Relevant bibliographies by topics / Performance measures

Academic literature on the topic 'Performance measures'

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Published: 4 June 2021
Last updated: 25 May 2024

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Journal articles on the topic "Performance measures"

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Pandey, Manoj, and Rajesh Kr Dubey. "Performance Measures in Supply Chain Management." Indian Journal of Applied Research 4, no. 2 (October 1, 2011): 14–17. http://dx.doi.org/10.15373/2249555x/feb2014/91.

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Kelley, Patricia M. "Performance Measures:." Journal of Library Administration 14, no. 2 (May 7, 1991): 21–36. http://dx.doi.org/10.1300/j111v14n02_03.

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O??LEARY, DENNIS S. "Performance Measures." Medical Care 33, Supplement (January 1995): JS18. http://dx.doi.org/10.1097/00005650-199501001-00003.

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Thompson, Betsy L., and Jeffrey R. Harris. "Performance measures." American Journal of Preventive Medicine 20, no. 4 (May 2001): 291–93. http://dx.doi.org/10.1016/s0749-3797(01)00294-x.

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Starbuck, William H. "Performance Measures." Journal of Management Inquiry 14, no. 3 (September 2005): 280–86. http://dx.doi.org/10.1177/1056492605279099.

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Baghai, Mehrdad, Lar Bradshaw, Stephen Coley, and David White. "PERFORMANCE MEASURES:." Journal of Business Strategy 20, no. 4 (April 1999): 17–21. http://dx.doi.org/10.1108/eb040013.

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Redberg, Rita F. "Performance Measures." JAMA Internal Medicine 173, no. 16 (September 9, 2013): 1549. http://dx.doi.org/10.1001/jamainternmed.2013.7769.

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Park, Sang-June, and Youjae Yi. "A composite measure of performance–expectation and performance-only measures." Service Industries Journal 37, no. 15-16 (September 2017): 936–47. http://dx.doi.org/10.1080/02642069.2017.1369968.

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Behn, Robert D. "Why Measure Performance? Different Purposes Require Different Measures." Public Administration Review 63, no. 5 (September 2003): 586–606. http://dx.doi.org/10.1111/1540-6210.00322.

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Lemmer, Hoffie. "Cricket performance measures." Suid-Afrikaanse Tydskrif vir Natuurwetenskap en Tegnologie 28, no. 1 (September 2, 2009): 13–34. http://dx.doi.org/10.4102/satnt.v28i1.46.

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The purpose of this paper is to present a summary of batting and bowling performance measures that have been developed over a period of eight years. The measure of batting performance (BP) consists of three components. Firstly, an exponentially weighted average (EWA) is calculated in which recent scores have higher weights than scores further back in time in order to get a measure that adequately reflects the batsman’s present form. EWA is then adjusted up- or downwards depending on the batsman’s consistency and strike rate. This measure was subsequently generalized to BPW which includes weights for runs scored against specific countries in home or away matches separately. In the case of bowling, the first challenge was to develop a single comprehensive measure of bowling performance, the combined bowling rate (CBR) for limited overs matches and the dynamic bowling rate (DBR) for unlimited overs matches. Hence, by alsotaking bowling consistency into account, the current bowling performance measure (CBP) wasdeveloped. This measure was also extended to include weights for wickets taken and runs concededagainst each international team in home or away matches. These measures contain parametersthat were based on players’ scores up to the specific date of calculation. Before using the latestavailable data for the present presentation, it is logical to update these parameters. This hasbeen done in the present study. If a player performs well at local level, it is important to be ableto compare his performance with those of players already in the national team. Weights havebeen determined for runs scored by batsmen and also for runs conceded and wickets taken bybowlers in local matches, whereby the performances of players can be compared irrespective ofthe proportion of international matches played by each player. After having used the measures onvarious occasions the need arose to revise some of them slightly. The updated measures are thenused to give rankings of South African batsmen and bowlers in First Class and List A matchesalike. Methods have also been developed to compare the batting and bowling performances ofplayers after a short series of matches. It was shown that the traditional average could haveunrealistic values in the case of a batsman who had a large percentage of not out scores in ashort series. Alternative measures have been developed and shown to give much more realisticvalues. In the case of bowling it is important not only to count the number of wickets a bowlerhas taken, but to distinguish between the wickets of top and middle order batsmen on the onehand and lower order batsmen on the other. A system has been established whereby weights areattached to the wickets according to the batting position of the batsman. Instead of just countingthe number of wickets taken, the sum of the weights of the wickets taken by the bowler is usedin the calculation of CBR* or DBR*. Finally, the latest form of the game, Twenty20 cricket, isalso discussed.
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Dissertations / Theses on the topic "Performance measures"

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Chibba, Aron. "Measuring supply chain performance : prioritizing performance measures /." Luleå : Luleå University of Technology, 2007. http://epubl.luth.se/1402-1757/2007/49.

