University Management

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

ABSTRACT
The information processing structure of universities is studied though a sample of 732 positions at over 100 different universities. Statistics reveal that there are structural disjoints that will result in persistent systematic issues. These structural “disjoints” need not be corrected. They are necessary for the effective functioning of the university,
A self-imposed standard of confining research blogs to about 2000 words requires omitting some useful elaborations. A complementary video which both expands on and abbreviates this research blog is available by clicking the icon to the right.




THE UNIVERSITY
The university has two goals. The inter-generational transfer of knowledge preserves the knowledge gains of past generations. The creation of new knowledge objective is focused on the legacy to be left to the future.

Professors control knowledge content. Surrounding them is a management structure that controls the circumstances through which the content is delivered. Effective functioning requires that the information processing strategies of these elements —their “I Opt” strategic styles and patterns—be aligned.

Each management component serves a different function its strategy must be aligned to fulfill this mission. That strategy must likewise “fit” with the mission of other organizational elements. How well these strategies mesh will determine university success.

University management is arranged in organizational layers. For purposes of this study these units are grouped. Directors are non-academic executives that head functions or programs. Administrators include managers and other supervisory staffs that guide the operations of the functions or programs.The staff consists of professionals and other support personnel who actually execute the needed functions. Table 1 outlines the sample based on these groupings. The size and diversity appears sufficient for it to be considered representative.

Table 1
UNIVERSITY SAMPLE CHARACTERISTICS

THE PROFESSORSThe Professor staff has been analyzed in a separate research blog ("The Professors"). There is no statistically significant difference based on academic rank. Therefore the “I Opt” profiles of all 254 Professors can be consolidated and their averages are shown in Graphic 1

Graphic 1
UNIVERSITY PROFESSOR PROFILE
(n = 254)

The combination of a very high idea-generating RI style (31.5% on Graphic 1) and a secondary analytical HA approach (25.6% on Graphic 1) describes a “Perfector” pattern. In colloquial terms the "Perfector" pattern of behavior can be described as “Great idea!! Let’s think it through completely.” This is not a formula for decisive management but does serve the Professor’s instrumental role of knowledge creation and transfer within a university structure.

THE DIRECTORS
A total of 140 Directors drawn from 69 universities were used in the sample. This category includes Deans whose position do not have academic content. Table 2 outlines the range of functions of Directors included in this study. Overall they appear to fairly represent the range of activities needed to support the university mission.

Table 2
DIRECTOR FUNCTIONS REPRESENTED IN THE STUDY

Directors set policy, practice and procedure. In doing this they extract information from the environment, process it using their own standards and issue output aligned with their responsibilities. Graphic 2 shows how well their profile—and thus their interpretation of decision issues—matches that of the Professors.

Graphic 2
PROFESSOR vs. DIRECTOR “I OPT” PROFILES

Overall, the Directors and Professors are “cut from the same cloth.” The single significant difference is in the decisive action Reactive Stimulator style. Directors are inclined toward acting more responsively than are their Professor counterparts. Graph 3 shows the clear shift in this style preference.

Graphic 3
PROFESSOR vs. DIRECTOR
REACTIVE STIMULATOR PROFILE

THE ADMINISTRATORS

This category of management consists of managers, assistant Directors, associate deans (non-academic) and similar titles sourced from 55 universities. The role of this level of management is in guiding relatively near-term operations in various functional areas. Table 3 identifies some of the areas of responsibility. It appears reasonably representative of the range functions typically involved in university operations.

Table 3
ADMINISTRATOR FUNCTIONS REPRESENTED IN THE STUDY

Administrators are likely to periodically interact with both Professors and Directors on decision matters. The degree to which their information processing profiles match will influence the level of managerial harmony. Matching profiles indicate people are “talking the same language.” Divergent profiles mean that the parties are paying attention to different aspects of an issue, weighting them differently and seeking a different resolution. Table 4 shows the statistical significance of the difference for each strategic style.

