Performance Measurement is an essential discipline for any business. Robust and reliable performance metrics for people, processes, and technologies enable a business to identify and address deficiencies to improve performance and profitability. The complexity of modern operating environments presents real challenges to developing equitable and accurate performance metrics. This thesis explores and develops two new methods to address common challenges encountered in businesses across the world. The first method addresses the challenge of estimating the relative complexity of various tasks by utilizing the Pearson Correlation Coefficient to identify potentially over weighted and under weighted tasks. The second method addresses the challenge of determining performers' influence on a metric by treating performance rankings as vectors and evaluating the change of the vector over multiple performance periods.
","abstract_html":"<p>Performance Measurement is an essential discipline for any business. Robust and reliable performance metrics for people, processes, and technologies enable a business to identify and address deficiencies to improve performance and profitability. The complexity of modern operating environments presents real challenges to developing equitable and accurate performance metrics. This thesis explores and develops two new methods to address common challenges encountered in businesses across the world. The first method addresses the challenge of estimating the relative complexity of various tasks by utilizing the Pearson Correlation Coefficient to identify potentially over weighted and under weighted tasks. The second method addresses the challenge of determining performers' influence on a metric by treating performance rankings as vectors and evaluating the change of the vector over multiple performance periods.</p>","abstract_has_math":false,"creators":["Benis, Jordan David"],"institution":null,"degree_name":"MS","degree_level":"Immediate Access","degree_discipline":"Computational Mathematics","degree_department":null,"school":null,"contributors":["John Kern","Frank D'Amico"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2018,"date_issued":"2018-05-11T07:00:00Z","date_published":"2018-05-11T07:00:00Z","updated_at":"2026-07-24T02:10:43Z","subjects":["Performance Management","Performance Measurement","Performance Metrics","Performance","Employee Performance","Computational Engineering","Mathematics","Operations and Supply Chain Management","Statistics and Probability"],"languages":["English"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://dsc.duq.edu/etd/1427","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["John Kern","Frank D'Amico"]},{"key":"dc:creator","label":"Author","values":["Benis, Jordan David"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2018-05-11T07:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computational Mathematics"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Immediate Access"]},{"key":"thesis:degree_name","label":"Degree Name","values":["MS"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Performance Management","Performance Measurement","Performance Metrics","Performance","Employee Performance","Computational Engineering","Mathematics","Operations and Supply Chain Management","Statistics and Probability"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["English"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://dsc.duq.edu/etd/1427"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Performance Measurement is an essential discipline for any business. Robust and reliable performance metrics for people, processes, and technologies enable a business to identify and address deficiencies to improve performance and profitability. The complexity of modern operating environments presents real challenges to developing equitable and accurate performance metrics. This thesis explores and develops two new methods to address common challenges encountered in businesses across the world. The first method addresses the challenge of estimating the relative complexity of various tasks by utilizing the Pearson Correlation Coefficient to identify potentially over weighted and under weighted tasks. The second method addresses the challenge of determining performers' influence on a metric by treating performance rankings as vectors and evaluating the change of the vector over multiple performance periods.
"]},{"key":"dc:title","label":"Title","values":["Re-Evaluating Performance Measurement: New Mathematical Methods to Address Common Performance Measurement Challenges"]}]}],"canonical_facts":{"dc:contributor":["John Kern","Frank D'Amico"],"dc:creator":["Benis, Jordan David"],"dc:date.available":["2018-05-11T07:00:00Z"],"dc:description.abstract":["Performance Measurement is an essential discipline for any business. Robust and reliable performance metrics for people, processes, and technologies enable a business to identify and address deficiencies to improve performance and profitability. The complexity of modern operating environments presents real challenges to developing equitable and accurate performance metrics. This thesis explores and develops two new methods to address common challenges encountered in businesses across the world. The first method addresses the challenge of estimating the relative complexity of various tasks by utilizing the Pearson Correlation Coefficient to identify potentially over weighted and under weighted tasks. The second method addresses the challenge of determining performers' influence on a metric by treating performance rankings as vectors and evaluating the change of the vector over multiple performance periods.
"],"dc:identifier":["https://dsc.duq.edu/etd/1427"],"dc:language":["English"],"dc:subject":["Performance Management","Performance Measurement","Performance Metrics","Performance","Employee Performance","Computational Engineering","Mathematics","Operations and Supply Chain Management","Statistics and Probability"],"dc:title":["Re-Evaluating Performance Measurement: New Mathematical Methods to Address Common Performance Measurement Challenges"],"thesis:degree_discipline":["Computational Mathematics"],"thesis:degree_level":["Immediate Access"],"thesis:degree_name":["MS"]},"updated_at":"2026-07-24T02:10:43Z"}