of 76,442 individuals domiciled in 115 countries and who occupy positions in 5,549 unique organizations. The study is able to define the organization as an ecosystem with specific, quantifiable relationships between its elements. The resultant model can be used as a framework for both micro and macro organizational analyses.

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As with any large evidence-based study, the data and logic can be somewhat tedious. Below are the conclusions for those who wish to avoid this tedium:

  • The conclusions describe an “ecosystem” with both autonomic and rational components interacting to sustain the system at the cost of some tension between the internal components
  • A majority (58%) of the sampled population strongly favors stability in addressing new situations
  • The balance of the population (42%) offers a capacity for adjustment to new situations.
  • Contaminated input disproportionately threatens the majority (i.e., misdirection or paralysis)
  • Over the longer term, the focus on stability drops from 58% to 43%
  • Over the long-term the change-oriented tendency roughly balances long-term stability tendencies (43% stability versus 42% change)
  • The long-term change-orientation (42%) is equally divided between analysis and experimental strategies. This division weakens the thrust toward change but increases flexibility
  • The “average” person relies on their primary or secondary style 70% of the time. This consistency is one of the foundations for the high predictive accuracy of “I Opt” technology.
  • The management hierarchy is the “conscious” change agent (versus the “automatic” style elections)
  • The style orientation of management changes by organizational level at a moderate 5 to 15% 
  • Top management has the strongest focus on new ideas and options
  • 1st Level management favors certainty, stability and reliability
  • Senior management (i.e., VP, SVP, EVP) is the primary change agent in both ideas and action
  • Mid-management acts as a bridge that is slightly bias towards upper management positions 
The research finds the parallel to biological systems to be obvious. Like biological systems the organizational system can accommodate disruptive changes up to some undefined point. That undefined point likely occurs where the certainty and stability required by the base orientation is seriously threatened. After that point the majority population will tend to look to an increasingly decisive management to re-impose predictable order. The direction of that order will likely be secondary to the establishment of order itself.

There is no such thing as a truly random sample for any large scale research in the social sciences. Society has too many dimensions. There is virtually a zero chance that all can be simultaneously balanced. A viable strategy is to identify the data sources used and leave it to the reader to assess whether it is adequate for their purposes.

Table 1 

  The total 76,442 people are used in the analysis as the sampled population to which various subsets are compared. This is deemed legitimate since all of the categories in the sample participate in the general organizational framework. As such they both influence and are influenced by the whole population.

Table 2 

Table 2 shows the number of unique organizations in the sample (see footnote #1 for more detailed listing).  Subsidiaries were collapsed into the parent organization. Thus an organization with multiple subsidiaries and thousands of employees is counted as one unique firm.

Table 3 

Table 3 describes where people were domiciled at the time they took the “I Opt” Survey. It does not necessarily represent their nationality. However, it is reasonable to expect that many if not most of the people are nationals of the countries cited.

The sample is not purely random. However, the large sample size and its wide distribution at least mitigates some of the concerns. The range of titles cited in Table 1 limits the effects of rank based bias. The wide scope of organizations cited in Table 2 reduces the possibility of industry based bias. The fact that the data captures about 60% of the countries on earth dampens the possible effects of a national culture bias. It is reasonable to accept the sample as being indicative if not definitive. However, final judgement is left to the discretion of the reader.

 “I Opt” technology is the lens that will be used to make the macro-level assessment of our sampled population. It is based on the classical information processing model shown in Graphic 1. Each component of that model has a role to play in nature of interactions and the character of the decisions likely to be made.

Graphic 1 


Input is a key component determining the behavior that can and will be emitted. One measurable aspect of input is the degree of acceptable ambiguity in input data. Some people require fact-based detail when confronting new issues. This provides them with the exact, precise evidence they require to execute their preferred decision strategy.

Other people tend to use a more probabilistic stance. They are prepared to infer needed information. This inference can be applied to the base data itself as well as “filling in” missing data elements. For example, it might be inferred that a commodity price is being manipulated even without specific evidence. Or the effect of an apparently unrelated variable might be assumed to be affecting the commodity price. An inferential strategy is less precise but considers a broader range of options since explicit connections are not required.