Table 4
STATISTICAL SIGNIFICANCE OF STYLE DIFFERENCES
Probability that observed differences are due to chance alone

Professors vs. Administrators:
The academic standard for statistical significance is 5% or less. Table 4 (above) shows that Administrators differ form both Professors and Directors but in different ways. Administrators fall short of the Professors idea-generating RI tendencies. In general Professors are likely to view Administrators as a bit shortsighted and unimaginative. Administrators are likely to see Professors as a tad “blue sky” in orientation and somewhat out of touch with the real world. The degree of difference probably nets out to more of an annoyance than a basis of hostility. Graphic 4 (below) gives a picture of the Professor-Administrator differences across the range of strengths which the RI style is held..

Graphic 4
PROFESSORS vs. ADMINISTRATORS
RELATIONAL INNOVATOR PROFILES

Directors vs. Administrators:
Administrator and Director differences center on the analytical HA style. Graphic 5 shows that it is localized to a particular group of Administrators (see blue arrow).

Graphic 5
DIRECTORS vs. ADMINISTRATORS
HYPOTHETICAL ANALYZER PROFILES

Directors are likely to view most Administrators as roughly in line with their view of issues. But one small Administrator group (blue arrow in Graphic 5) will probably be seen as spending too much time, energy and probably money on analysis, assessment and evaluation. For their part, this Administrator subset is likely to view the Directors as a bit superficial.

Overall, Directors and Administrators are reasonably well matched. The information processing disjoint is local and focused on a small percentage of Administrators (i.e., 17%). Overall, Directors and Administrators may not come to exactly the same conclusion on particular issues. However, they are likely to see the same variables as important, process them in roughly the same way and more or less agree on the degree of analysis appropriate to an issue.

THE STAFFS

The staff is technically not part of management. However, their roles affect the information available to and decisions made by management. In addition the ability of the staff to execute the directives given by management fundamentally influences the functioning of the university system. Non-management staffs matter.

Table 5 specifies some of the roles performed by the 203 staff members from 59 universities included in the sample. It appears to be sufficiently broad as to be representative of the universities non-managerial staffs.

Table 5
NON-MANAGEMENT FUNCTIONS REPRESENTED IN THE STUDY

Table 6 (below) shows 12 “I Opt” relationships between the staff versus Professors, Directors and Administrators. Eight of the 12 meet the 5% or less standard of academic significance. In other words most staff members will tend to “see” issues in fundamentally different terms than do the various levels of management.

Table 6
NON-MANAGEMENT STATISTICAL SIGNIFICANCE TESTS
VERSUS OTHER MANAGEMENT LEVELS
Probability that observed differences are due to chance alone

Table 7 (below) shows 12 relationships between the various management levels without staffs considered. Only 3 or 25% (3/12=.25) of the relationships register a statistically significant difference. Management is a relatively homogeneous group. What this means is that the university’s challenge is integrating the staffs into the management structure in a way that people can “understand” each other. Left unattended this can lead to misinterpretation, misdirected outcomes and emotional stress for all involved.

Table 7
MANAGEMENT LEVEL STATISTICAL SIGNIFICANCE TESTS
Probability that observed differences are due to chance alone

The university structure directly addresses this potential issue in a traditional manner. The interaction of management and non-management staffs is mediated. Administrators and non-management have a marginally significant disjoint on only two levels strategic style levels (i.e., the LP and RI on Table 6). Effectively, Administrators act as a “bridge” between the Staffs and the various levels of management.

The idea-generating RI is one of the points of difference between Administrators and Staff. Graphic 6 shows that the difference divides neatly.

Graphic 6
ADMINISTRATORS vs. STAFF
RELATIONAL INNOVATOR PROFILES

The red arrow shows that the staff tends to prevail the lower levels of RI. The blue arrow indicates that Administrators tend to hold sway at the higher levels. Overall, Administrators are 13% more committed to the RI strategy. Staffs will contribute ideas. But they are likely to be fewer in volume and less radical in content.

