Tuesday, July 18, 2017

Information Technology: How Different are We?

An earlier evidence-based study (see footnote #1 for reference) showed that Senior IT executives differed significantly from their peers in other functions. The cause was traced to the unique nature of the job being done.  This study expands on that research to quantitatively discover just how different IT is from other organizational functions.

This evidence-based research samples 3,673 IT people from all levels (Sr. executives, mid-management, professional staffs). It compares these people to 44,448 non-IT people from similar levels in other organizational functions. The study found statistically significant differences that show IT is in a natural leadership position.

The differences found are not great. This is seen as a positive. IT leadership can be exercised while preserving a common culture and with minimal dysfunctional turmoil.  The study provides evidence that IT departments in diverse firms are more alike than different. This means that the IT discipline as a whole is likely to exhibit a consistently positive leadership momentum across the various industries in which IT participates.

The study found that IT people were more different from each other internally than IT is externally different from other non-IT functions. Finally, the study compared IT to engineering. It found a near identity in approach. It also found that this identity was due to IT women.   IT men were substantially different from their male engineering counterparts. 

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

The current study draws its evidence from a sample of 3,673 people in Information Technology at all levels from senior management to non-management professionals.  These were contrasted to a database of 44,488 non-IT respondents who occupy similar organizational levels. The database contains subsections divided by organizational rank, job titles and gender.  The individual charts and tables used here contain citations of the sample sizes of the subsections of the database applied to a particular segment of the analysis.

The proportion of males and females was tested. It proved to be reasonably close to the Bureau of Labor Statistics’ 46.8% estimate for total female workforce participation and the 25.5% estimate for women in computer related professions (see Footnote #2 for reference). Overall, the sample appears to be reasonably representative of the IT and non-IT professional populations. 

The work content of IT and non-IT functions will be different. Every functional area must adopt an information processing strategy suited to its mission. They must pay attention to particular inputs. A specific character of output will be targeted. And a unique mechanism (i.e., process) is needed to connect the input used with that of the output issued. This approach is instantly recognizable as the classical engineering model of input-process-output. This is the principal tool used in this research.

Image 1

The measurement applied to the classical model is “I Opt” technology. Central to its vocabulary is the concept of strategic style. This refers to specific combinations of input-process-output that are repeatedly used in the conduct of life (see Footnote #3 for validity and effectiveness citations).  Table 1 outlines the major variables used in “I Opt” technology. It does not summarize the theory (i.e., what causes what and why) underlying “I Opt” technology. It merely provides a simplified orientation.

Table 1

Styles define the way a person interprets the world. The way a person “interprets” the world affects the behavior that we see in each other. A practical example demonstrates the connection. A person focused on detailed input automatically restricts the decision horizon. Detail grows exponentially the farther one looks into the future.  At some point it overwhelms the finite human mind. A future beyond that point cannot be “seen” (see footnote #4 for fuller description of style generated behaviors). A person who is not confined by a demand for detail faces no such restriction. Different strategies endow people with different capabilities.

The principal measurement tools applied to the styles in Table 1 styles are standard statistical tests. A two-tail t-test assuming unequal variances is used to determine if the groups being compared are really different or just random measurement variations of the same population. This is a trusted and conservative tool for judging differences between groups.


Graphic 1 is a visualization of the testing procedure.  The graph defines the level of commitment of two samples (red and blue lines).  They are very similar in structure. The statistical t-test tells us that we can be 99.9% certain that the two groups are really different.  In other words, the two graphs are not just wiggles in the level of commitment of the same population that was sampled twice. Rather, they are two different populations superimposed on each other. The average person in each group interprets the world in a somewhat different way.

Every functional area does different things. All of them have access to all four styles cited in Table 1 (i.e., different mixes of input, process, and output).  If the difference in style elections is too large coordination issues will begin to arise.  If the difference is too little, they could miss important variables that they should consider (e.g., everyone sees blue trees because they are all wearing blue glasses.) 

The style strengths combining all organizational levels of IT (n=3,673) and Non-IT (n=44,498) were compared. The use of the opportunistic RS style showed no difference. The three remaining styles showed highly significant differences (99%+ confidence that the difference is not due to measurement variation).  This tells us that the differences are structural. These differences will reappear if we were to repeat the test on a different sample.  But it does not tell us how much they are different.