Everyone can and does use both inferential and detailed factual data in conducting their life. Common issues that we confront everyday can effectively be pre-decided whether they be inferential or fact based. New unfamiliar issues are a different story. If the issue does not carry a “label” defining a resolution method people must choose a way of addressing it. They will tend to use a way that has worked for them in the past. “I Opt” registers that experience as a level of strength with which they are committed to a particular stance—inferential or explicit.

Graphic 2

(Sample Size = 76,442)

Graphic 2 shows that 61.9% of sampled population favors explicit detail and “facts.” Facts can take time to gather. They typically do not come with certifications of their veracity or relevance. They take time to assess and evaluate. In addition, “facts” can conflict. Reconciliation of factual discrepancies can add to the time demands. The dominance of fact-based methods means that we can reasonably expect the sampled population to be a bit slow to react to unfamiliar issues.

Graphic 3

(Sample Size = 76,442)

Graphic 3 shows another basic component of our model—output. A person can be oriented toward either thought or action as the targeted outcome of the decision process. Thought in our terms is a preparatory activity. It involves things like plans, assessments, projections and the like. Action is the other option. It involves directly affecting the issue in question. Action causes the “real world” condition of that issue to be changed. Action output can change thought activities, thought output cannot change actions already taken. Thought is conditional. Action is final.

The dominance of thought-based output reinforces the measured pace tendencies noted in Graphic 2. Preparatory activity takes time. It also presumes that time will be available to deploy the knowledge that was created by thought—more time. The combination of detailed input and thought output makes it reasonable to expect that our sampled population will not be fast “out of the blocks.” The combination is not a formula for a responsive posture on new unfamiliar issues.

‘I Opt” connects input and output elections with the “Process” component as shown in Graphic 4. Process is an activity rather than an event. It tells input what to look for in order to satisfy the output objectives. It informs output of the range of possibilities given the input that is available. It is an iterative process. It proceeds toward issue resolution in a step-by-step fashion (see footnote #2 for references to more detailed explanations)

Graphic 4
(Sample Size = 76,442)

The process activity is not “free.” It takes mental effort. People typically do not reinvent “process” for every new issue that arises. Rather they tend to settle on a general process that seems to work within their particular environment. This reinforces the stability of the system. That stability helps make people predictable. This fact has been empirically validated in research studies on both the validity and reliability of “I Opt” (see footnote #3 for detailed references)

The population does have a change component. The 38.1% favoring inferential input and the 41.9% action output components are not inconsequential. However these are secondary inclinations that will most likely be engaged in the face of a recognized threat not well addressed by the dominant strategies.

In general our sampled population is inclined to seek certainty in both thought and action. A reliance on trusted methods provides a level of this certainty in action outcomes. Rigorous logic, reliable methods and thorough analysis provides the certainty assurance on the preparatory thought side. This posture is a trade off. Reliable, efficient performance is being purchased at the cost of a certain level of sluggish behavioral caution when confronting new issues. Since we all want the lights to go on when we flip a switch and water to flow when we turn the facet this may be a near optimal social posture.

The above analysis is useful for overview purposes. However, predictive accuracy needs more precise delineation of the variables involved. “I Opt” technology is able to do this by combining the macro components into discrete strategic style combinations as shown in Graphic 5.
Graphic 5 

Table 4 shows the labels “I Opt” has assigned to each strategic style. “I Opt” technology actually measures these more precise variables rather than the generalized concepts (i.e., inference and “facts") used in the macro review. The formal “I Opt” concepts are shown in red italics and are included in deference to those readers already familiar with “I Opt” technology.

Table 4 

The style measurements of the sampled population are shown in Graphic 6. The summary level data of the previous section has been refined to gain increased behavioral specificity.

Graphic 6 
(Sample Size = 76,442)
Thought (i.e., preparatory activities) remains the dominant approach to a new issue. But it has been divided. The Hypothetical Analyzer (“HA”-34.9%) uses detailed thought-based analysis to assess and plan. The Relational Innovator (“RI”-23.2%) uses inferential input to generate thought-based options, ideas and alternatives. The result is an increase in the range of options considered. Both styles favor thought output but approach it differently.