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Stec, David J. (David Joseph) 1968. "Performance measures for lean manufacturing." Thesis, Massachusetts Institute of Technology, 1998. http://hdl.handle.net/1721.1/9887.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering; and, (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management, 1998.
Includes bibliographical references (p. 83-84).
by David J. Stec.
M.B.A.
S.M.
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Chirico, Kristina Eva Lewis Philip M. "Predicting objective measures of performance." Auburn, Ala., 2005. http://hdl.handle.net/10415/1286.

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Huang, Rong 1973. "Exploration of brand equity measures : linking customer mind-set measure to product-market performance measure." Thesis, McGill University, 2008. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=115606.

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Motivation: Various brand equity measures have been proposed in extant literature. Few researches have explored the theoretical similarities, differences and relationship between different brand equity measures. In the thesis, I will explore two types of brand equity measures, namely customer mind-set measures and product-market performance measures. In particular, I will look at: 1) the correlation between the two types of measures; 2) which measure reflects the underlying brand equity construct better; 3) the impacts of marketing mix elements on the two types measures respectively; and 4) the prediction relationship between customer mind-set measure and product-market performance measure.
Theoretical Framework: My main theoretical framework will be brand equity literature. I draw upon the Brand Value Chain framework (Keller & Lehmann, 2(03) to explore the conceptual differences between two types of brand equity measurements, namely customer mind-set and product-market performance measurements. Furthermore, I also use Keller's (1993) Customer-Based Brand Equity concept to explore how specific marketing activities impact the brand equity theoretically.
Data and Methodology: This thesis measures brand equity by two methods: customer mind-set (Keller, 1993) and revenue premium (Ailawadi, Lehmann, & Neslin, 2(03). I use two types of data in the empirical analyses. Survey data, procured from a consumer-packaged product company 1, is used to measure customer mind-set brand equity. This unique data consists of proprietary equity scan surveys on 11 brands from 2004 -- 2006 in the United States. The measurement model of brand equity is rooted in Keller's customer-based brand equity concept (1993). The measurements include brand awareness, brand performance, brand image, brand judgment, brand feelings and brand resonance (Keller, 2(01). The second data is from commercial sources, including IRI and TNS, for the specific brands and time periods corresponding to the survey data and includes revenue premium, price, sales, distribution, promotion, and advertising information. Various techniques are employed for analyses including descriptive and reliability analyses, correlation analyses, multiple-regression, and cross-validation.
Contribution: The contribution ofthis thesis is threefold. Firstly, it sheds light on the underlying theory and relationship between two types of brand equity measurements and provides empirical test of the theory. Secondly, it provides a systematic exploration of the impact of marketing mix elements on brand equity using real market data and two different measurements. Third, it offers very practical guidance for managers on how to choose a specific brand equity measures and how to track the brand equity measures over time for their brands.
1Because a confidential agreement with this company, the company name, brands name as well as product category will be disguised in the thesis.
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Jivsäter, Andreas, and Marcus Martinsson. "A comparison of management performance measures." Thesis, Högskolan i Jönköping, Internationella Handelshögskolan, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-21644.

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Eltemsahi, Ayman. "A systemic approach to performance measures." Thesis, University of Sunderland, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.341433.

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Young, Charles William. "Measures of environmental performance in business." Thesis, University of Huddersfield, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.285841.

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Erasmus, Petrus Daniel. "Evaluating value based financial performance measures." Thesis, Stellenbosch : University of Stellenbosch, 2008. http://hdl.handle.net/10019.1/1407.

