Friday, March 03, 2017

Information Technology: Senior Executive Organizational “Fit”

by: Gary J. Salton, PhD., Chief of Research and Development
      Shannon Nelson, CEO
      Professional Communications, Inc.

This evidence-based research investigates the degree to which senior IT management “fits” into the organizational structure. The technology applied is able to identify reasons for the conditions discovered as well as the opportunities and exposures implied by those reasons. The study investigates optimal IT reporting relationships by building on exploratory research done by Deloitte Consulting (1,271 CIO’s, 43 countries; see footnote #2). It isolates a cause for varying levels of IT reporting relationship satisfaction. The study also measures the compatibility of senior IT levels to their peer VPs in different functions. It finds both opportunities and exposures in these relationships.

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A companion video both expands and abbreviates this research.  You can access this YouTube video from our website at or by clicking the icon on the right to go directly to the YouTube video.


A common denominator of any difference in strategic orientation will be the information processing methods used. Different strategic focus will require that attention be paid to particular inputs. A specific character of output will be targeted. And a unique mechanism (i.e., process) will be needed to connect the logic of the input used with that of the output issued. This approach is instantly recognizable as an application of the classical engineering model of input-process-output.

Image 1

“I Opt” technology offers a method of measuring the basic information processing strategies being used in real world situations. The scope, predictive accuracy, validity and effective range of “I Opt” has been extensively documented and reported (see Footnote#1 for multiple citations).   Many more such papers, articles, research studies and tools can be accessed on our websites at and

As is the case with any advanced technology, “I Opt” requires a vocabulary to convey meaning in a reasonably efficient manner. Central to this vocabulary is the concept of strategic style. This refers to specific combinations of input-process-output that are repeatedly encountered in life. These combinations of characteristics have been named and those names are used in this study. So as to limit the burden on the reader this research will cite relevant defining characteristics as these terms are used.

The focus of this paper is senior IT leadership. People carrying the title Vice President (including SVP and EVP) are senior executives by definition. The position of Chief Information Officers (i.e., CIO) is not as transparent.  This title can conceivably denote a policy making position or an executive position charged with executing policy laid out by others. 

This study tested the potential misclassification exposure by comparing the information processing profile of two groups—those with the formal VP tile and those with only a CIO designation. Table 1 shows no statistically significant difference between the way CIOs and IT VPs approach issues.

Table 1

The academic standard to declare a difference to be “significant” is .0500 or less (i.e., 95% or better chance that the difference is non-random). The VP-CIO difference does not even approach the academic standard. This commonality means that VPs and CIOs can be combined into a single group for our present purpose. There will be no distortion when comparing IT senior management with other functions.

The database consists of 2,526 senior executives distributed as shown in Table 2. These are the various groups with whom IT senior management interacts in the course of fulfilling their functional role.


The Vice President category in Table 2 consists of VP’s who do not carry a “C” level designation (i.e., COO, CFO, etc.). Table 3 compares IT senior executives (n=147) with these other comparable VP levels (n=1,511). There are two strategic styles that closely approach statistical significance, Logical Processor and Relational Innovator (shown in red in Table 3).

Table 3

The Relational Innovator (RI) style misses statistical significance by 0.1%. This could easily be flipped to significance with an increase in sample size. The magnitude of the difference (i.e., 9%) is large enough to be visible in ordinary transactions at the VP level. Graphic 1 below looks at that difference in more depth.

Graphic 1

Graphic 1 shows the level of commitment to the RI style which is focused on innovation, options and alternatives. It is obvious that it is not the entire range of IT VPs driving IT’s ~10% advantage in this style. Rather it is driven by several “clumps” of highly committed IT senior executives. These are highlighted by designator “A.”  This means that the judgment of the creativity of IT executives is likely to be uneven across companies. This is not unexpected since different firms confront different digital environments (e.g., a focus on innovation vs. security). Companies are likely to select IT executives that match the constraints that the firm confronts. This is the likely source of the variety of positions represented in Graphic 1

Graphic 2 shows the distribution of the Logical Processor (LP) style for IT and other executives. It also approaches statistical significance. But here the variation between the IT and non-IT group pops up and down across the entire range of commitment. It is likely that IT’s lower reliance on proven methods (LP -7%) will be sensed rather than objectively recognized. It is probable that variation in this dimension will remain a nuance coloring a relationship rather than a focus of explicit interest.