Similarly the earlier action based output of 42% has been divided between the Logical Processor (“LP”- 27%) who favors detailed “facts” to guide precise action and the Reactive Stimulator (“RS” -14.9%) who tends to use inference as an action guide and is prepared to act on that inference. Again the range of options “automatically” considered by our sampled population is increased.

The dominant HA and LP styles share a common detail oriented “fact based” approach. Together they account for the 61.9% inclination toward fact-based input. But they produce different behavioral outcomes. The HA produces a plan or assessment. The LP produces conclusive action. Both HA and LP strategies see a measured, cautious approach as the best way to confront our environment—whether in thought or action.

At the time of this writing the world is attempting to digest a new technology which directly affects the information flow entering the decision process. The internet and wide availability of smart phones has created the ability to publish “news” of all types without edit or attribution.

News is by definition “new” and unfamiliar. Strategic style strategies are the vehicle used to initially assess “new” information. Misinformation will affect everyone. But it has a disproportionate effect on the analytical HA and process oriented LP styles. Both styles depend on accurate detailed input to engage their preferred resolution strategies. 

The effect of misinformation depends on its design. Faulty conclusions are a general exposure to all styles. However the HA and LP styles have an incremental exposure. They will tend to see matters as “settled” once a conclusion is reached. Their initial reliance on “solid” evidence provides them with a level of confidence that is not easily shaken even in light of subsequent contrary information. Faulty decisions will tend to persist. There is also the possibility of frustration paralyzing the HA and LP’s strategic processes. Confused data generated by misinformation can render an issue unsolvable and cause it to be set aside or ignored.

A majority of the sampled population subscribes to the HA/LP styles (61.9%).  This means that the population as a whole has something of a bias toward increased risk exposure from misinformation. This risk is embedded in the structure of the sampled population. Methods to offset the increased risk are beyond the charter of this paper but merit the attention of those in a position of influence.

Strategic styles describe the likely approach to a new issue that does not give a clear signal as to the “right” resolution method. However, most issues of consequence involve a series of interrelated decisions. Each step in this process can require a radically different style-based response. To meet this challenge people develop a style “fall back” strategy. The “fall back” is merely the style with the next highest strength. The combination of primary and secondary (i.e., “fallback) style strength commitments are designated by “I Opt” as strategic patterns.  Table 5 outlines the strategic style combinations for each of the four “I Opt” patterns.

 Table 5 
Graphic 7 shows the distribution of strategic patterns in the sampled population. The Conservator Pattern (HA/LP) is dominant. Over twice as many people chose this rigorous, detail oriented pattern over the next most used patterns. This means that interactions in organized settings will most likely be the examination of the “facts” through the lens of well-understood analytical methods (HA) and will favor execution elections using a disciplined, exacting approach (LP).
Graphic 7 
(Sample Size = 76,442)

It might be noted that the tendency toward the 61.9% cautious measured approach in Graphic 6 (the combination of HA and LP considered independently) has declined to 43.4% in Graphic 7. The reason is that people do not always select the HA or LP as their secondary approach. They can choose the RI and RS both of which accept inferential input. This longer-term stance diminishes rigidity seen in the initial response style profile.

This distribution described in Graphic 7 makes societal sense.  A majority of the functions, conventions and practices of a society are established and well-tested. Keeping them running is the prime requirement. Employing standard procedural practices (LP) or well-tested analytical approaches (HA) are probably the best way to ensure that the lights stay on, the supermarket has groceries available and the toilet has water to flush.

But environments do occasionally change. These changes can require new approaches. The “I Opt” distribution accommodates this ability to change with the Perfector and Changer Patterns. Both are focused on new options (both have an RI component). The Perfector uses analysis to assess the RI options, the Changer validates them with experimentation. The combined strength of these two change-oriented patterns about equals that of the stability-oriented Conservator pattern (42.6% versus 43.4%).

However, this apparent equality probably overstates the change orientation. The 42.6% change orientation is the strength of two combined strategies (Perfector 21.1% + Changer 21.5%) which can compete. This competition is likely to diminish their overall thrust toward change. On balance the long-term orientation of the sampled population continues to favor cautious stability.