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Thesis (PhD (Economics))--University of Stellenbosch, 2008.
The primary financial objective of a firm is the maximisation of its shareholders’ value. A problem faced by the shareholders of a firm is that it is difficult to determine the effect of management decisions on the future share returns of the firm. Furthermore, it may be necessary to implement certain monitoring costs to ensure that management is focused on achieving this objective. A firm would, therefore, benefit from being able to identify those financial performance measures that are able to link the financial performance of the firm to its share returns. Implementing such a financial performance measure in the valuation and reward systems of a firm should ensure that management is aligned with the objective of shareholder value maximisation, and rewarded for achieving it. A large number of traditional financial performance measures have been developed. These measures are often criticised for excluding a firm’s cost of capital, and are considered inappropriate to be used when evaluating value creation. Furthermore, it is argued that these measures are based on accounting information, which could be distorted by Generally Accepted Accounting Practice (GAAP). Studies investigating the relationship between these measures and share returns also provide conflicting results. As a result of the perceived limitations of traditional measures, value based financial performance measures were developed. The major difference between the traditional and value based measures is that the value based measures include a firm’s cost of capital in their calculation. They also attempt to remove some of the accounting distortions resulting from GAAP. Proponents of the value based measures present these measures as a major improvement over the traditional financial performance measures and report high levels of correlation between the measures and share returns. A number of studies containing contradictory results have been published. On the basis of these conflicting results it is not clear whether the value based measures are able to outperform the traditional financial performance measures in explaining share returns. The primary objectives of this study are thus to: • Determine the relationship between the traditional measures earnings before extraordinary items (EBEI) and cash from operations (CFO), and shareholder value creation; • Investigate the value based measures residual income (RI), economic value added (EVA), cash value added (CVA) and cash flow return on investments (CFROI), and to determine their relationship with the creation of shareholder value; • Evaluate the incremental information content of the value based measures above the traditional measures. The information content of the traditional measures and the value based measures are evaluated by employing an approach developed by Biddle, Bowen and Wallace (1997). The first phase of this approach entails the evaluation of the relative information content of the various measures in order to determine which measure explains the largest portion of a firm’s market-adjusted share returns. The second phase consists of an evaluation of the incremental information content of the components of a measure in order to determine whether the inclusion of an additional component contributes statistically significant additional information beyond that contained in the other components. The study is conducted for South African industrial firms listed on the Johannesburg Securities Exchange for the period 1991 to 2005. The data required to calculate the measures investigated in the study are obtained from the McGregor BFA database. This database contains annual standardised financial statements for listed and delisted South African firms. It also contains EVA, cost of capital and invested capital amounts for those firms listed at the end of the research period. Including only these listed firms in the research sample would expose the study to a survivorship bias. Hence these values are estimated for those firms that delisted during the period under review by employing a similar approach to the one used in the database. The resulting sample consists of 364 firms providing 3181 complete observations. Since different information is required to calculate the various measures included in the study, different samples are compiled from this initial sample and included in the tests conducted to evaluate the information content of the measures. The results of this study indicate that the value based measures are not able to outperform EBEI in the majority of the relative information content tests. Furthermore, the measures EVA, CVA and CFROI are also not able to outperform the relatively simple value based measure RI. The results from the incremental information content tests indicate that although some of the components of the value based measures provide statistically significant incremental information content, the level of significance for these relatively complex adjustments is generally low. Based on these results, the claims made by the proponents of the value based measures cannot be supported. Furthermore, if a firm intends to incorporate its cost of capital in its financial performance measures, the measure RI provides most of the benefits contained in the other more complex value based measures.
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Yuksel, Hasan Zafer. "Performance measures: Traditional versus new models." CSUSB ScholarWorks, 2006. https://scholarworks.lib.csusb.edu/etd-project/3086.

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The thesis analyzed the performance of 5,987 mutual funds using a database called Steele Mutual Fund Experts and compared the predicting ability of various measures of performance. The measures discussed in the thesis are Treynor Ratio, Sharpe Ratio, Jensen's Alpha, Graham-Harvey-1 (GH-1), and Graham-Harvey-2 (GH-2). The performance measures are mostly used by professional money managers and scholars for literary purposes.
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Krishnapillai, Alagesan. "Understanding key performance indicators through driver measures." Thesis, University of Ottawa (Canada), 2009. http://hdl.handle.net/10393/28321.