The difference in idea-generation is unlikely to be a major issue. Administrators will not feel a shortage of ideas. They are likely to have an overabundance from the Professor and Director levels. Administrators will probably have been learned by experience that staffs are not a productive source of major change options. Hence staffs are not likely to be pestered to generate ideas and options of a major nature.

It is unlikely that the staff level will feel “left out” of the major decision process. It is not a component of their role so their expectations will not be violated. In addition, the university culture typically dictates that major changes are “hashed out” in relatively public discussions. This gives everyone involved a sense of having an “input.”

Staffs are about 17% more committed to the disciplined Logical Processor (LP) style than are Administrators . This is the cumulative result of small differences over a broad range. The general configuration of the profiles of the two groups are aligned. This close “tracking” is shown in Graphic 7.

Graphic 7
ADMINISTRATORS vs. STAFF
LOGICAL PROCESSOR PROFILES

Overall both Administrators and staff are likely to see each other as having about the “right” level of commitment to methodical, deliberate and careful action. The difference in information processing profiles is likely to be of only minor consequence.

Graphic 8 shows the integration of all of the various levels more explicitly. Note that the bold green Administrator line breaches all of the various management levels.

Graphic 8
“I OPT” PROFILES OF ALL MANAGEMENT LEVELS

In general, the administrative management of universities appears to represent an effective bridge. The bridge goes both ways. Administrators are likely to effectively represent staff interests to the other management levels. They are also likely to effectively interpret Professor and Director concerns to staffs.


SUMMARY
The information processing compatibility among the management levels is not perfect. But it is structurally efficient and effective. Overall the differences between levels are functional. For example, the decisive action tendencies of Directors make sense in terms of their role in getting things done. The Professors idea-oriented RI tendencies directly address their role in creating new knowledge. The staff’s LP capacities are essential for smooth day-to-day operations.

The structural differences will give rise to “bumps in the road.” The decisive Directors are likely to cause Professors to see themselves as “left out” of decision making. The segment of Administrators holding a high analytical HA commitment may frustrate the Directors need to get things done. Administrators are likely to feel themselves “caught in the middle” in trying to balance off the needs of the various levels. These “bumps” are structurally built-in. A modest level of tension is probably needed for the university to function.

Locally there may be information processing disjoints that give rise to difficulties of consequence. But when viewed across all universities the “bumps” are likely to be more of an annoyance than a source of hostility. The picture painted by the 732 person sample of this study is one of general satisfaction but with a tinge of annoyance over one or another issue generated by one or another management level. The fact that the source of the discomfort changes with time is evidence that there is no fundamental structural problem.

In summary, universities are to be celebrated. They appear to have devised about as good of a management structure as is possible given their mission and constraints.

The Professors

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

ABSTRACT
The information processing profiles of 254 professors from 109 different universities showed an unexpected finding. A double peak in the idea-generating Relational Innovator (RI) dimension appears to create two distinct classes of professors. This condition exists across academic disciplines. It applies equally to both the hard and soft science disciplines.

The dual peak appears to be the result of the tenure system that favors two distinct activity patterns that give rise to the dual peak. The result appears ideal for the creation and maintenance of reliable knowledge. A corollary to this finding is that threats to the tenure system may compromise the knowledge generation role of the university.

An unintended consequence of the system is that negative “Rate my Professor” ratings are “built in”. They are likely be the result of the strategic style alignment of the students and one category of professors.

A video that both expands on and abbreviates this research blog can be viewed by clicking the icon on the right.



THE SAMPLE
Professors teach many subjects. Table 1 shows that the sample used here can be considered to reasonably representative of professors in the average university.

Table 1
SAMPLE AREAS OF PROFESSORSHIP

Professors also are differentiated by rank. Some are tenured (usually Full and Associate), some aspire to tenure (usually Assistant) and some are contracted teachers (usually Adjunct, Lecturers, Readers, etc.). Table 2 shows that the representation of each group within the sample is sufficient to be representative.