Comparing the average strength of commitment tells us that these “real” differences are not strong. Graphic 2 shows the greatest measured difference—a variation of 7% on average.  A consistent group of IT people place a low reliance on the LP scale (see ‘A”) while an equally consistent number of non-IT people rely to a greater degree on that style (see “B”). 


Graphic 3 (below) shows that IT also has a structural difference in the two intellectually based styles. IT has a 2% stronger commitment to the analytical HA style and a 4% higher allegiance to the idea oriented RI style than do their non-IT counterparts. 


These differences are small but “real.” They are not large enough to be explicitly recognized. But they are likely to be sensed over repeated interactions. They will enter into our evaluations of each other. IT’s stronger analytical HA and idea oriented RI postures tend to be highly valued in modern firms. They are likely to become a basis for a little differential respect. This respect is can act as an inducement for new entrants to elect IT over other functions. It is a good thing.

A natural tendency is to see differences and ignore similarities. It is worth noting the area under the curves. About ±90% of the people in both IT and Non-IT occupy a position in this area. This means that most people in both areas will tend to view the world through roughly the same lens. This commonality is what gives rise to a corporate culture.  A common culture is what allows large, complex organizations to exist over time.

Section 3 describes how the IT function as a whole is likely to be perceived by the organization as a whole.  However, interactions between IT and other functions tend to occur at similar organizational levels. It is worth examining these individual levels.

IT senior management level differs significantly from other areas in only one dimension—the idea-oriented RI style by about 8% (95.2% confidence that the difference is real). They are likely to be viewed as forward thinking contributors by their peers.

Mid-management (managers and directors) is a different story.  They differ significantly (99.99% confidence) from their non-IT peers by registering 10% less in the opportunistic action RS style and by 5% less in the methodical action LP style. These are both action oriented styles. This posture will tend to position IT mid-management as being a bit slow out of the blocks in the opinion of other functions. As a group they are unlikely to be seen as having a sense of urgency.

The situation changes once again when Professional (non-management) levels are considered. Like mid-management the professional level registers a highly significant 8% less inclination toward methodical execution LP style. However, they have a 3% greater tendency (99.4% confidence) to use the analytical HA strategy and a 6% greater predisposition toward the idea-oriented RI (99.99% confidence). The people working in the trenches are likely to be seen as innovative thinkers by their non-IT peers,

The magnitude of the style differences described above can be visualized by graphing the distributions. Graphic 4 shows the distribution of IT and Non-IT professionals by style.


The red shaded areas show where IT exceeds non-IT in strength. The blue shaded area is where non-IT exceeds IT.  Notice the consistency across the range of style strengths (i.e., the solid blue and red colored areas). This is what the “significance test” is picking up. The statistics are telling us that the difference represents two unique and different curves being superimposed on each other. They are not just wiggles in the same curve. The direction and amount of the average difference is indicated by the arrow and percentage figure.

Overall IT is a bit different than its non-IT counterparts. The idea-generating RI style remains the signature characteristic of IT.  Senior IT management will be seen as sponsoring and supporting new initiatives.  Professional IT will be seen generating options and alternatives at a higher than expected rate.  Mid-management will be viewed as quelling the implementation a bit but not at a rate that would be threatening to the mission.

The overall story told by the global analysis of IT versus other functions remains intact. IT is likely to have an overall positive image regardless of the level upon which it is approached. This likely translates into an ease of recruitment and higher retention probabilities. IT fits well as a leader in the matrix of the various organizational functions.

Virtually every IT group with whom we deal thinks of themselves as unique among their peers. In one sense this is probably true. Every IT group has a somewhat unique central objective (e.g., dependability vs innovation), relies on rather different tools (e.g., C++, Python, Java, etc.) and runs a different mix of applications.

However, what is really at issue is the underlying capacity of the group.  People maintaining their uniqueness are likely to be referring to is their capacity to respond quickly (RS), innovate more aggressively (RI), provider deeper insight into issues (HA) or to execute with greater precision (LP).  This understanding can be tested with hard data.

The sample database included in 12 IT firms and one Research University where the sample of IT group members was large enough to statistically test all four “I Opt” style dimensions. This produced at total of 312 points at which a difference could occur (see footnote #5 for a table based visualization). Each of these points was tested for statistical significance. The academic standard of 95% or more certainty that the difference is “real” (i.e., structural in the sense that it is likely to repeat over multiple tests) was used as the significance cutoff.