Graphic 2

The foregoing addresses the overall relationships. Any averaging process nets out bad and good relationships. Non-IT executives positively affected will tend to balance out those with negative views. The overall effect is that IT executives will tend to be seen as more creative in their approach and perhaps a bit wanting in their attention to detail. Not a bad overall fit.

Individual IT executives deal with particular functions and not averages. Table 3 shows that the response to IT’s approach to issues can vary strongly with between functional areas (red indicates significant levels).

Table 3
(IT Sample Size = 147; Academic significance - 95% or higher)

Table 3 cites the probable relationship of IT with specific areas where our sample size was large enough to be statistically meaningful (see sample size on extreme right of Table 3). The block of numbers on the right shows the percent variation in strength of commitment to a style between IT and non-IT executives. The block on the left is the chance that this strength variation is structural (i.e. statistically significant) rather than just noise generated by the measurement system.

The percentages in red are those that meet the test of statistical significance (i.e., 95% or better chance that the variation is not random). A positive strength number (in the right block) indicates IT is higher in strength on that dimension. A negative indicates that the non-IT function cited is more committed to that strategy. Table 3 shows that IT’s exposure is likely to be localized by function. The importance of any exposure depends on the interests of the parent organization. For example, IT’s malalignment with legal could be inconsequential in a firm with few legal exposures. It could be vital in a heavily regulated industry. There is no universally specific point of exposure.
The “take home” from the above analysis is that IT is well positioned at the level of overall policy. IT commands about as much respect and deference as do other functions. In fact, it is in a somewhat preferred position. The innovative posture it stresses is generally highly valued. Little needs to be done to improve IT’s overall organizational standing.

Specific areas like finance, legal and engineering (in our sample) can “trip up” IT’s generally favorable position. A larger sample may well identify more such points of exposure. The importance of these “disconnects” depends on the specific industry and a firm’s targeted mission within its operational context. Identifying and addressing these specific areas of exposure offers IT the best opportunity for a high return on its organizational investment.

A viable strategy for addressing organizational issues is to first identify the points of contention. This should not be difficult. Positions tend to be subtle at high levels but senior executive sensitivities are typically attuned to pick up nuances. Executive judgements can probably be trusted.

The second step is to identify the “root cause.” The first impulse is to blame the condition on the function involved. For example, finance is stingy and nit-picky.  Or engineering is too focused on intellectual improbabilities. These observations may be true. But they are of little operational value. A more productive strategy is to focus on the information strategies needed by these other functions to do their job. Disconnects between these strategies and those favored by IT are the likely points of tension.

The third step is for IT to design strategies that specifically address the points of tension; different ones for different functions. Strategies that work in Human Resources are unlikely to be optimal for Engineering. Adjustments need not be major initiatives. For example, merely tempering a proposed innovation with an acknowledgment of risks can lessen the concerns of a function whose interests’ center on full understanding before acting (e.g., engineering)

The most appropriate remedial strategies will depend on the area affected. In general IT’s strategy will involve acknowledging the legitimacy of interests involved. Then reframing (not necessarily changing) the position of contention in a way that mitigates (not necessarily eliminates) some portion of the concern.

A remedial strategy can be developed by trial and error. Simply understanding the concept of root cause as applied here is a step forward. Detailed knowledge provided by an “I Opt” assessment can help provide definitive guidance. The last observation is self-serving but nonetheless valid. An accurate assessment of both direction and magnitude of commitment benefits all involved. 