Table 2
NUMBER OF PROFESSORS BY RANK

Universities also differ in size, specialization, endowments, geography and other areas. Table 3 suggests that the range of universities represented in the sample is wide enough to characterize universities as an institutional class.

Table 3
UNIVERSITY SAMPLE DISTRIBUTION

In total, the sample population can reasonably be accepted as a fair representation of university professors in general. Its conclusions are likely to be generally applicable.


THE EXTERNAL VIEW OF PROFESSORS
Information processing elections affect how professors teach, the nature of their research and the likely character of their interactions. “I Opt” technology measures these strategies in as “strategic styles.” These are specific combinations of input and output elections. “I Opt” is fully validated across all eight dimensions of validity and its reliability has been repeatedly checked under both normal and stressed conditions. Its 20-year use in virtually every segment of society further testifies to its relevance. The “I Opt” tool can be trusted as a tool of research. Its appliction to the various professor ranks is shown in Graphic 1.

Graphic 1
STRATEGIC STYLE DISTRIBUTION OF PROFESSORS

The red quotes on the bottom of the graph characterize the style in colloquial terms to facilitate understanding at the sacrifice of some level of accuracy.

The structural commonality between the ranks is visually apparent. All professors put little emphasis on action (the RS and LP strategies). The idea-oriented RI dominates followed by a strong preference for analysis (HA style).

Table 4
STATISTICAL SIGNIFICANCE OF STYLE DIFFERENCES
Expressed as a percent for ease of understanding (vs. p< 05, etc.)

Table 4 shows that the differences shown in the line graph are not material. One statistic (Full vs. Associate professor) approaches the 5% academic standard (p < .05) of significance. However, it is isolated and marginal in value. This argues that it can be considered an anomaly. For the purposes of this study all professors can be seen as being cut from the same cloth.

Profile commonality means that professors are looking at the world through the same set of glasses. They will tend to view issues in roughly the same way. They will tend to focus on roughly the same kind of variables, weight the factors in a similar fashion and arrive at a similar character of conclusion.

The above commonality invites stereotyping. People outside the academy live in more mottled environments. They interact with people of a more varied character. To them, the judgements and proclamations of professors are likely to seem strangely coordinated.

To the non-academic this “coordinated” view is likely to be seen as missing obvious factors that are fully visible to those living in more varied non-academic environments. The net result is that professors will tend to be judged as having an oversimplified view of reality. As a group, professors are likely to be viewed as being a bit “out of kilt” with the “real” world.

THE INTERNAL VIEW OF PROFESSORS
Unlike non-academic outsiders, insiders are able to contrast professors individually and over time. Graphic 2 plots the strength of the idea-oriented RI style for each academic rank. It suggests that they are likely see two distinct groups of professors based on the volume, scope and quality of idea generation.

Graphic 2
RELATIONAL INNOVATOR STYLE STRENGTH DISTRIBUTION
BY PROFESSOR RANK

Except for the non-tenure track adjuncts (green line), a distinct dual peak pattern is visible. These peaks are distinct clusters of professors. One group is about average in the idea-generating RI style. The other is highly committed. This unusual gap separates the two groups. It invites examination.

The subject being taught is a probable cause. Grouping the 254 sampled professors tests this hypothesis by discipline. The groups must be large enough to be representative. They must also be small enough to reveal differences. The balance that was used in this research is shown in Table 5.

Table 5
GROUPING OF PROFESSORS BY SUBJECT AREA

The basis of the groupings is structural rigor. The “constrained” group is governed by relatively well defined, stable theories. These theories are a framework against which new knowledge can be tested.

The “expansive” group has less well-defined theory. The “looser” theory provides fewer comparison points against which to test new knowledge. This difference in structure of the subject matter “should” result in different information processing approaches.