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Performance management is a vital part of every organization for its success and long term survival in the current competitive global market place. An organization needs a sound performance management framework to analyze its data to make strategic and tactical decisions. The weaknesses in today's performance management frameworks are linked to their inability to integrate strategy formulation with implementation. In our thesis, we introduce D river Measure Models that can define cause-and-effect relationship between d river measures and Key Performance Indicators (KPIs) for effective performance management and strategic alignment. Driver Measure Models make the performance management more dynamic as the operational activities are linked to strategies. Another contribution of the thesis is the identification of mathematical techniques to quantify relationships between KPIs and driver measures. Thesis makes an effort to show how mathematical techniques can be used for planning and forecasting outcomes while changing strategies. After conducting analysis using the mathematical techniques, organization can arrive at a set of operational tasks associated to driver measures which need to be executed to achieve its optimal result. Finally, we identified the essential set of criteria that a performance management framework needs to address through a literature survey and a gap analysis of existing frameworks. We created an extension to the Balanced Scorecard framework based on Driver Measure Models and support for the management of external factors to address these criteria and compared it to existing frameworks using a case study.
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Books on the topic "Performance measures"

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Division, United States General Accounting Office Accounting and Financial Management. IRS performance measures. Washington, D.C: The Office, 1992.

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United States. General Accounting Office. Accounting and Financial Management Division, ed. IRS performance measures. Washington, D.C: The Office, 1992.

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Maskell, Brian H. New performance measures. Portland, Or: Productivity Press, 1994.

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United States. General Accounting Office. Accounting and Financial Management Division., ed. Customs performance measures. Washington, D.C: The Office, 1993.

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Eikelmann, Nils. Value Based Performance Measures. Wiesbaden: Springer Fachmedien Wiesbaden, 2020. http://dx.doi.org/10.1007/978-3-658-31429-3.

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Garry, Eileen M. Performance measures: What works? [Washington, DC]: U.S. Dept. of Justice, Office of Justice Programs, Office of Juvenile Justice and Delinquency Prevention, 1997.

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Accreditation Council on Services for People with Disabilities. Outcome based performance measures. Landover, MD: Accreditation Council on Services for People with Disabilities, 1993.

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Human performance measures handbook. Mahwah, N.J: Lawrence Erlbaum Associates, 2000.

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Oregon. Fiscal Policy Analysis Division., ed. Performance measures reference guide. Salem, OR: Dept. of Administrative Services, Fiscal Policy Analysis Division, 1994.

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Turnbull, Katherine F. Developing Freight Fluidity Performance Measures. Washington, D.C.: Transportation Research Board, 2014. http://dx.doi.org/10.17226/22266.

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Book chapters on the topic "Performance measures"

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Bogetoft, Peter. "Performance Measures." In Performance Benchmarking, 23–47. Boston, MA: Springer US, 2012. http://dx.doi.org/10.1007/978-1-4614-6043-5_2.

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Goddard, Stephen. "Performance Measures." In Feed Management in Intensive Aquaculture, 159–79. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-5861-3_9.

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Fernández, Alberto, Salvador García, Mikel Galar, Ronaldo C. Prati, Bartosz Krawczyk, and Francisco Herrera. "Performance Measures." In Learning from Imbalanced Data Sets, 47–61. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-98074-4_3.

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McGahan, Anita M. "Performance Measures." In The Palgrave Encyclopedia of Strategic Management, 1276–81. London: Palgrave Macmillan UK, 2018. http://dx.doi.org/10.1057/978-1-137-00772-8_218.

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Seifert, Werner G., Ann-Kristin Achleitner, Frank Mattern, Clara C. Streit, and Hans-Joachim Voth. "Performance Measures." In European Capital Markets, 8–59. London: Palgrave Macmillan UK, 2000. http://dx.doi.org/10.1057/9780230287068_2.

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Goddard, Stephen. "Performance Measures." In Feed Management in Intensive Aquaculture, 159–79. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-1-4613-1173-7_9.

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Jang, Jihyeon, and Hale Kim. "Performance Measures." In Encyclopedia of Biometrics, 1062–68. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-73003-5_111.

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McGahan, Anita M. "Performance Measures." In The Palgrave Encyclopedia of Strategic Management, 1–6. London: Palgrave Macmillan UK, 2016. http://dx.doi.org/10.1057/978-1-349-94848-2_218-1.

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DeAngelis, Joseph P. "Performance Measures." In Quality Improvement and Patient Safety in Orthopaedic Surgery, 127–32. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-07105-8_12.

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Khandelwal, Dhruv. "Performance Measures." In Automating Data-Driven Modelling of Dynamical Systems, 99–124. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-90343-5_5.

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Conference papers on the topic "Performance measures"

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Li, Howell. "Performance Measures without Communication." In Automated Traffic Signal Performance Measure Workshop. Purdue University, 2016. http://dx.doi.org/10.5703/1288284316043.