Leader-follower reporting relationships reflect themselves in the ability of IT to realize its full potential. A portion of this condition is registered by level of reporting relationship satisfaction. In general, a positive level of IT satisfaction probably bespeaks of an ability of IT to do its job in something approaching an optimal manner. Everybody is on the same page.
Deloitte Consulting recently addressed this issue (see footnote #2 for citation). Deloitte surveyed 1,271 CIO’s domiciled in 43 countries. The survey captured both IT reporting relationships and the satisfaction being enjoyed by the IT executive. The results are shown in the first two columns of Table 4.

Table 4

A majority of IT executives in Deloitte’s sample reported to either the CEO (33%) or CFO (22%). The difference in satisfaction levels is striking—89% found working for a CEO to be satisfying in contrast to 18% working for the CFO. The COO and Business Unit Manager fall about in the middle. The ~30% satisfaction level is higher than CFO and lower than with the CEO. The question of satisfaction level appears to be settled by the Deloitte study; the question of “why?” is unaddressed.

One difference in the reporting levels is that, with the exception of the CEO, each is focused on a different aspect of that enterprise. This focus requires that each role have a sensitivity to different variables (i.e., input) and favor a particular character of response (i.e., output). Connecting these different input-output particulars requires a somewhat different logic or reasoning (i.e., process). There can be little doubt but that these information processing differences can affect the nature of a relationship.

Any divergence between the information processing favored by the IT executive and that of their reporting principal can be a basis for dissatisfaction. That difference can be local to one style (e.g., level of analysis) or cumulatively spread across all four styles.

The direction of the difference does not matter. Too much stress on a particular posture can be as damaging as too little. Therefore in measuring divergence we want avoid methods that “net out” differences (like averages). What is needed is an index that treats any kind of divergence equally.

Table 4 provides that kind of index in column 3. It is titled “Information Processing Variation.” It registers the absolute magnitude of differences between the IT executive and the various leaders to whom they might report. In other words the sign is ignored. A 5% positive difference in one style and a 5% negative in another style is treated as a 10% difference.

A rough correlation between the leader and IT executive is obvious. The direction of the correlation is fairly certain. The higher is the divergence in information processing; the lower is the satisfaction of the IT executive.

The data also suggests that the degree by which satisfaction falls with the rise in information divergence is large. The CEO/IT satisfaction level is 4 to 5 times higher than the CFO/IT. The degree of divergence is also about 4 to 5 times higher (CEO 3%, CFO 18%). A similar but not identical result obtains when comparing the CFO to the COO. The COO/IT satisfaction is about 2 times higher than the CFO/IT (COO 34%, CFO 18%). The divergence in information processing is also about 2 times (COO 7%, CFO 14%).

The temptation is to say that every 1% increase in information processing divergence yields a 1% decline in satisfaction. However, that kind of certainty would be misplaced. IOPT measures information processing on a ratio scale (i.e., like a ruler). Deloitte’s satisfaction is measured on a nominal scale (i.e., like a light switch—satisfied/unsatisfied). We do not know what the CIO’s standard of satisfaction was or if it is consistent across the 1,271 CIO respondents. The size of the differences makes the direction of the relationship fairly certain. But the arithmetic consistency can only be accepted as suggestive rather than definitive.

“I Opt” technology is not confined to identifying overall levels of commonality and potential tension. It also has the capacity to identify specific areas where tension is likely to arise.

One of the simplest ways is through a measure is akin to the Mode in descriptive statistics. The Largest Single Variation column (column 4) in Table 4 applies this measure to the current research. This is the single area (among 4 possible) where the divergence on issue assessment is most likely to be encountered. This is a crude tool. However, it has the merit of quickly characterizing the differences in an easily understandable manner.

There are some risks. One is that Largest Single Variation may be irrelevant to both IT and the responsible executive. Another is that there may be another style difference just a bit lower in strength but of more relevance. These situations will arise but probably not with great frequency.  

The utility of the Largest Single Variation (i.e., Mode) is that this is the area most likely to be the source of difficulty between the functional head and the IT executive. Addressing this single area is likely to yield the highest return on investment for any relationship improvement efforts.

