428 Book reviews

Von Hippel’s second major contribution is something he calls ‘functional sources of innova- tion’. According to Von Hippel, the source of innovations can be foreseen by identifying the firm that is most likely to benefit from it. In his own words: “I have been repeatedly struck by the clear, strong patterns that can be observed in the data”. (p. 7) Like most forecasters, my ears perked up when I read about a sighting of those elusive recurring patterns, especially strong patterns in the diffusion of innovations. Unfortunately, my enthusiasm was dashed when I learned that in- novations were overwhelmingly developed by firms that expected the most attractive returns from an innovation. 1 was expecting something more. It sounded very much like the basis of capitalism to me.

The chapters in Souder’s book that should be of interest to forecasters focus on the evolution of products from commercialization to market accep- tance. Souder presents a growth/ decline pattern that serves as an alternative to the traditional product life cycle. New products, he found, rarely face a smooth ride to market acceptance. Instead, flawed early versions are replaced by superior later versions, which, in turn, are replaced by still

better innovations later on. The summary provided in Chapter 3 sings the

often sung refrain that “Perseverance is the lifeblood of an innovation”. (p. 45) Other findings include:

_ Successful innovation is not achieved by a single person acting alone, or a single theory or practice. Combinations of sources, technologies and persons are most likely.

_ Innovation does not follow a straight line. Consequently, extrapolations are unlikely to be successful. Rather the process is characterized by many fits and starts and move jerkily from com- mercialization to acceptance.

_ Innovations face many barriers Chapter 5 covers the causes of new product

project success and failure. The discussion centers on topics such as ‘quality of available resources’ and the ‘degree of detailed planning and control’.

Chapter 8 - Picking Winners - talks about ‘idea flow’ and ‘idea judging’, and provides five approaches for accomplishing each. Checklists and scoring models are provided, along with guidelines for choosing the most suitable project selection method. Sounder also proposes a scale for decid-

ing whether to undertake a new project, to wait, or to pursue alternative technologies. The procedures seem worthwhile, but a bit mechanical.

To be fair, Souder’s approach is tempered. Those looking for a recipe for successful innova- tion, he notes, are likely to be disappointed. Sounder clearly states that we do not know enough about innovation to provide such a recipe. But, there is reason for encouragement. With the use of more databases such as those painstakingly con- structed by both Souder and Von Hippel we are taking steps closer towards such goals.

Actually, drawing inferences about the diffu- sion of innovations from large databases is not all that new. In 1958, Jewkes, Sawers, and Stillerman published a 428 page book on the sources of inventions. It too relied on a large database - 150 pages of cases. Most telling, more than thirty years later it is still one of the best books on innovation available. Jewkes et al. conclusions are remarkable similar to those offered by more mod- ern authors.

In summary, I recommend both of these new books to those interested in predicting innovation. I also recommend the Jewkes book from 1958 (revised in 1968). I recommend Jewkes highly.

Steven P. Schnaars Baruch College, CUNY, USA

References

Jewkes, John, David Sawers and Richard Stillerman, The

Sources of Invention, (MacMillan, London, 1958).

Von Hippel, Eric, 1986, “Lead users: A source of novel

product concepts”, Management Science, 32, no. 1, July,

791-805.

M.B. Priestley, Non-linear and Non-stationary Time Series Analysis, (Academic Press, London, 1988) c25.00, pp. 237.

The twin assumptions of linearity and stationarity underlie much of the theory of traditional time- series analysis. In recent years, there have been many interesting research developments regarding non-linear and non-stationary models and this book covers much of this research. In some ways the book can be regarded as an up-dated expan- sion of Chapter 11 of the author’s earlier book

Book reviews 429

(Priestley, 1981) which has established itself as a sound, thorough and frequently-cited reference source, particularly on frequency-domain topics.

After two introductory chapters, Chapter 3 dis- cusses Volterra series expansions, polyspectra and generalized transfer functions. Chapter 4 describes some special non-linear models including bilinear, threshold and exponential autoregressive models, while Chapter 5 covers the author’s work on state-dependent models. Chapter 6 discusses non- stationary processes with the emphasis on time-de- pendent spectra such as evolutionary spectra. Fi- nally, Chapter 7 considers the prediction, filtering and control of non-stationary processes. Given the mathematical difficulty of some of the material, the author makes it as accessible as can reasona- bly be expected. Even so, some sections are inevi- tably rather heavy going (e.g. Volterra series ex- pansions).

It is evident that there has been substantial progress on non-linear modelling in recent years, although implementation problems can still be formidable in practice. In contrast the treatment of non-stationarity indicates less development and I noted more common material with Priestley (1981). I also note that the book does not attempt to cover alternative time-domain approaches to non-stationarity such as the use of intervention analysis. There is also no treatment of outliers which are relevant both as one form of non- stationarity and also to non-linear modelling, given the difficulty in distinguishing between a linear model with a few outliers and a non-linear model.

The immediate implications of this material to practical forecasting may be rather limited. The examples in the book are mainly restricted to the famous sunspots and lynx data-sets and to simu- lated data where model-building, rather than fore- casting, is of prime concern. It can be difficult to evaluate conditional expectations for non-linear models for more than one step ahead, while the frequency domain material is perhaps more rele- vant to filtering and control, Nevertheless, the book will undoubtedly be of interest to academic time-series researchers, particularly those working on non-linear models.

Chris Chatfield University of Bath, U.K.

Reference

Priestley, M.B., Spectral Analysis and Time Series (Academic

Press, London, 1981).

M.J.D. Hopkins, ed., Employment Forecasting: The Employment Problem in Industrialized Countries,

(Pinter Publishers, New York and London, 1988) pp. 257.

Since the increase in oil prices in 1973, unemploy- ment has been a major problem for the industrial- ized world. This volume, which is based on papers presented at a workshop held in September 1983, was prepared for the United Nations International Labor Office’s World Employment Programme. The aims are (p. xv) “to analyse feasible alterna- tives to unemployment which can be implemented within the next ten years” and “to assess the experience and methodologies for medium - to long - term employment forecasting of experi- enced practitioners in this field”. The intended readership includes those interested in the tech- niques used in forecasting unemployment. How- ever, the definition of forecasting adopted is (p. 210) that it “is not an attempt to predict the future but rather to look at alternatives under varying assumptions about events unknown [in advance]“. The result is that the future of the labor market is considered in the context of vari- ous assumptions about national and world econo- mies.

There are nine chapters: case studies of the United Kingdom, France, Belgium and the Netherlands; two studies of international lin-

kages; one study of a semi-industrialized country (Mexico); and an introduction and a conclusion. The approach adopted in each chapter is to de- scribe the main forecasting techniques (that is, usually the macroeconomic model used), sum- marize recent trends in unemployment, discuss alternative scenarios as solutions of the urnemployment problem, and present the policy implications of the work.

The description of the models and scenarios is generally clear and relevant. However, the authors of each chapter usually consider only their own economic model and then use it for simulation. There is little evidence that the particular model selected is adequate, and in fact, when it is possi- ble to evaluate forecasts, the impression is that the