 “I Opt” technology maintains that no style election offers any particular advantage or disadvantage on an absolute basis. Graphic 8 confirms the validity of that assertion.

Graphic 8 

Graphic 8 measures the “average” style strength distribution for all of the people subscribing to each of the four “I Opt” patterns as their primary approach. The proportional representation of the styles is roughly the same across all patterns. It is consistent whether a particular “style” component holds a primary or peripheral position within a pattern. If a particular style were inherently better or worse than another we would expect to find that condition reflected in its proportional representation. There is no visible distortion. It is reasonable to conclude that all styles of equal organizational value when applied in the domains to which they are applicable.

Graphic 8 is also telling us that the “average person” is able to navigate the “real world” about 70% of the time using their primary and secondary styles. When they do have to fall back to a peripheral style, they are equally likely to choose either one of the two remaining styles at about a 15% rate. The consistency of these ratios across the four patterns also argues for the validity of measurement. The intervals measured will be the same regardless of the pattern to which they are applied.

It is worth reiterating that Graphic 8 is an average. It is not a standard of comparison. For example, Graphic 9 shows the distribution of styles of the author. Yours will likely be different. Different environments generate different style elections. “I Opt” reliability studies have statistically demonstrated that our style and pattern elections are stable for periods as long as 18 years (see footnote 3c and 3d for reference to those studies). This means that the consistent averages shown in Graphic 8 are not a statistical fluke. They are founded on the consistency of our individual behaviors.
Graphic 9 
This style consistency also accounts for a portion of the “I Opt” technology’s high predictive power. The average reliance on two principal strategies means that the character of decisions is likely to be consistent over time. An “I Opt” assessment made using person’s current style and pattern preference is likely to persist into the future. This stable correlation is one foundation for “I Opt” predictive accuracy.

The foregoing analysis is akin to the autonomic nervous system in biology. It describes the probable behavior of the population in the absence of any overarching authority. Modern organizations typically superimpose some form of merit based bureaucracy on these basic capacities. The authority resident in the hierarchy allows it to “call out” specific attributes of the population as a response to the particular issues being confronted.

The large size of the current sample gives us the opportunity to examine the information processing preferences of each level of the hierarchy. The large sample size limits then need for esoteric statistics. We can simply graphically overlay the ‘I Opt” style curves for the population segments. The areas under each curve can be measured and differences calculated. The result is a quantified, visually obvious degree of similarity and difference.

First Level Management 
First level management consists of both Supervisor and Assistant Managers (see footnote #4 for a test of title equivalency). They typically manage functions with narrow areas of responsibility. The position is ordinarily seen as the first rung of the management ladder. Graphic 10 compares current occupants of this position with the rest of the sampled population (i.e., all other ranks both above and below 1st Level).

Graphic 10
(1st Level Sample n= 2,765 - All non-1st Level n= 73,677)

The blue area on the chart is the area where the curves for 1st Level and that of all of the others in the sample population overlap. In other words, people whose profiles lie in the blue area are using the same information processing approach regardless of their hierarchical position.

The red area represents the portion of 1st Level managers who are more inclined in a particular direction than are others in the population. For example, the red RS area in Graphic 10 (upper left quadrant) shows the amount that 1st level managers are more inclined to use less RS strategy than is the rest of the population. The red area in the LP graph (upper right quadrant) shows the portion of 1st Level managers more inclined to use this LP approach.

The green area shows the amount by which the general sample population (non-1st Level) exceeds 1st Level managers. The simple mechanics of overlaying two charts dictate that the green area always lies opposite that of the red. However, how that distribution is spread across the range of strength commitments can tell you something. In our sample RS reference (upper left quadrant) the green area is spread across a wider range of strengths. Clumps of strength are likely to be more noticeable than are this kind of diffuse distribution. The low RS will likely be noticed and may come to characterize the 1st Level group. The fact that others in the population are more RS inclined is unlikely to be remarked upon—it just “is.”