The Largest Single Variation offers a clue on where to begin looking for the root cause. However, an examination of the CFO/IT executive relationship clearly shows its limits. The 18% Largest Single Variation in the fast acting RS style is highest single variation registered in this relationship. Spontaneous opportunistic actions by the IT executive will be a point of contention. However, such actions may not be all that frequent. Positions based on other styles in the IT executive’s repertoire also matter.

In the case of the CFO/IT executive relationship the differences in all four information processing styles register statistical significance. Graphic 3 indicates that something is going on that arises from the inherent structure of the two areas (i.e., finance versus IT).

Graphic 3

The blue shaded areas of Graphic 3 in LP (structured action) and HA (structured thought) shows the CFO substantially exceeding the IT executive across all higher levels of commitment. It is quite likely that IT will fall short of the CFO’s standards in these areas. Initial IT proposals are likely to be seen as inadequate in both the intellectual justification (HA) and operational specificity (LP). It is unlikely that the CFO’s guidance along these lines will be favorably received by the IT executive.

The situation is reversed on the RS (opportunistic action) and RI (creative options) dimensions. Here IT executives exceed the CFO across all higher commitment levels. And this is not a formula for organizational happiness. Spontaneous RS actions are likely to be seen as irresponsible. Innovative proposals will probably be challenged (not necessarily rejected) on the grounds of risk. Neither of these responses are likely to be well-received by the IT executive.

It is no surprise that the CFO and IT executive satisfaction level registers a rock bottom 18%. The style strength distribution (the red and blue shaded areas) is appropriate to the main mission of each function. There is nothing wrong with the elected styles of either party. The problem is that the mission requirements of the two functions seriously diverge in terms of the kind of information processing needed to do the job.

The central characteristic of the CFO function is protection and risk avoidance. Mistakes carry potentially catastrophic legal and financial consequences. We want our CFO’s to be rigorously conservative in their approach to decision issues. For example, having to restate past earnings due to a classification judgement can affect the wealth of all shareholders—a situation likely to reverberate throughout the organizational structure, including that occupied by the IT function.

 A central characteristic of IT is efficiency and opportunity. Mistakes are typically tolerable. They seldom threaten the existence of the organization. Risk has less consequence. Under these conditions an experimental posture which tolerates risk is appropriate. For example, Google News was initially just programmer’s diversion but evolved into a central feature of the Google search engine. Had that initiative been run by a CFO it is likely that its value would have been challenged with consequent delays and potentially dismissal.

The root cause of the structural tensions is not an irrational condition. The two executives in the relationship spend a majority of their time addressing issues within their main function. The general decision algorithms embedded in their minds from doing their primary function will tend to be applied on decision issues that require joint action. This is what is playing out in the CIO’s low 18% satisfaction level.

Matching information processing profiles generally benefits all concerned, including the organization as a whole. Irrelevant points of tension are minimized. Decisions focus on appropriate variables. The satisfaction of all involved helps insure organizational commitment.

However, there are many other reporting structure considerations beyond information processing alignments. Time available, subject matter expertise, proximity, interest and workload are just a few. The Deloitte descriptive study and the evidence-based research offered by “I Opt” technology provide useful insights to help decision makers gauge the areas and degrees of exposure flowing from organizational relationship decisions and processes. It is usually a good thing.

1.  “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. 

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

b)    “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:

c)    “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:

d)    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.

2.  Kark,Khalid; White, Mark; Briggs, Bill (2015); 2015 Global CIO Survey. Deloitte University Press, Westlake, Texas.  Accessed on the internet December 20, 2016 at

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Monday, May 09, 2016

Gender Bias in Engineering: Root Cause Analysis

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

Click to see video
Earlier studies (see footnote #1 for references and summaries) have shown that engineering is unique. It shares tools with other STEM disciplines but applies them in a unique way. Risk aversion was identified as the root-cause of information processing strategies used (see footnote #2 for citation). Effectively, failure carries more consequence for engineering than it does in other STEM professions.This stance creates a strong culture (see footnote #3 for citation). Things that frustrate this culture engender negative responses. This research investigates whether systematic gender differences create such a condition.

