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Measuring Relative Efficiency and Effectiveness
| Content Provider | Semantic Scholar |
|---|---|
| Author | Lengacher, David Cammarata, Craig Lloyd, Shannon M. |
| Copyright Year | 2014 |
| Abstract | The performance of an organization is a function of its efficiency and effectiveness. Efficiency is the degree to which inputs are used to produce outputs. Effectiveness is the degree to which organizational goals are met. Efficiency provides a starting point for our discussion, as it continues to be an active area of research. Efficiency is defined as the ratio of total output to total input. However, in the real world, outputs and inputs are rarely measured in the same units. Therefore, one cannot characterize efficiency by simply dividing the sum of outputs by the sum of inputs. One way to address this problem is to assign weights to each output and input. Weights can be determined using a variety of approaches including cost-accounting information and subject matter expertise. By assigning fixed weights, whether equal or based on relative importance, an efficient frontier is created a priori. In other words, the efficient frontier and all isoquants (contour lines) are not determined by the data, but rather by subjective opinion. Data Envelopment Analysis (DEA) provides an alternative to these fixed weight approaches and is data-driven. DEA is an established nonparametric approach for estimating the relative efficiency of peer entities called decision making units (DMUs). In practice, DMUs can represent a wide variety of entities including countries, economic sectors, business units, organizations, institutions, projects, processes, products, and policies. In all cases, DMUs may use multiple inputs to produce multiple outputs. DEA has been applied in many sectors including Energy, Education, Healthcare, and Finance. See Galterio et al. (2009) and Paradi et al. (2011) for examples. Effectiveness differs from efficiency in that it focuses solely on outputs. Simply stated, effectiveness is the degree to which results are achieved. Fortunately, DEA can also be used to measure the relative effectiveness of DMUs by simply using a vector of 1s in place of all inputs (Chang et al., 1995; Tsai & Huang, 2011). The frontier DEA determines from this modified data set is the effectiveness (possibility) frontier. Finally, once measures of efficiency and effectiveness are computed, they can be used together to characterize total performance. In fact, some researchers have explicitly defined total performance as a function of both measures. See Eriksson et al. (2007), Fugate et al. (2011), and Tucker and Hargreaves (2008) for examples. However, this raises important questions. For instance, what is the appropriate mathematical model to combine these measures? Although each case is unique, we provide guidelines for measuring the total performance of mutually exclusive alternatives as well as portfolios. We also discuss a method for measuring total organizational performance over time. The objective of this chapter is to provide an overview of DEA, making it more accessible for David Lengacher Concurrent Technologies Corporation, USA |
| Starting Page | 1529 |
| Ending Page | 1538 |
| Page Count | 10 |
| File Format | PDF HTM / HTML |
| DOI | 10.4018/978-1-4666-5202-6.ch138 |
| Alternate Webpage(s) | https://www.igi-global.com/viewtitlesample.aspx?id=107346&ptid=90651&t=measuring+relative+efficiency+and+effectiveness |
| Alternate Webpage(s) | http://www.igi-global.com/viewtitlesample.aspx?id=107346&ptid=90651&t=measuring+relative+efficiency+and+effectiveness |
| Alternate Webpage(s) | https://doi.org/10.4018/978-1-4666-5202-6.ch138 |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