Applying the academic standard to the data revealed that the average IT unit will find itself using a different strategic style than another IT unit about 29% of the time; 71% of the time they will discover the other IT unit is approaching issues in the about same manner as they do. This statistic is notable. But it just speaks to the occurrence of a difference, not to its direction or size.

When 29% difference is encountered it is likely to average about 15.9% in strength. However, Graphic 5 shows that this average is a composite of very different style strengths.


Thought-based (i.e., intellectual) idea-generating RI and analytical HA styles are less than half as strong as the action-based RS (opportunistic action) and LP (methodical action) styles. When differences in the thought-based strategies are encountered, they are likely to be of a nuanced rather than an “in your face” variety.

Graphic 5 shows that the action based differences are not only more powerful but they are likely to be encountered more frequently. These styles are likely to center on “how” work is done (i.e., RS and LP action styles) rather that “what” is being done (i.e., the more intellectual RI and HA styles). “How” is easier to see than “what.” This notice-ability helps to explain why IT groups tend to think of themselves as “unique.”  These “in your face” differences are much easier to see than are the commonalities. But in reality, commonalities are the more distinctive characteristic of IT groups.

IT professional staff is a composite many different functions. An incomplete sampling of the diversity of titles from our database is provided in Table 2. The commonality running through all of the IT titles is the general focus on computers, programming and/or networks. However, how the way that common content is applied can differ markedly.

Table 2

Our earlier Organizational Rank and Strategic Styles (see footnote #6 for reference) study provides a tool to be used to sort out various groups. That study included a “stress test” which showed strategic style variation within a single organizational rank was predictable from the character of work content. This study builds on that concept to create Feedback Horizon categories. The Feedback Horizon represents the speed at which an organization can be assured that the work effort has been successful (see footnote #7 for an elaboration of the Feedback concept). Table 3 applies the Feedback Horizon to the job titles in our database.

Table 3
Table 3 internal consistency was tested using a sampling strategy.  A sample of 88 comparisons between specific job titles yielded 3 individual styles where there was a significant difference (3.4% of the 88 sample).  Two of these occurred in the Medium Term and one in the Short Term categories.  Overall, the internal consistency was judged to be adequate for the purposes at hand.

The second criterion is that the groups have to be different form each other. The left column of Table 4 displays the results of that test. Only a single medium term horizon group’s RS style category (see yellow highlight in Table 4) failed to reach academic statistical significance (+95% chance that the groups are truly different from each other). The single difference is judged to be tolerable in the context of this study.  The groups are different from each other.
Table 4

A certification of difference does not speak to the degree of that difference. The right hand column in Table 4 is the difference in average strength for each style. The absolute difference average (i.e., the direction of difference is ignored) of all of the individual differences is 19.4%.  This is larger than the differences between IT and other functional areas.


Graphic 6 is a visualization of the differences in the individual styles. The structure of the lines follows results obtained in an earlier, larger study of style-job relationships (see previously cite footnote #6 for reference).  This cross-confirmation tends to lend credibility to both studies.

The downward sloping analytical HA and action-based LP styles both rely on predictability to be effective.  Both draw on predefined “maps” (i.e., patterns) to guide their work. The accuracy and value of these “maps” declines as the Feedback Horizon lengthens. In the real world time always introduces unexpected variables. These accumulate and will compromise any predefined strategy—intellectual (HA) or operational (LP).

The same logic explains the increasing value of the option-generating RI and opportunistic RS styles as the Feedback Horizon lengthens.  Neither requires any kind of predetermined pattern.  This increases their value as the time between action and feedback lengthens. New unexpected variables can be treated as opportunities rather than obstacles.

The “take home” from this section is that the components of IT’s professional staff are more different from each other than is IT different from the other functions. This is no accident of the IT functions history.  It is built into the nature of the jobs being done at the professional level. It is here today and it will be here tomorrow. The managerial challenge is permanent.

This analysis offers some directional guidance for IT management. It tells management that they will have to invest in people if they want to create a career ladder for all involved.  Strategic styles can change over time. Those that want to advance up the hierarchical ladder need to be provided with the opportunity to develop competence in the styles appropriate to the targeted level.