1st Level management generally has about a 90% overlap with the general “I Opt” population. Almost everybody’s profile can fit somewhere within 1st Level management without the need for any change in approach. “I Opt” profiles do not appear to offer any significant impediment or advantage to advancement at this level.

1st Level management “pulls” the organization toward restraint. They are 8.0% to 9.3% less inclined to use the change-oriented RS (red area upper left) and idea-oriented RI styles (lower right). They also “pull” the population toward a greater reliance on traditional methods (LP-upper right) and more analysis and planning (HA-lower left). But the strength of this incremental pull toward stability is a modest 3% to 5%.

Overall, 1st Level management appears to be discouraging spontaneity rather than advocating for greater detail and depth. This makes some sense. 1st Level management’s role is to keep things running. Spontaneous ideas (RI) and actions (RS) can cause glitches. Glitches can disrupt “running” operations.

Mid-management consists of people with responsibility for a functional area. They typically have multiple supervisory level subordinates and usually carry the title of Manager or Director. 

Graphic 11 shows that the 90% overlap with the rest of the population is about the same as 1st Level management. In both cases, if any style penalty to advancement exists it can probably be easily offset by education, experience or other such job relevant factors.

Graphic 11
(Mid-Management Sample n= 21,750 - All non-Mid-Management n= 54,692)
There is a directional shift from 1st Level. Mid-Management is “pulling” the organization toward more new ideas (lower right—positive 5.8% versus minus 8% for 1st Level) and responsive RS (upper left—positive10.4% versus minus 9.3%). At the same time they are lessening the influence of the analytical HA (lower left—minus 5.5% versus positive 3.5%) and the established process LP (upper right—minus 5.3% versus positive 5.3%).

The difference between 1st Level and mid-management is substantial in statistical terms. However, it is of marginal practical significance in absolute terms. A majority of the sampled population (the common area) shares mid-management’s profile preference. Any stress arising from the difference is likely to be confined to 1st level managers reporting to the 5% to 10% of mid-managers who differ from the general population.

In addition, mid-Management continues to have a high respect for traditional methods (e.g., mid-management “pulls” toward the center of the LP distribution—Graphic 11, upper right). The RS inclination (Graphic 11, upper left) is material. But it is offsetting about an equally strong 1st Level inclination  in the opposite direction (see Graphic 10, upper left). The overall direction of mid-management can probably be best described as cautiously inclined toward a more adjustment-oriented overall posture.

Senior Management 
Senior management consists of people who have policy making authority for major components of the organization. They typically carry the general title of Vice President, Senior Vice President and Executive VP. They also include “C Level” chief tiles such Chief Financial officer, Chief Operating Officer, Chief Information Officer and the like.

Graphic 12 
(Sr. Management Sample n= 2,840 - All non-Sr. Management n= 73,602)
Graphic 12 shows a marked change. The senior management common area falls to 80%. This is likely due to two factors. First, gaining a senior management role is a highly competitive process and small differences may carry heavier weight. Secondly there is less content variety than there is at lower levels. This narrower skill set may place higher value on specific strategic style profiles. It is worth noting that the 80% absolute level of the common area remains large in absolute terms.

But the directional “pull” is substantial. Senior management doubles the rate of de-emphasis of the precise action LP and analytical HA styles (about 11% lower for Senior versus about 5% lower for mid-management in both cases). Senior management’s RI style inclination stands out at 14.4%; almost triple mid-management’s 5.8% pull. The one consistency between the mid and senior levels is that the fast responding RS style remains roughly the same at about 11%. This suggests that a threshold limit to spontaneous RS actions has been reached. This is consistent with what Ashley Fields discovered in his 2001 doctoral dissertation using a much smaller “I Opt” sample (See footnote #5 for reference).

The picture painted by this analysis is of Senior Management as the major organizational change agent. It is not impulsive change. Rather it is a measured but aggressive push toward adjusting to meet current conditions. 

Top Management 
Top Management consists of Presidents, CEO's, Board Members, and similar titles that have the ability to influence the direction of the organization as a whole. 