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Finger, George, and Carey McClesky. "Spaceport Performance Measures." In SpaceOps 2010 Conference: Delivering on the Dream (Hosted by NASA Marshall Space Flight Center and Organized by AIAA). Reston, Virigina: American Institute of Aeronautics and Astronautics, 2010. http://dx.doi.org/10.2514/6.2010-2148.

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Bullock, Darcy. "Traffic Signal Performance Measures Workshop." In Automated Traffic Signal Performance Measure Workshop. Purdue University, 2016. http://dx.doi.org/10.5703/1288284316016.

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Garrett, Christopher, Daniel Levack, and Russel Rhodes. "Using Technical Performance Measures." In 47th AIAA/ASME/SAE/ASEE Joint Propulsion Conference & Exhibit. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2011. http://dx.doi.org/10.2514/6.2011-5500.

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Huang, Hui-Min, and Seungbin Moon. "Session details: Measures & metrics." In PerMIS '10: Performance Metrics for Intelligent Systems. New York, NY, USA: ACM, 2010. http://dx.doi.org/10.1145/3260126.

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Tian, Zong. "Signal System Performance Measures Based on Conventional Travel Run Data." In Automated Traffic Signal Performance Measure Workshop. Purdue University, 2016. http://dx.doi.org/10.5703/1288284316036.

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Clayton, Rob. "Automated Traffic Signal Performance Measures, AASHTO Innovation Initiative 2013 Focus Technology." In Automated Traffic Signal Performance Measure Workshop. Purdue University, 2016. http://dx.doi.org/10.5703/1288284316018.

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Freije, Richard, Alexander Hainen, Amanda Stevens, Howell Li, W. Benjamin Smith, Hayley Summers, Christopher Day, James Sturdevant, and Darcy Bullock. "Graphical Performance Measures for Practitioners to Triage Split Failure Trouble Calls." In Automated Traffic Signal Performance Measure Workshop. Purdue University, 2016. http://dx.doi.org/10.5703/1288284316047.

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Impagliazzo, John. "Graduate attributes and performance measures." In the 14th annual ACM SIGCSE conference. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1562877.1563012.

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Liston, Richard, Sridhar Srinivasan, and Ellen Zegura. "Diversity in DNS performance measures." In the second ACM SIGCOMM Workshop. New York, New York, USA: ACM Press, 2002. http://dx.doi.org/10.1145/637201.637204.

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Reports on the topic "Performance measures"

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Elder, B. L., Linda A. Hansen, and Donald L. Harville. Examining Operational Measures of Performance: Performance Measures Matrix. Fort Belvoir, VA: Defense Technical Information Center, June 1992. http://dx.doi.org/10.21236/ada251896.

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Layton, Richard L., and Phillip Feld. Part-Task Performance Measures. Fort Belvoir, VA: Defense Technical Information Center, January 1996. http://dx.doi.org/10.21236/ada307049.

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Young, Stanley, and Dennis So Ting Fong. Arterial Performance Measures Software. Purdue University, December 2017. http://dx.doi.org/10.5703/1288284316567.

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McMullen, B., and Christopher Monsere. Freight Performance Measures: Approach Analysis. Portland State University Library, May 2010. http://dx.doi.org/10.15760/trec.148.

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Meystel, Alexander, Jim Albus, Elena Messina, and Dennis Leedom. Performance measures for intelligent systems :. Gaithersburg, MD: National Institute of Standards and Technology, 2003. http://dx.doi.org/10.6028/nist.sp.1014.

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Zoltowski, Michael D. Analysis of Statistical Performance Measures. Fort Belvoir, VA: Defense Technical Information Center, December 2002. http://dx.doi.org/10.21236/ada414936.

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Zoltowski, Michael D. Analysis of Statistical Performance Measures. Fort Belvoir, VA: Defense Technical Information Center, May 2004. http://dx.doi.org/10.21236/ada422819.

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Young, Stanley, and Dennis So Ting Fong. Arterial Network Performance Measures Software. Purdue University, December 2017. http://dx.doi.org/10.5703/1288284316570.

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Sterrett, A., and M. Minei. Performance Measures of the ADA Rendezvous. Fort Belvoir, VA: Defense Technical Information Center, March 1992. http://dx.doi.org/10.21236/ada250060.

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Weiss, Howard M. Evaluation of Recruiter Performance Measures and Policy. Fort Belvoir, VA: Defense Technical Information Center, July 1988. http://dx.doi.org/10.21236/ada201344.

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