Table 6 tests whether the subjects in each “bundled” group are really using the same basic strategy. It shows that there is no significant difference between the disciplines “bundled” within each of the two groups (i.e., none of the values are less than 5% or p < .05). In other words the bundling appears to be a reasonable approximation of the information processing styles used by the professors sampled.

Table 6
STATISTICAL SIGNIFICANCE OF GROUPING
Expressed as a percent for ease of understanding (vs. p< 05, etc.)

The row at the bottom of Table 6 is a between group comparison. It shows that the two professor “bundles” really do use different information processing strategies. The profiles (i.e., the combination of strategic styles) used by the two groups differ in a statistically significant ways. Graphic 3 defines those differences.

Graphic 3
AVERAGE STRATEGIC STYLE COMMITMENT
BY AREA OF STUDY

The constrained group (e.g., exact sciences, medicine, etc.) puts more emphasis on analysis (HA) and less on new ideas and options (RI). This makes some sense. Well-defined theory makes new ideas costly. A professor’s time and resources in have to be invested in evaluating, assessing and validating the new knowledge against the existing theory. This is a “cost” of new ideas. More ideas, more cost.

The “looser” theory of the expansive group (e.g., management, learning, etc.) means that there is less to analyze. Assessment and validation is done with experimentation. For the professor this is much less expensive. There are fewer papers to write, fewer equations to run and fewer differences to reconcile. All you need do is to “try it.” Ideas are cheaper so you can afford more of them.

It is clear that the two “bundles” or groups of professors are really different in terms of the way they process information. The hard and soft sciences really do tap different information processing capabilities. But does the difference in subject matter (e.g. constrained vs. adaptive) account for the double RI peak? If it did, we would expect to find one peak in each subject matter group. The peaks would be in different places. The merged result would thus give us the double peak. Graphic 4 shows that the cause is not the result of this merging of two single peaks.

Graphic 4
RELATIONAL INNOVATOR (RI) STRATEGIC STYLE STRENGTH
BY AREA OF STUDY

A double peak appears in both groups. This means the source is not in the field of study. Something common to all areas of study and all academic ranks is causing it. The cause must lie somewhere in the structure of the university itself.

The tenure system is a prime candidate. Tenure can be achieved via two routes. One focuses on the creation of new knowledge. This speaks to the university’s knowledge creation mission. If the quality, quantity and scope of new knowledge is high enough, this is all that is needed. However, this is not the only route to tenure.

After realizing some minimum level of published new knowledge other qualities can weight into the tenure decision. Speaking (i.e., representing the university), teaching skill, graduate studies supervision and administrative contributions can also argue for granting tenure. These activities support the university’s knowledge transfer mission. Both routes are valuable to the university. Both types of professors are needed for the university is to fulfill its mission.

A consequence of the double peak can be seen in Graphic 5. Professors highly committed to the idea-oriented RI tend to have a lower commitment the disciplined LP and analytical HA strategies. Professors with a lower commitment to the idea oriented RI strategy tend to favor the more rigorous analytical HA and LP strategies.

Graphic 5
RELATIONAL INNOVATOR (RI) AND HYPOTHETICAL ANALYZER
STRATEGIC STYLE STRENGTH

The net effect of the interplay between the types of professors is that new knowledge is systematically tested. The knowledge created by professors on the upper peak will be tested in detail by the rigorous capacities of professors on the lower peak. The result is the culling substandard work.

All professors have an absolutely high level of RI. Professors on the lower peak will also create new knowledge. However, it is more likely to be of an incremental character. Professors on the higher peak will tend to generate knowledge of a “quantum leap” character. These abrupt transitions will always merit more focused challenge. They do not have the validating history of established knowledge.

The rival information processing postures of the two groups of professors thus create a critically important system. Acting together they insure that the knowledge being created is worth preserving. In other words, they act to create and maintain society’s store of reliable knowledge.

The existence of the double peaked idea oriented RI is a fact. The difference in the disciplined postures of HA and more spontaneous RI is a fact. Assigning its cause to the tenure system is a testable hypothesis. However, regardless of its cause the existence of the double peak has university level implications for its students.


