A survey and framework to determine which administrative duties should be centralized rather than decentralized
A set of algorithms and metrics that help tertiary institutions restructure effectively, continually, and seamlessly
The triple S model
A survey and framework to determine which administrative duties should be centralized rather than decentralized
Outline of the problem
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When tertiary institutions restructure the organization, they need to decide which administrative duties, such as clerical tasks and personnel management, should be centralized. That is, tertiary institutions need to ascertain which duties should be assigned to a body that serves the entire university and which duties should be assigned to specific faculties or departments.
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To reach or to justify these decisions, managers often refer to imprecise goals—such as the objective to prevent duplication, to reduce overheads, to promote synergies, and to improve flexibility.
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Yet, these goals seldom help institutions decide precisely which administrative duties should be centralized.
Outline of a solution
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To resolve this concern, tertiary institutions should distribute a survey to administrative staff including directors, deans, and other managers.
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The survey should list the administrative duties of each staff member, perhaps as defined by their position description
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In response to each duty, staff members should first estimate, on a scale from 1 to 5, the extent to which the capacity of individuals to fulfill this responsibility effectively continues to improve with time and experience on this task.
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Next, staff members should estimate the extent to which the capacity of individuals to fulfill this responsibility effectively continues to improve with time and experience in a particular field of research or field of education.
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Finally, staff members should estimate the degree to which they can fulfill this responsibility more efficiently if they assist many, rather than only a limited number, of individuals on this activity.
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These questions measure three features of this administrative duty, called skill, scale, and specificity respectively, called the triple S.
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Institutions can then subject these responses to an algorithm that predicts whether an administrative duty benefits from centralization.
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Specifically, if performance on a duty benefits from experience on this task but not experience in a particular field of research or education—and staff can fulfill this responsibility more efficiently if they assist many individuals on this activity—this duty should generally be centralized. That is, one person or team should fulfil this duty across the institution.
Structured restructures
A set of algorithms and metrics that help tertiary institutions restructure effectively, continually, and seamlessly
Outline of the problem
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Tertiary institutions often need to reconfigure their faculties, colleges, schools, departments, or other academic units.
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Unfortunately, these restructures tend to amplify stress and diminish motivation, even in staff whose positions and duties remain intact.
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And the notion that restructures can enhance the financial position of institutions has received only scant evidence.
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These concerns may partly be ascribed to the observation that institutions seldom apply metrics or algorithms to guide and to justify these decisions.
Outline of a solution
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Tertiary institutions should regularly apply several key algorithms. The first algorithm applies natural language processing, such as n-grams, to identify clusters of academic staff whose teaching interests overlap and could thus collaborate effectively if members of one school or unit.
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In particular, this algorithm minimizes the variability of teaching interests within clusters and maximizes the variability of teaching interests across clusters; teaching interests can be derived from past teaching materials.
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The second algorithm is similar but identifies clusters of academic staff whose research interests overlap.
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The third algorithm identifies clusters of academic staff whose research interests overlap but teaching interests diverge—to enable individuals to solve the same problems with diverse perspectives. Specifically, this algorithm minimizes the variability of research interests, but maximizes the variability of teaching interests, within clusters. These clusters may generate innovative schools or units.
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The fourth algorithm identifies clusters of academic staff whose research interests overlap both with each other and with recent trends in research. These trends can be derived from publications on this topic or from analyses of changes in the content of research papers over the last five years. This algorithm may generate productive research centers.
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In short, if complemented by appropriate discussions with staff and judgment from managers, these algorithms and similar calculations can inform decisions about restructures.
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Indeed, this information may enable tertiary institutions to adjust the academic units, such as schools and centers, continuously but incrementally— ultimately to afford flexibility but without the disruption and costs associated with wholesale changes
Contributors
To seek advice or engage specialists on these initiatives, contact the contributors of this page
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The triple S model
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Structured restructureS