European Journal of Operational Research 154 (2004) 345–362 www.elsevier.com/locate/dsw Returns to scale in different DEA models Rajiv D. Banker a‚ William W. Cooper b‚ Lawrence M. Seiford c‚ Robert M. Thrall d‚ Joe Zhu e‚* c School of Management‚ The University of Texas at Dallas‚ Richardson‚ TX 75083-0658‚ USA Graduate School of Business‚ The University of Texas at Austin‚ Austin‚ TX 78712-1174‚ USA Department of Industrial and Operations Engineering‚ University of Michigan‚ Ann Arbor‚ MI
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Chapter 7 Survival Models Our final chapter concerns models for the analysis of data which have three main characteristics: (1) the dependent variable or response is the waiting time until the occurrence of a well-defined event‚ (2) observations are censored‚ in the sense that for some units the event of interest has not occurred at the time the data are analyzed‚ and (3) there are predictors or explanatory variables whose effect on the waiting time we wish to assess or control. We start with some
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Leadership Model (LMX & Style Approach) Assignment submission for Post Graduate Certificate Program in Management (PGCPM–3) Name of the Faculty: Prof. Pawan Kumar Singh Subject: Group Behavior in Organization (OB – 2) Submitted by: Srinivasa Kottakota Student ID: S080600000036 CAF ID: 60602080320 [pic] Name of the Centre: NIIT Imperia‚ Hyderabad TABLE OF CONTENTS |S. N. |DESCRIPTION
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the heuristics and biases approach) and the ecological approach advanced by Gigerenzer and others. We make a proposal of how to integrate Simon‟s approach with the main current approaches to decision making. We argue that this would lead to better models of decision making that are more generalizable‚ have higher ecological validity‚ include specification of cognitive processes‚ and provide a better understanding of the interaction between the characteristics of the cognitive system and the contingencies
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SPJIMR ‘2013 Application of Malcolm Baldrige in the current American Industry Term Paper - MQPE Shishir Jindal PGP-12-065 Rajesh P PGP-12-208 Rajesh P PGP-12-208 ABSTRACT The Oxford dictionary describes an organization as “an organized body of people with a particular purpose”. Organizations‚ whether business‚ government or non-profit‚ play an important part in satisfying the complex and changing needs of the society. In doing so‚ organizations bring together their human‚ capital
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Knowledge Management Models Knowledge management literature is plentiful with different understandings of knowledge‚ information and data‚ as ideas and thoughts. As a result many organizations experience lots of expenses on knowledge management technologies‚ which hardly deliver with the expected outcome (Davenport‚ and Prusak‚ 1998). For further explanation‚ Argote et al.‚ (2003) tried to organize Knowledge Management literature by separating it into two parts: Knowledge Management context and
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Appendix 2 A model of cultural influences on teamwork practice - a design team case-study Cristina Chisalita‚ Gerrit C. van der Veer‚ Johan F. Hoorn & Mari Carmen Puerta Melguizo Vrije Universiteit Amsterdam Contact author: Cristina Chisalita Postal address: Faculty of Sciences‚ Division of Mathematics and Computer Science‚ Department of Information Management and Software Engineering‚ Free University‚ De Boelelaan 1081 A‚ 1081 HV Amsterdam‚ The Netherlands
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Number 1 The Gordon Model is particularly useful since it includes the ability to price in the growth rate of dividends over the long term. It is important to remember that the price result of the Constant Dividend Growth Model assumes that the growth rate of the dividends over time will remain constant. This is a difficult assumption to accept in real life conditions‚ but knowing that the result is dependent on the growth rate allows us to conduct sensitivity analysis to test the potential error
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or goods is constant‚ the inventory model is called deterministic. However‚ when the demand rate is not constant and not deterministic‚ the inventory model is called probabilistic and is best described by a probability distribution. The minimum-cost order quantity and re-order policies are based on the assumptions of the demand rate. PROBABILISTIC INVENTORY MODELS 1. A single-period inventory model with probabilistic demand The single-period inventory model refers to inventory situations in which
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CAPM is a model which enables investors to determine the expected return from a risky security. It observes the relationship between the risk of an asset (Mobil Oil) and its return. The model uses Beta as the main measure of risk. This model works under the following situations: • In a perfectively competitive market where they are many price-takers’ investors‚ who have a small market share each. • Investors behaviour is myopic • Also investments included in the model are publicly
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