STUDENT IMPLICATIONS
An unintended consequence of the double peak effect is its effect on the student’s “Rate my Professor” judgment. The ability of a processor to transfer knowledge is governed by the match of the professor and student’s information processing profiles.

Graphic 6
PROFESSOR AND STUDENT STRATEGIC STYLE DISTRIBUTION
(Professor n=254, Student n = 1800)

The student’s profile in Graphic 6 (blue line) is based on a sample of 1,800 graduate and undergraduate students from 31 different colleges and universities. The coefficient of determination (R2 or R squared) is cited on the graph. R2 puts a number on the visual differences. R2 is the proportion of fluctuation (i.e., variance) of one variable that is predictable from the other. Roughly put, R2 is a measure of how well one distribution matches another.

Graphic 5 locates the disjoint between students and professors. The high R2 in the Reactive Stimulator and Hypothetical Analyzers dimensions confirm these will not be a problem. On these dimensions the professor and student are “talking the same language.”

The Logical Processor dimension will create a bit of an annoyance. Students have a stronger desire for a disciplined, step-by-step procedure than does the professor. However, the R2 of 52.3% suggests that negative “Rate my Professor” comments based on this factor will be restrained—more like “grousing” than true complaints.

The RI graphic in the lower right box of Graphic 5 is the major disjoint. The R2 of 33.7% is concentrated on professors holding a position on the second high RI peak. Negative “Rate my Professor” complaints are likely to center on this specific group of professors.

The nature of the “disjoint” is likely to focus on professor’s ability to offer the student an uninterrupted stream of “what causes what and why” logic. The RI style uses fragmented knowledge in its processing. The style is also susceptible to diversion by an interesting comment or speculation. The net result is that the student must try to piece together a consistent “chunk” of knowledge that is understandable in terms of their way of viewing the world—i.e., their strategic profile.

A means of at least partially remedying this condition is readily available. Professors holding a position on the second peak can be taught to align their instruction with the student needs. The professors do not have to change their own strategies. They just have to adopt in their student’s perspective for the period in which they interact.

This is an economical and “doable” option. There is no need to involve all professors. Just those on the second peak. The only intervention needed is some relatively simple instruction. The affected professors only need to know how to suppress a “natural” approach for the duration of a class or meeting. They then need only to know what to substitute for their “natural” approach. This process can realign the strategic style profiles on a temporary basis. That is all that is needed for learning.

The result will not be perfect. But it will be a step forward. The transfer of knowledge will be better served without sacrificing knowledge creation. The only difficulty will be convincing the professors that their way is not the only “right way.” That could be the real challenge.

CONCLUSION
This study finds that all professors share a basic, overall strategic profile that favors new ideas and careful analysis. This commonality lends some justification to the popular characterizations of professors as a bit “out of kilt” with general society.

A double peak was found on the idea generating RI dimension among tenure track professors. Two distinct groups of professors appear to populate the academy. One is well suited to the creation of “paradigm shifting” knowledge. The other is ideal for testing new knowledge. The result of their interaction is reliable knowledge. .

The university’s knowledge creation mission appears to hinge on the tenure system. Recognizing excellence in both knowledge transfer (the lower RI peak) and knowledge creation (the upper RI peak) creates the dual peak. This suggests that tenure is not just useful in motivating individual professors. It is a system selection mechanism. It creates a system of two complementary, contesting components that act together to insure the consistent creation of reliable knowledge.

A corollary of the twin peak tenure hypothesis is that were the tenure system to be replaced, there is a strong probability that the replacement system would not create the twin peak profile. The result could be the compromise of a critical component of the university’s knowledge creation mechanism. Caution is in order.

Finally, nothing comes without a price. The price here is that the upper peak professors will tend to accumulate more negative “Rate My Professor” comments. This testifies to a structurally based compromise of the university’s inter generational knowledge transfer function. However, this can be ameliorated if not resolved through a simple focused educational process.