Language : English
Published : 2010-04-28
Pages : 247
Winning in Emerging Markets
About the Book : – The best way to select emerging markets to exploit is to evaluate their size or growth potential, right? Not according to Tarun Khanna and Krishna Palepu. In Winning in Emerging Markets, these leading scholars on the subject present a decidedly different framework for making this crucial choice. The authors argue that the primary exploitable characteristic of emerging markets is the lack of institutions (credit card systems, intellectual property adjudication, data research firms) that facilitate efficient business operations. While such institutional voidspresent challenges, they also provide major opportunities for multinationals and local contenders. Khanna and Palepu provide a playbook for assessing emerging markets potential and for crafting strategies for succeeding in those markets. They explain how to: Spot institutional voids in developing economies, including in product, labour, and capital markets, as well as social and political systems Identify opportunities to fill those voids, for example, by building or improving market institutions yourself Exploit those opportunities through a rigorous five-phase process, including studying the market over time and acquiring new capabilities Packed with vivid examples and practical toolkits, Winning in Emerging Markets is a crucial resource for any company seeking to define and execute business strategy in developing economies. About the Authors : – Tarun Khanna is the Jorge Paulo Lemann Professor at Harvard Business School and the author of Billions of Entrepreneurs: How China and India Are Reshaping Their Future and Yours. Krishna Palepu is the Ross Graham Walker Professor of Business Administration and senior associate dean for international development at the Harvard Business School.
The new edition of Essentials of Business Statistics delivers clear and understandable explanations of core business statistics concepts, making it ideal for a one-term course in business statistics. The author team emphasize the importance of interpreting statistical results to make effective decisions to improve business processes. The text offers real applications of statistics that are relevant to today’s business students which can be seen in the continuing case studies throughout the book. Continuing cases span throughout a chapter or even groups of chapters, easing students into new topic areas.
About the Author
Bruce L. Bowerman is a professor of decision sciences at Miami University in Oxford, Ohio. He received his Ph.D. degree in statistics from Iowa State University in 1974, and he has over 37 years of experience teaching basic statistics, regression analysis, time series forecasting, survey sampling, and design of experiments to both undergraduate and graduate students. In 1987 Professor Bowerman received an Outstanding Teaching award from the Miami University senior class, and in 1992 he received and Effective Educator award from the Richard T. Farmer School of Business Administration. Together with Richard T. O’Connell, Professor Bowerman has written 11 textbooks. These include Forecasting and Time Series: An Applied Approach and Forecasting, Time Series, and Regression: An Applied Approach (also coauthored with Anne B. Koehler); The first edition of Forecasting and Time Series earned an Outstanding Academic Book award from Choice magazine.
Richard T. O’Connell is an associate professor of decision sciences at Miami University in Oxford, Ohio. He has more than 32 years of experience teaching basic statistics, statistical quality control and process improvement, regression analysis, time series forecasting, and design of experiments to both undergraduate and graduate business students. In 2000 Professor O’Connell received an Effective Educator award from the Richard T. Farmer School of Business Administration. Together with Bruce L. Bowerman, he has written seven textbooks. These include Forecasting and Time Series: An Applied Approach and Linear Statistical Models: An Applied Approach. He is one of the first college instructors in the United States to integrate statistical process control and process improvement methodology into his basic business statistics course. He (with Professor Bowerman) has written several articles advocating this approach.
James Burdeane “Deane” Orris J.B. Orris is a professor of management science at Butler University in Indianapolis, Indiana. He received his Ph.D. from the University of Illinois in 1971, and in the late 1970s with the advent of personal computers, he combined his interest in statistics and computers to write one of the first personal computer statistics packages—MICROSTAT. Over the past 20 years, MICROSTAT has evolved into MegaStat, which is an Excel add-in statistics program. In 1999 he wrote an Excel book (Essentials: Excel 2000 Advanced) and has done work in neural networks, spreadsheet simulation, and statistical analysis for many research projects. He has taught statistics and computer courses in the College of Business Administration of Butler University since 1971. He is a member of the American Statistical Association and is past president of the Central Indiana Chapter. In his spare time, Professor Orris enjoys reading, working out, and working in his woodworking shop.
Spreadsheet Modeling and Decision Analysis: A Practical Introduction to Business Analytics 7th Edition$74.80
Updated for use with Microsoft Office Excel 2013, Spreadsheet Modeling and Decision Analysis, 7th Edition, provides you with succinct instruction in the most commonly used management science techniques and shows you how these tools can be implemented using the most current version of Excel for Windows. This text also focuses on helping you develop algebraic and spreadsheet modeling skills.
Analytic Solver Platform replaces Risk Solver Platform in this edition. Analytic Solver Platform includes all of the capabilities of risk Solver for risk analysis and Monte Carlo simulation, all of the capabilities of Premium Solver Platform for optimization, and new capabilities for finding robust optimal decisions using simulation, optimization, stochastic programming, and robust optimization methods.
Each chapter opens with a real-life case study that forms the basis for several examples within the chapter. The questions included in the examples create a roadmap for mastering the most important learning outcomes within the chapter. A synopsis of each chapter’s introductory case is presented when the last of these examples has been discussed. Instructors of distance learners may find these introductory cases particularly useful.