Graphic 13
(Top Management n=1,048: Non-Top Management n=75,394)
Graphic 13 shows that the Common Area remained in the same order of magnitude as for senior management at roughly 80%. The exception is in the innovative RI category where the common area fell to roughly 70% (Graphic 12, lower right). The smaller common area is caused by a 22% increase in RI strength over senior management (14.4% versus 17.6%). This makes some sense. Issues that could be resolved at senior management levels have been resolved. What is left is for top management is likely to be the even more uncertain and less well-defined issues. The RI style offers ideas with nonthreatening thought as its output. However, it is also worth noting that while the common area in RI distribution is diminished, it remains large at about 70%. There appear to be many roles in top management that do not particularly favor a strong RI stance.

The drop in the RS difference from 11.1% to 7.2% (a 35% drop) between senior and top management is also notable. The broader nature of the issues confronted by Top Management makes quick, simple actions less probable. The organization wide scale of potential consequences associated with the risky RS strategy is also likely to play a role. The consequences of error can be organizationally threatening.

The overall picture painted by the Top Management numbers is that of a long-term advisory role. However, this may be due to the inclusion of board members who do not have executive responsibilities. The inclusion of top managers of non-profits may also influence this result. Their dependency on external agencies for funding may limit their behavioral options. It is likely that if these types of influence were factored out, Top Management could more closely resemble the posture of senior management.

The general organizational structure of the sampled population has capacities for stability and change built-in at all levels. The portion of the sampled population operating without supervision favors stability over change at about a 60%-40% ratio. This 60% majority acts to limit the possibility of being “whip-sawed” over potentially transient issues. But the 40% minority remains as the option for near-term adjustment “just in case.”

Over a longer-term series of transactions (i.e., “I Opt” patterns) our sampled population shifts to about equal stability versus change inclinations (43.4% for stability, 42.6% for change). The longer time span provides “room” for deeper consideration as well as giving the opportunity for more “evidence” to accumulate.

In both the short (i.e., “I Opt” style) and long-term (i.e. “I Opt” pattern) situations the general sampled population retain about a 15% capacity for fast response using minimal analysis. This is the RS “firefighting” capacity. Firefighting can bring spontaneous change as collateral condition but it is change without strategic direction. Sometimes it is positive, sometimes negative; sometimes durable, sometimes transient.

The consistency of the system is sustained by a stable behavioral predisposition among the participants. About 70% of the time they can (as a group) be expected to respond to a situation using their primary or secondary styles. This makes them predictable. Predictability is a necessary condition for coordination. Coordination is the essence of organized activity.

The autonomic system described above is a system with the capacity to accommodate a fluctuating environment. But it will be optimal only when those fluctuating conditions conform to the 60%-40% ratio embedded in the population. Unfortunately, real world fluctuations can vary from this optimal ratio. An additional overarching mechanism is needed to call out specific population capacities that match unpredictable environmental changes. That mechanism is the organizational hierarchy.

The organizational hierarchy is itself a system. The 1st Level base of the hierarchy acts something as like a shock absorber. It reinforces the basic stability tendencies of the sampled population. It interprets and dampens initiatives generated at higher levels. It “buys time” for management initiatives to be absorbed and incorporated at operational levels.

Mid-management appears to function as a managerial bridge. Its major style effect is a 10.4% accent toward the responsive RS strategy. Effectively this posture serves to incentivize lower levels toward action. The remaining three mid-management styles (HA, LP, RI) are in directional support of the senior management position but at a modest 5% strength level.

Senior Management is the major change agent. The combination of a 14.4% greater inclination toward new ideas (RI) combined with a strong 11.1% greater willingness to take responsive action (RS) is a prescription for initiated change. Their reduced 11% stress on the cautious HA and LP strategies lessen the likelihood of restraint.

Finally, Top Management’s heavy 17.6% inclination toward new ideas and options (RI) casts them in something of an advisory role. The positive 7.2% pull toward responsive action (RS) suggests that the advice given will be accompanied by an action imperative.

The above structure is sustained by a pool of commonality. Between 70% and 90% of the participants in the population share a common “I Opt” profile with the various levels of management. This means that most of the time the position of management and the general population will be in accord with the approach being taken—even if they differ on the specific actions taken using that approach.