Bias can arise from many sources. Unreasoned ignorance such as found when racial or cultural bias is passed on in families or confined social groups is one. However, behavioral patterns which threaten a desirable outcome can sometimes be assigned to an identifiable group. A bias can then be applied to that group as a means to suppress the offending pattern (see footnote #4 for evidenced-based citation on the existence of bias). This research seeks to determine if this condition exists in engineering.

The tool used in this research is “I Opt” information processing. It is based on the well-accepted input-process-output model of engineering. Information determines what can be thought. What you accept as input matters. You cannot consider what you do not notice. Your intended output sets the social effect of the input. An action output produces objective outcomes that can be assessed. Thought output leaves no trace and can only be assessed by inference.

Process links input and output. It determines how the input and output variables are expressed. For example, structured input (i.e., following some kind of predefined scheme) combined with action output tends to produce predictable outputs of uniform quality. This consistency is produced by the process (structured action) and not what the person “feels” about the subject. The same logic applies to other input/output combinations.

The “I Opt” model produces behavior. Behavior has no inherent meaning. For example, a person might be seen as rigid and inflexible. Alternatively, that same person exhibiting exactly the same behavior might be seen as careful, diligent and dependable (see footnote #5 for reference to a more complete explanation). Inferential analyses that rely on the interpretation (qualitative, second order data) run this type of risk of systematic error.

“I Opt” technology minimizes misinterpretation by focusing on the process that underlies behavior—input-process-output (see footnote #6 for validity citations). In addition it quantifies those components. This limits distortions based on the misjudging of relevance. Relevance might include such things as importance, visibility or degree of conviction.

Table 1 shows the gender distribution of the sample used. The size and distribution appears sufficient to draw meaningful conclusions. The inclusion of organizational level recognizes the differential power residing in organizational rank (see footnote #7 for detail on the composition of the categories used).

Table 1
"First impressions" matter. They can create expectations which can then guide future behavior. A place to begin the search for patterns is in these initial postures.

“I Opt” strategic styles are behavioral sequences (see footnote #8 for detail on the “I Opt” framework). They define behavior most likely to be used when confronting an unfamiliar issue. For example, the author’s dominant style is Reactive Stimulator (RS). When confronting a new issue my first inclination is to search for any input that is relevant (unpatterned input), attempt to apply it (process) in a way that most expeditiously resolves the issue (action output). This is not the only strategy I can use. It is just the one I most prefer (this research evidences the fact that I can access other more thoughtful styles).

Biases are created by differences. Table 2 shows the percent difference of the number of women versus men subscribing to each style as dominant. For example 16% more women than men engineers will choose my RS style as their initial response (see the first row of Table 2). Similarly, 29% fewer of these women than men will elect the RI pattern of exploring for new, untested methods.

The small numbers of women in some of the categories distort the percentages. However, the direction of differences is relevant and unprejudiced. The black entries signal the proportion of women exceeding men. The red entries signal women with less of an inclination than men.

Table 2

A pattern is clearly evident. Women tend to put more emphasis than men on the RS and LP styles (see number of black entries in these columns). They put less weight on the HA and RI styles (see number of red entries in these columns). The issue is whether this has consequence.

Styles can be categorized by output. The RS and LP styles favored by women are action oriented. The HA and RI are thought focused. Both action and thought are needed to get things done. However, the choice of one posture can limit the other. For example, action precludes the need for thought. You do not have to think about something already done. Thought (e.g., assessment, planning, etc.) introduces delays and uncertainties that can frustrate an action.

The thought/action divergence in initial positions is probably not enough to create a bias. But it is enough to alert people that “something is going on.” There are more men in engineering than women. That means that the average man will get a lot of confirmation that his way (see red numbers in Table2) is the “right” way. That implies that the average woman must be “wrong” (see black numbers in Table2). This difference can set a negative direction if not the destination.

