Academic journal article Academy of Educational Leadership Journal

An Application of Linear Programming in Performance Evaluation

Academic journal article Academy of Educational Leadership Journal

An Application of Linear Programming in Performance Evaluation

Article excerpt

INTRODUCTION

Assessing performance of employees is an important process for achieving excellence. Performance evaluations serve as a basis for many key decisions such as compensation, promotion, and employee development. Organizations that lack effective evaluation systems may experience higher rates of employee dissatisfaction, attrition, and lowered productivity. Universities and colleges, like other organizations, are expected to develop effective systems for evaluating faculty performance. There are some unique challenges to evaluating faculty performance because of the wide-ranging activities in which faculty regularly engage. Never the less, a consensus has emerged on the need to establish valid and reliable systems of assessing faculty performance (Wolfer & Johnson, 2003).

The distribution of responsibilities and allocation of effort to various components of responsibility such as teaching, research, and service affects the ability to perform in each of these components (Ridley & Collins, 2015). It is therefore necessary to present a complete measure of performance while recognizing the relative importance of these various components. Systems of faculty evaluation and reward should recognize differing patterns of productivity in faculty as well as the mission of the institution (Boyer, 1990).

Elmore (2008) suggests that assessment of faculty work should be based on the most empirical and objective means possible. Miller & Seldin (2014) show that academic administrators are under growing pressure to assess faculty performance through formalized, systematic methods and that deans weigh a wide range of factors in the evaluation process. Practices of assigning difference weights to different roles vary widely among institutions and are influenced by numerous institutional characteristics (Centra, 1977).

In many performance evaluation processes, a supervisor assigns scores, subject to a predefined scale, for specified areas of responsibility. An overall score is determined by weighting the scores from the individual areas such that the weights account for 100% of the total effort. For example, a Department Chair assigns scores on a 0.0-4.0 scale to assess a faculty member's teaching effectiveness, student engagement, scholarship, and service. These scores are then weighted, based on the level of importance attached to each area, to obtain an overall evaluation score. In many cases, the faculty has some degree of flexibility in choosing the weights assigned to their areas of responsibility.

Caldwell Jr. & Schulte (2002) describe results of their survey showing various indicators of faculty dissatisfaction with the evaluation process. Indicators of dissatisfaction included uncertainty of specific responsibilities, difficulty in preparing for promotion and tenure because of a lack of consistency in performance evaluations, and a lack of standards for various aspects of their responsibilities. Allowing faculty to choose their own weights, within predefined ranges, gives them more freedom and control and should lead to greater satisfaction with the evaluation process. Arreola (2006) recommends the use of such a dynamic faculty role model rather than a static faculty role model.

Linear Programming (also called Linear Optimization or LP) is the study of methods to achieve an optimal outcome in a linear mathematical model. It uses mathematical techniques to find an optimal value for a linear objective function, subject to linear equality and/or inequality constraints. It was originally developed during the Second World War, mainly by George Dantzig, to optimize the use of limited (i.e. constrained) military resources. A historical treatment of the subject can be found in Lenstra, Rinnooy Kan & Schrijver (1991). However, it has since been extended to a wide variety of business, engineering and scientific applications (Charnes & Cooper, 1961; Gartner & Matousek, 2006; Dantzig &Thapa,1997). …

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