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Every day, more attention is paid to the management of human resources to achieve organizational performance improvement. In consequently new approaches and tools that integrate competency management are used more frequently. However, it still lacks important aspects such as strategic alignment of performance and use of mathematical techniques as Rating and Weighted sum, that reduce the subjectivity of human capital processes. In response to this need is decided to make a proposal to decrease the lack in the process of performance evaluation, that is why is traced as general objective to redesign the process of performance evaluation supported in the use of quantitative techniques. In this way it is given a model that supports the evaluation on performance indicators aligned with the scorecard of the organization. [ABSTRACT FROM AUTHOR]

The purpose of this study was to examine employees’ reactions to the use of organizational citizenship behavior (OCB) in performance evaluations. In addition, gender differences in such reactions were examined. Data were obtained from a sample of working adults ( n = 78) and a sample of students ( n = 249). In the first study, participants compared the fairness of 11 different weighting combinations of OCB and core task behavior, using a within-subjects design. In the second study, low, medium, and high weightings of OCB were compared using a between-subjects design. In both studies, participants reported that evaluating employees on OCB was fair. OCB weightings of 30– 50% were perceived as the most fair. Men felt that OCB weighting of 20–30% were the most fair and women felt that OCB weightings of 25–50% were the most fair. Considering that employees are evaluated on their OCB, it is important to know that they feel that it is fair to do so. Choosing how heavily to weigh OCB may be more difficult, although weightings of 25–30% OCB were perceived to be fair to both the men and women in this research. This is the first study to examine employee reactions to the use of OCB in performance evaluations and add to a growing body of evidence suggesting that there are gender differences in the perceptions of OCB. [ABSTRACT FROM AUTHOR]

We isolate and describe four key elements that distinguish different forms of forced distribution systems (FDS). These key elements are the consequences for low performers, differentiation of rewards for top performers, frequency of feedback, and comparison group size. We examine how these elements influence respondents’ attraction to FDS. Undergraduate students ( n = 163) completed a policy capturing study designed to determine how these four FDS elements influence their attraction to FDS. We examine the relative importance of these elements that most influence attraction to different FDS, as well as individual attributes (i.e., cognitive ability, gender, and major) that may affect those preferences. Respondents were most attracted to systems with less stringent treatment of low performers, high differentiation of rewards, frequent feedback and large comparison groups. Consequences for low performers were nearly twice as influential as any other element. Respondents with higher cognitive ability favored high reward differentiation and males were less affected by stringent consequences for low performers. Before practitioners implement FDS, it would be prudent to consider all four elements examined in this study—with the treatment of low performers being the most salient issue. Future accounts of FDS should clarify the nature of these elements when reporting on FDS. Such precision will be useful in generating a knowledge base on FDS. We add precision to the discussion of FDS by identifying four key elements. This is one of the first studies to examine perceptions of FDS from a ratee perspective. [ABSTRACT FROM AUTHOR]

Most companies use multiple measures to evaluate employee, departmental, or divisional performance. Aggregating multiple measures is subjective and can lead to employee dissatisfaction and game playing. Now there is TOPS (Total Optimal Performance Scores), which uses an objective, mathematical way to aggregate multiple performance measures into a single performance score. [ABSTRACT FROM PUBLISHER]