Style strength measures the degree of commitment to a style. The greater the strength, the more tenaciously a person will cling to an approach in the face of opposition. The more they cling, the greater is the likely behavioral impact. The greater the impact, the greater is the motivation for a bias formation.

Table 3 shows the style strength differences between men and women engineers.  The average woman is 9.6% more committed to the LP style than is the average man (see Weighted Avg./Total row). This means that the average women will stick to an initial LP position with about 10% more vigor than will the average man. An “inflexible” or “stubborn” attribute is easily applied.

On the other side the average man will hold to the idea oriented RI with about 10.9% more vigor than will the average woman.  One possible inference among the dominant male population could be that women run out of ideas sooner than do men.  In other words, men are more creative.

Table 3

The more important bias generating issue is the 20 percentage point divergence between the mutually exclusive posture of the LP (9.6%) and RI (-10.9%). The average male engineer sees an opportunity for major gains using creative options. The average woman engineer sees the certainty, efficiency and effectiveness of applying existing methods.These are mutually exclusive options

Table 4 shows that this is not a local phenomenon.  The same LP and RI divergence appears in virtually all engineering work areas and among people with all types of academic engineering degrees.  The structural differences will be visible in ordinary interaction.  They may or may not generate the inferences used here. But there can be little doubt that they will have some kind of negative effect.  However, there are mitigating factors that will tend to mask recognition of any biases.

Table 4

Engineering is the most thoughtful of the professions (see footnote #9 for citation). Graphic 1 shows that both men and women favor the use of the analytic HA style. The HA style is highly rational, reflective and measured. Both genders also put the spontaneous RS style at the bottom of their preferences. This commonality (see hardhat symbols in Graphic 1) tempers the expression of pattern-based bias.

Graphic 1

Graphic 1 tells us that any pattern generated bias will exist at a secondary level (see arrows). Secondary styles are used where the dominant style does not apply. Thus the structural divergence is likely to appear as something of an undertow in the flow of engineering transactions. Divergences repeatedly appear but not at a frequency that would make them standout as a part of the organizational structure. On an instance by instance basis they can be discounted. This discounting will tend to mask their function as a source of bias.

But the low participation of women is obvious and it has not escaped attention. The sample contains data on 106 participants in Engineering Advanced Leadership Programs.  These programs typically involve rapid rotational assignments. Participants quickly gain wide knowledge of a number of engineering areas. More importantly, they get working exposure to a large number of executives who can be instrumental in facilitating future promotions. Positions on the program are coveted and not lightly dispensed.

Table 5
Table 5 shows that women make up 34.0% of program participants versus 16.8% in the overall sample. This is evidence that an effort is being made to include more women in the profession. The fact that the women being selected repeat the misalignment seen in other areas (more LP, less RI) is not noticed. Organizational science is not an engineering strong suit.

The secondary style divergence of men (RI) and women (LP) is an underlying cause of pattern based bias in engineering. Once established it can be magnified by the operation of unreasoned ignorance. With a population of 80% men, biases can be readily passed around and magnified. They offer a convenient explanation of occasional problems encountered when working with women engineers. With so few women engineers, there is no counterbalance. It is fertile ground.

Secondary level patterns do not clearly signal a bias. The widely shared highly rational HA style can also mask any bias that exists. Bias is irrational by definition.  Suggestions that it is embedded in the engineering structure are likely be avoided, dismissed, discounted or ignored. When bias presents itself in unavoidable form, a logical “reason” for any disparities will be created (“reasons” are the forte of the highly rational HA strategy). This masking allows bias to persist. 

There are undoubtedly other sources of gender bias. However, the structural divergence identified here is at least one of those causes. It has a behavioral consistency that insures its persistence over time.

Style strength measures the degree of commitment to a style. The greater the strength, the more tenaciously a person will cling to an approach in the face of opposition. The more they cling, the greater is the likely behavioral impact. The greater the impact, the greater is the motivation for a bias formation.