Another element of guidance is that management must make an investment in developing a common strategy that all elements of the IT workforce can “buy into.”  This will not be easy.  It will take more than writing a memo or a publishing a statement of purpose. It will require interacting with people who see the world in different way and have different expectations.

IT will probably function at some level of competence even if nothing is done. Everyone has access to all four styles. It is likely that no one wants failure and they will eventually access the style appropriate to the issue rather than just relying on personal preference. However, if management can harness the diverse power of all four styles, outstanding results move from possible to probable to the inevitable.

Electrical engineering gave birth to information technology. IT is fond of pointing to this common heritage. Engineering and IT share many common intellectual and operational tools. But everyone that uses a wrench is not a mechanic. It is worthwhile to see whether both fields share common strategic styles (i.e., issue understanding) when pursuing their work.  Table 5 suggests that this may be, in fact, the case.

Table 5
(IT more + or less – than Engineering)

At a functional level there is no significant difference between the issue resolution approach of IT and engineering on any “I Opt” style dimension.  However, if we dig a bit deeper we find that this identity of perspective has an unexpected foundation.

Table 6 compares females in IT versus females in engineering. The single difference is in the RS style (opportunistic action) and that difference only reaches marginal significance levels. For practical purposes the same kind of women are being attracted to both professions.

Table 6
(IT more + or less – than Engineering)

The story is different when IT males are compared to males in engineering. Table 7 shows that males in IT and engineering differ significantly on all four style dimensions. IT is drawing in different kinds of men than is Engineering.

Table 7
(IT more + or less – than Engineering)

Table 7 shows that IT males have significantly less LP (-6%) and HA (-3%) than do engineering males. If IT were to consist entirely of males it would mean less performance consistency, less planning and less diligence than that displayed in engineering.

Table 7 also shows that IT males have appreciably more RS (+7%) and RI (+5%) strength than does engineering. In a world of entirely male IT personnel this would mean more errors, more adequacies instead of excellence and more chaos than that now experienced.

But there is a good side. The lower level of structured styles (LP and HA) and the higher levels of the more opportunistic strategies (RS and RI) make IT more open to new options.  Graphic 7 shows that IT has over twice engineering’s level of female participation (27% versus 13%).  In effect, the very condition that would distinguish IT from engineering (male style profiles) may be the reason it is able to attract more women who then level the information processing playing field with engineering.

1970 TO 2011

Both the LP and HA styles use a structured strategy in conducting life. Structured approaches rely on predefined patterns—norms, rules, formulas, conventions, etc. These patterns tend to be interwoven.  A change in one can require changes in others. This interdependency makes change difficult. The fact that IT males have less commitment to structured strategies makes it a bit easier to accept change—including changes in the female workforce composition.

Another plus for the IT males accent toward the unpatterned RI and RS styles is it furthers IT’s position of organizational leadership. The new ideas and responsive action is leavened by a strong female commitment to consistency, dependability and precise execution.  The combination produces an organizational leadership profile that is forward leaning without be so strong as to be out of range for the remainder of the organization.

In summary, IT is on target in claiming a kinship with engineering.  But that kinship is built on a mix of strategic styles very different from that of its engineering brethren. That mix, along with a broader range of responsibilities, allows IT to assume a more prominent role in setting organizational direction.

IT sits in an advantageous position at this juncture of business history.  It is responsible for the tools that will be key to the future direction of any organization.  It is favorably structured in terms of the way that it approaches issues.  Its higher RI strength insures that it will be prepared to offer new ideas and options to meet challenges as they arise.

IT’s overall style strength profile is well suited to its mission. Its RI strength is not so high as to be “out of range” of the functions with which it must interact. IT’s structured approach is sufficiently disciplined to handle the rigorous demands of technology that it commands. In final analysis, IT is different from other functions but not so different as to be dysfunctional.

The organizational challenge of IT is internal. The variety of information processing postures held by its staffs mean that expectations and capabilities vary widely.  The internal challenge is two-fold.  IT management must develop coherent strategies that can positively engage the variety of people involved.  That will require considerable and continuing work by the leaders of the various IT functions.

