Market structure and technological change

Market structure and technological change

Book reviews (2) We artificially fix a great number of variables [which we can't observe] consumer preferences, actions or inactions of competitors, a...

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Book reviews (2) We artificially fix a great number of variables [which we can't observe] consumer preferences, actions or inactions of competitors, and social values, to name a few. Reality changes along hundreds of axes, while models typically are aligned along only a few. (3) We ignore 'tolerance thresholds' and the cumulative effects of past changes (in the independent variables). (4) Once an active cause has been isolated, we are often unable to detect that element of it which actually produced that effect: its affective essence. (5) We can quantify the value of ( h a v i n g ) ' g o o d timing'. (6) It is difficult to separate symbiotic causes and effects. Causes and effects feed each other and, given certain conditions, chain reactions develop, the extent of which we are unable to predict or even trace once they occur. (7) We assume 'reversibility of results' but we cannot undo consumer impressions, perceptions, and values, nor can we erase experiences from their memories. (8) (Models ignore that) people represent the most unkown, most unanticipated ingredient in any process chain. Each person is the ultimate ' black box'. If causal modeling is contrived, extrapolative models, in the author's view, are perilous. " T h e road to success is strewn with carcasses of those who considered change a constant." (p. 22). With what, we now ask, do we replace our quantitative decision aids? Is there a secret weapon that will enable us to succeed when managing in change? And the Prophet answers us: " T o profit in change, we must profit through people." (p. 92). "Whereas managers of the past focused on things, managers of the future will focus on people." (p. 170). To engage in 'forward thinking', then, is to understand how to select, develop, shape, nurture, and reward one's human capital, especially one's managerial talent. Forward thinking is in essence an essay on the key role of people in adapting organizations to the turbulence of changing times. Robert Gilbreath has written an incisive, provocative, and lucid commentary on management practice. Practitioners of the art of management will enjoy its one-liners ('In times of change, the

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bottom-line still exists. It's simply moving up the page') and find its message energizing. The methodologist will also appreciate the clarity of the warnings against robot-like modeling, and will find herself rethinking traditional assumptions about the proper balance of analysis and judgment. In one respect, the message of the book is certainly overstated. Time and again the author implores us to realize that 'change is the factor in the world.' Presumably then, all our creative energy must be channeled into coping with change. In this, the Prophet appears to overlook the fundamental theorem of the limits of change: 'Plus ~a change, plus c'est la meme chose.' The more things change, the more they stay the same. Leonard Tashman University of Vermont

William L. Baldwin and John T. Scott, Market Structure and Technological Change (Harwood Academic Publishers, Chur, New York and Switzerland, 1987) pp. 170, $? This monograph is a volume in the Harwood publisher's series in the Fundamentals of Pure and Applied Economics, within the section covering the Economics of Technological Change. After a brief overview that discusses the pioneering work of Joseph Schumpeter on the process of innovation in an oligopolistic economy, the authors provide an extensive review of current economic theory in the area. This is presented mainly by means of diagrams. The discussions are generally lucid, although more detailed annotation of the diagrams would have helped the reader grasp the arguments more readily. The next major chapter reviews the empirical research that has attempted to test the basic theories. The authors have done an excellent job of distilling the essence from a large literature (there are 340 references at the end of the book) and summarizing the main findings. In particular, it appears that firm size has very little to do with innovation, once due allowance has been made for different industries. The third and final major component of the text is an analysis of the diffusion of innovations and of the factors that affect the rate of diffusion. It is this part that is likely to be of greatest interest

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Book reviews

to forecasters. By and large, the rate of diffusion is found to be positively related to the number of firms in the industry, but negatively related to size differences. While such findings may not affect technological forecasts in the short- to mediumterm for single markets, such ideas could be useful when considering regional sub-markets or diffusion in an industry in different countries. A declared aim of the series is to prevent the ' Balkanization' of economics, and the authors have covered a lot of ground in providing a unified treatment of the subject matter. Those interested in technological forecasting will find some familiar names such as Mansfield interspersed through the text but, by and large, the forecasting literature and related issues are not discussed. Nevertheless, this monograph can be recommended as a stateof-the-art review of economic theory and practice relating to technological change. Keith Ord

The Pennsylvania State University

Bruce L. Bowerman and Richard T. O'Connell,

Time Series Forecasting, Unified Concepts and Computer Implementation, 2nd ed. (Duxbury Press, Boston, 1987) pp. 540. The focus of this book is on the concepts and applications of time series methods. The authors' approach to time series forecasting is illustrated using the SAS software package, providing the readers with detailed information about the use of SAS. The importance of forecasting for many functional areas (marketing, finance, production, personnel management and process control) is highlighted. Bowerman and O'Connell introduce the book as a text for applied courses in time series forecasting and as a reference book for practitioners. I agree that the text is suitable for applied undergraduate and graduate courses. However, I disagree that it is a useful tool for practitioners. The text touches only slightly on the practical implementation of forecasting. In this context, other books such as those by Levenbach and Cleary (1984) and Wheelwright and Makridakis (1985) do a superior job. In addition, practitioners seek user-friendly software that, unlike SAS, do require complex

procedures to use particular/forecasting methods, and which provide easy-to-interpret results. It is very difficult to judge the efficiency and accuracy of a forecasting method from the output of SAS, the authors' chosen software for illustrative purposes. For example, SAS provides the actual, forecast and error values in the same sequence for each time period, one period after another. This makes it difficult to draw conclusions and to track error over periods of time. Managers prefer software such as Auto-Box, 4Cast/2, AUTOCAST, ISP, STATGRAPHICS, etc. (see Mahmoud et al., 1986, Mahmoud, 1988). For Bowerman and O'Connell's text to have been appropriate for and attractive to practitioners, software considerations should have been more carefully addressed. For all readers, a summary table of all SAS procedures with respect to time series methods would be helpful. The table could describe the functions of each procedure (SAC, SPAC, etc). In chapter 1, the authors discuss the components of time series. Later, they explain data patterns. They do not address stationary data, however. It is essential to explain stationarity before this is referred to in the discussion of the Box Jenkins technique in chapter 2. The authors should introduce stationarity in chapter 1, provide a graph to illustrate it and explain that it will be covered in chapter 2. One observation is that the authors focus on only a few accuracy measures. This is in contrast to the approaches taken by other authors such as Makridakis et al. (1983) and Mahmoud (1987). It is important to expose the reader to a variety of accuracy measures and include those such as Theil's U-statistic and R 2. The focus of the text would have been more sharp had Bowerman and O'Connell provided brief background information about both quantitative and qualitative forecasting methods, with examples of each. They should then have indicated that their text addresses, within the group of quantitative methods, only a selection of time series methods. This is important for the reader who is new to forecasting. Some exponential smoothing, methods such as Holt's exponential smoothing, were omitted. The authors introduce the Box-Jenkins technique first. It would be better to begin with simple time series techniques and gradually progress to the more difficult ones. Also chapter 7, which