The divergence in male-female secondary styles could be caused by nature, nurture or selection. Any conclusion would be speculation. It is also of little consequence. Averages are based on distributions. The relatively small divergence (~10%) suggests that within the female distribution there will be many individual women who match or surpass their male counterparts.

The STEM focus of attracting more women into engineering is on target. More women will be available to de-fang bias as it surfaces. Also, among the women attracted there will be those who are visibly exceptional. Their example will weaken the pattern-based rationale underlying the bias. The “just add women” strategy will work. Bias will be mitigated but not resolved.

Attracting more women with stronger idea-oriented RI inclinations could re-balance the structure. These people are attracted by the opportunity to discover new, unexpected relationships. They are motivated by things that have the potential of yielding major gains. Programs that stress this potential within engineering will erode the structural basis of bias.

It will take years to substantially increase female representation. A basic engineering degree takes four years. More years will be need for these women to accumulate the credentials to be accepted as full peers. A complementary strategy can facilitate the process. It will also improve the lot of the women already in the field. As a bonus it can improve the effectiveness of the engineering process itself. Everybody can win.

This complementary strategy involves redefining the “meaning” of the structural divergence. Currently the structural divergence creates a low-grade but irreconcilable conflict. This gives rise to negative emotions which in turn encourage bias. The complementary strategy must involve demonstrating as well as preaching. HA engineers do not take things on faith.
The core of the issue is an irreconcilable structural divergence problem.  What is needed to reconcile the two mutually exclusive approaches is a common basis.  The subject of the engineering effort is a natural common focal point. Everyone involved shares the desire for success. A tool that demonstrates a rational method to guide the application of the divergent approaches is likely to be well-received by all involved.

The “I Opt” TeamAnalysis™ is such a proven tool.  The technology recognizes the relative strength of each style and the overlaps between the specific individuals in the group. Using this information the report is able to generate detailed recommendations. Conflicts born of structural divergences are automatically recognized and addressed by providing detailed alternative options.  The group applies the knowledge with the common denominator of the specific engineering effort in which all are engaged (see Footnote #10 for detail on TeamAnalysis).  It is a safe, effective and proven tool.

Attracting women and adjusting the profiles can diminish gender-based bias in engineering. However, to be truly effective the women attracted have to be retained.  Graphic 2 shows that about 70% of men engineers are retained versus 60% of the women engineers—a 15% incremental loss among women. It is unknown if the incremental women leaving engineering are those with stronger idea-oriented RI preferences. However, any incremental loss of will act to frustrate strategies to reduce bias.  Earlier studies have offered suggestions on how this might be addressed by making engineering more “woman friendly” (see Footnote #11 for options detail).  
 Graphic 2

Bias can be based on many environmental, social and personal factors. This research does not pretend to have addressed all or even most. However it has identified, quantified and offered fact-based options for addressing at least some of them. It is hoped that this effort will help reduce the discrimination now being experienced. To the extent that it is successful it will improve the lot of women engineers.  But even more importantly, it will improve engineering itself.

1.  A series of recent studies have focused on examining the 
     engineering discipline. These studies include both textual 
     and video formats.   
The Engineering Personality. Salton, Gary J., 2014. This study demonstrates that the engineering “personality” traits are an entirely predictable outgrowth of the job being done.  It goes on to show how these traits arise, quantifies the unique character of engineering and explores the implications of the findings.
Text Research Blog:

Video (15 minutes):
Women in Engineering. Salton, Gary J., 2015. This study quantitatively demonstrates that the gender bias in engineering has nothing to do with the intellectual demands of the profession by contrasting it with science where women are better represented.  The study traces the core of the engineering/science difference to different consequences of failure in the two areas.   This difference demands that the tools common between science and engineering be applied differently. The difference in tool application generates the social differences that impact women’s participation.
Text Research Blog:

Video (17minutes): 

Engineering Insights. Salton, Gary J., 2015. Information processing commonalities generated by the structure of the engineering mission are measured. The study shows how this naturally results in an extremely strong culture. This culture has advantages and exposures. One such exposure is a systematic gender bias. The corollary positive counterpart is the efficient and effective discharge of engineering responsibilities.
Text Research Blog:

Video (13 minutes):

Women in Engineering Leadership: Salton, Gary J., 2015. This study shows that the “I Opt” profile of women promoted from the professional pool match the profile of men similarly promoted. Equity in the opportunity for promotion is evidenced. The gender problem arises from the character of the professional pool. The number of women whose profile matches that of managerial jobs is relatively low. A naïve judgement based on the numerical shortfall is that women are being “discriminated against.” Actually correcting the condition requires that engineering attract and retain women with the requisite managerial profile.  Various methods of accomplishing this are offered.
Text Research Blog:

Video (14 minutes):
2. The Women in Engineering video from 7:35 minutes to 9:30 minutes explains the core difference between science and engineering.
Video (17minutes):

3. The Engineering Insights video from 3:55 minutes to 5:50 minutes explains the factors and processes governing the creation of the engineering culture.
Video (13 minutes):

4. The Women in Engineering video from 25 seconds to 1:30 minutes outlines the evidence for systematic bias in engineering.
Video (17minutes):

5. The video Introduction to the EIM Report explains how the interpretation of behavior (i.e., patterns of action) can vary with the perspective. This process applies to the researcher as well as the research subject.  The relevant part of the video begins at about 4 min 30 seconds and extends to about 6 min into the video (1½ min total).   You can view this video on YouTube at: 6

6. "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 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

 I Opt” Style Reliability Stress Test: The 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: 

“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:

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.

7. Category Definitions of Sample Data:The sample is categorized by
     organizational level so the reader can discern whether differences are
     local to a particular level or distributed throughout engineering.

      The Professional category consists of professional, non-supervisory, degree
      holding engineers actually working in engineering roles.
      Project Manager is event (i.e., a specific task that has a defined end point) driven
      activity. Its importance can vary from responsibility for the construction of a multi-billion
      dollar production facility to the CAD/CAM design of knobs for a new instrument.  The
      role functions as both a terminal specialty role and as a route to higher management
      levels. The roles’ managerial responsibilities are typically confined so as to give
      greater focus to objective project. For example, Project Managers typically have
      “dotted line” relationships to those assigned to the project.
      Supervisor is a first level function whose focus is on the maintenance of an ongoing
      process rather than an event. Supervisors typically carry the full range of managerial
      Mid-management consists of managers, directors, general managers and other
      similar titles that typically do not carry corporate officer status. As with Project
      Managers, the range of responsibilities within this category is broad.  For example,
      the budgets controlled by these titles can range from tens of millions to only thousands
      of dollars.

      Senior Management
consists of Vice President level engineering executives usually
      with officer level
status within their corporate entity.  It also includes Chief Engineers
      who may or may not hold that status

8. A general orientation to the “I Opt” paradigm c
an be found by viewing
    the 8 minute YouTube video "I Opt" Strategic Styles and Patterns 


    An explanation of the dynamics of the input-process-output model
    can be found in the YouTube video Team Tension—Causes
    and Management 
from 1:30 minutes to 2:10 minutes.

9. Thoughtfulness is typically associated with the analytical Hypothetical
    Analyzer (HA) style. The HA strategy involves system level 

    comparative evaluations of multiple options. This thought orientation 
    mandates that all considerations involved in an issue be treated 
    objectively. Irrational biases are an anathema to these calculations. 
    It can be expected that any anomaly will be seen as having some 
    form of rational basis.  The thought processes of engineers can be 
    seen in the behavioral cascade section video of The Engineering 
    Personality YouTube video starting at 3 minutes and ending at 
    about 6 minutes in the presentation.
10. The video  IOPT TEAMANALYSIS ORIENTATION  video provides
       a walk-through of the TeamAnalysis report as well as information 

       on how it is constructed with the data provided. Video (9minutes):.

11. The Women in Engineering video from 13:15 minutes to
      16:15 minutes outlines some of the options for improving
      engineering’s attractiveness to women.
Video (17minutes):.

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