The other challenge is to source and develop the staffs who will become future leaders. Finding and attracting women who are better aligned with the strategic profiles of the male component of IT is one possible step. It could vastly increase the IT resource pool. But on a broader level, everyone in IT would benefit from a management program that helps all groups within IT develop their strategic style talents in a way that benefits them as well as the organization as a whole.  That “soft side” managerial talent capacity is probably the scarcest commodity in the current IT organizations.

1.    Salton, Gary J., 2017.  Information Technology: Senior Executive Organizational “Fit”:   Evidence-based measurements are used to determine the overall standing of senior IT executives versus other senior executives. “I Opt” technology was applied to extend a Deloitte Consulting (2015) study. It showed that IT satisfaction directly related to information processing profile overlaps between the levels. Predictable points and degrees of tension could be identified.  The study also measured the overall fit between 147 senior IT executives with 1,511 non-IT VP’s identifying specific points of opportunity and exposure.
Text Research Blog: http://garysalton.blogspot.com/2017/03/information-technology-senior-executive.html
Video (15 minutes): https://www.youtube.com/watch?v=0PHvPIR6rVU&feature=youtu.be
2.    United States Department of Labor, Bureau of Labor Statistics, Feb 8, 2017. Table 11. Employed persons by detailed occupation, sex, race, and Hispanic or Latino ethnicity. Accessed April 6, 2017: https://www.bls.gov/cps/cpsaat11.htm

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:  http://www.oeinstitute.org/articles/validity-study.html. 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   http://www.oeinstitute.org/articles/ashley-fields.html.

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 Amazon.com.

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: http://garysalton.blogspot.com/2011/03/i-opt-style-reliability-stress-test.html. 
 A 10-minute video of the study is available on YouTube at: https://www.youtube.com/watch?v=Vs6eoIsqVkc

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: http://garysalton.blogspot.com/2011/03/i-opt-pattern-reliability-stress-test.html.
 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 work force to Board of Director levels of Fortune 50 organizations in the profit, non-profit and government sectors. An outdated (last updated 15 years ago) listing of the organizations involved can be found at http://www.iopt.com/corporate-information.html.  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.    The underlying concept is that information determines what can be done. For example, the absence of detail automatically limits the degree of precision possible. Similarly, any intended output precludes certain options and favors others. For example, thought oriented output (i.e., plans, evaluations, calculation, interpretation, etc.) impedes spontaneous action. The interaction of the various postures (both kind and degree) within a group determines probable group performance.

The table is a simplified outline intended to convey the general principles underlying “I Opt” technology. It is NOT the theoretical foundation of “I Opt.” A more complete exposition of the concepts and construction of strategic styles is provided in the “Team Tension – Causes and Management” beginning about 2 minutes into the video. This can be accessed at:
YouTube: http://www.youtube.com/watch?v=xQ_5b4BUUB0&feature=youtu.be
Google Research Blog: http://garysalton.blogspot.com/2013/01/team-tension-causes-and-management.html

5.    Significance tests were run on every possible combination of the 13 organizations. These were posted to a standard matrix. Each style offered 78 potential points of difference.  All four styles produce 78 x 4 = 312 total points at which a difference can occur.

6.    Salton, Gary J., 2012.  Organizational Rank and Strategic Styles  A study of 10,617 executives from 1,559 different organizations disclosed a systematic, statistically significant relationship between “I Opt” Strategic Styles and organizational rank. This condition is caused by a single factor that is an inherent quality in any organization. This factor, in combination with corollary conditions, sets the value of each style at various organizational levels. This value then becomes effective in hiring and promotional decisions. The net result is a tendency for people at a particular organizational level to share a common information processing perspective.
7.    The Organizational Rank and Strategic Styles study (above) identified predictability as the factor determining whether a particular strategic style was favored at the various organizational levels.  A “stress test” showed that this factor also could be applied to work content characteristics to various jobs at a single level. Within a single organizational level predictability works through the agencies of opportunity, incentive, and resolution.  All of these factors are correlated to the speed at which work provides feedback on the adequacy of the effort.

The “feedback” in question is NOT the speed of reaction of interim efforts
(e.g., whether code works or not). Rather “feedback horizon” is defined as the speed at which the organization surrounding the IT worker is able to definitively recognize that work as a successful contribution to the organization.  That recognition is what determines the fate of the person doing the job.