The large Common Area also signals that Strategic Styles are important but are not everything in terms of individual advancement. Intelligence counts—you can be a genius while subscribing to any of the four styles. Education matters—no one is going to hire an accountant for an engineering position and vice versa. Experience is relevant—a person with in-depth, successful experience in an area can have an “edge” offsetting that of any style. There are many other such factors. Any of these other factors can and have been leveraged to offset any “I Opt” style-based advantage. However, knowledge of the style advantage is a useful signal alerting a person to the need to invest in one or another of these other factors.

In final analysis the sampled population has sufficient diversity of styles to provide resilience to disruptive events and the directional energy to maintain the system on an ongoing basis. This is a definition of a social ecosystem. Organizational science may have more similarity to biological systems than is currently recognized.


1.  Organizational citations are for unique organizations. Subsidiaries were collapsed into their parent as were multiple location wholly owned entities.

2. A general orientation to the “I Opt” paradigm can be found by viewing the 8 minute YouTube video "I Opt" Strategic Styles and Patterns at:
An explanation of the dynamics of the input>process>output model can be found in the YouTube video Team Tension—Causes and Management.

3. “I OPT” VALIDATION:  “I OPT” technology has been extensively validated both in terms of theory and operation.  The major publications on the subject include:

a)      A book has been published which covers all eight accepted tests of validity is available from Professional Communications at a modest cost. The book is available free of charge at the Organizational Engineering website at: An included resume outlines the extensive professional qualifications of the author.
Soltysik Robert (2000), Validation of Organizational Engineering: Instrumentation and Methodology, Amherst: HRD Press. 

b)      A doctoral dissertation titled A Study of Intuition in Decision-Making using Organizational Engineering Methodology was approved by Nova Southeastern University in 2000. The dissertation used “I Opt” as both a subject and research instrument. The dissertation was subject to review by an independent doctoral research committee headed by a Ph.D. focused on research methods and found to meet all academically accepted standards of validity. The complete dissertation is available free of charge at The dissertation is also available in book form as: Fields, Ashley (2001). The Effects of Intuition in Decision-Making, ISBN-13: 978-3639368185, Germany: VDM Verlag Dr. Müller (August 18, 2011). Available from

c)        “I Opt” Style Reliability Stress Test: A sample of 171 surveys applied a classic test-retest design covering a period of 18 years to test the reliability of the “I Opt” instrument on styles (i.e., short term decision responses). The results far exceed the reliability of traditional instruments (i.e., MBTI, DiSC, Firo-B, 16PF). The research is available of the Google research blog in textual form at: 
A 10-minute video of the study is available on YouTube at:

d)       “I Opt” Pattern Reliability Stress Test: The same data as used for style reliability was applied to patterns (i.e., long-term decision sequences). The change between test-retest was found to be negligible. The research is available of the Google research blog in textual form at:
A 15-minute video of the study is available on YouTube at:

e)      Operationally “I Opt” has been validated through continued worldwide use at all levels from hourly workforces to Board of Director levels of Fortune 50 organizations in the profit, non-profit and government sectors. An outdated (last updates 15 years ago) listing of the organizations involved can be found at  Many of the clients cited have continued to use the technology for decades and many more pages of new clients could be added if the list were to be updated to today. 

4. Combining Assistant Managers with Supervisors raises the question as to whether the two types of jobs may have different strategic style demands put on them. A statistical test found only one marginally significant difference (p=.0166) of 4.2% difference in average LP strength. This difference is one of degree and not direction as shown in Graphic below. For present purposes this small difference in LP can be safely ignored.


Project Managers were also considered for inclusion in the analysis. They occupy a level between the professional and management levels. The typically have more authority than does the professional staff. But have a narrower range of options than do people at full managerial levels. Also, analysis revealed that Project Managers were statistically indistinguishable from others in the organization. The combination of these factors lead to the decision to exclude Project Managers from this study.

5. Fields, Ashley. A Study of Intuition in Decision-Making using Organizational Engineering Methodology. Ph.D. dissertation, Nova Southeastern University, 2000. The complete dissertation is available free of charge at