IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT. VOL. 37. NO, 3. AUGUST 1990 177

Developing Hi-Tech Industrial Products for World Markets

IGAL AYAL A N D JOEL RABAN

Abstract-The innovation process for 81 industrial products, devel- oped and launched internationally hy 55 Israeli electronic and chemical manufacturers between 1980- 1985 was investigated. Performance of 25 preparatory marketing activities listed in the context of an innovation process model was studied. Factor analysis and cluster analysis were used to identify four total activity patterns. These patterns were found to be significantly correlated with economic success: hest performers were the clusters identified as market guided developers and systematic plan- ners. While the preparatory marketing activity patterns are related to firm characteristics, markethroduct characteristics and technologi- cal process characteristics the major inter-cluster differences appear to arise from managerial practice and market strategy rather than external characteristics.

Keywords- Product innovation process: R&Dharketing interface; hi- tech product development: hi-tech exports; new product success factors.

INTRODUCTION

HE SELECTION and development process for new hi- T tech industrial products is a highly risky endeavor. While the success ratio depends on the study cited (see [2]-[4], (131) and particularly on the stage of development when counting starts (e.g., idea versus formally budgeted project), there is no doubt that many or most projects fail to achieve economic success.

The risks for small-country hi-tech manufacturers are even larger than usual. The typical American (or Japanese, or Ger- man) manufacturer of hi-tech industrial products, particularly if he is small, would in all probability develop the new product for his home market, and would launch it in export markets (if at all) only after fairly substantial experience has been ac- cumulated. Note also that the U.S. Department of Commerce defines a small firm as under 500 employees-which could easily translate into $50 million or more in annual sales. In small countries, such as Israel, the home market for new in- dustrial products is far too small to allow gradual internation- alization. The firm frequently has to export the new product from day one, generating demand in markets that are far away from home in geographic, cultural, and economic terms. Thus we find in Israel start-up hi-tech firms with $300-K in annual sales whose initial launch for a new industrial product occurs

Manuscript received August 15, 1988. The review of this papcr was pro- cessed by Editor D. F. Kocaoglu. This work was supported financially by the Office of the Chief Scientist, Ministry of Industry and Trade, Government of Israel, and by the Israeli Industry Center for Research and Development.

I . Ayal is with the Faculty of Management, Tel Aviv University. Tel Aviv Israel 69978.

J . Raban is with the Interedisciplindry Center for Technological Forecast- ing, Tel Aviv University, Ramat Aviv, Tel Aviv, Israel 69978.

IEEE Log Number 9035704.

in export markets-and when their annual volume reaches two or three million dollars they are quite likely to set up foreign branches or subsidiaries [ 11.

The process of technological innovation has been studied extensively over the past 30 years (e.g., [14], [12], 1171, 1711, and several empirical and/or normative models of the process have been suggested (e.g., [2]-(41, [7]-19)). Much of the ef- fort has concentrated on empirical identification of the deter- minants of economic success, the most prominent of which turn out to be marketing-related factors (e.g., [2], [ 3 ] , [4], [6]-[9], [lS], [16], [18]-[20]). Many of these studies have in- vestigated the development process in detail, using a model not too different from the one presented in the next section (e.g., (81). Several of them used fairly large samples of firms and/or projects. Almost all of them, however (with the exception of [ 1 SI), investigated distinct activities rather than total patterns. Furthermore, apart from Cooper’s studies of Canadian firms, the research has concentrated on large manufacturers, devel- oping products primarily for their large domestic markets.

It stands to reason that a small manufacturer, developing a product for distant markets, would have less experience in his potential market than would a large domestic marketer. Thus the values of the marketing-related success determinants for this small potential exporter would be more dependent on formal data collection and analysis. The present study differs from those reported in the literature on several counts:

1) It concentrated mostly on small firms. 2 ) The products under study were developed in Israel for

export to developed country markets. The ratio of ex- ports to total sales for these products ranged from 80% to 100%.

3 ) Rather than focusing on success determinants as distinct activities in the development process we attempted to identify total patterns of preparatory marketing activi- ties. The rationale was that many of these activities are performed at the same time, and extent and quality of ex- ecution depend on management philosophy- which was not expected to be specific to a single activity.

4) The analysis did not stop with identification, but went on to test whether these patterns are determined by external characteristics of the firm, market and technology, or depend on project management.

The study was conducted at the Interdisciplinary Center for Technological Analysis and Forecasting, Tel Aviv University, at the request and financial support of the Office of the Chief Scientist, Ministry of Industry and Trade, Government of Is-

0018-9391/90/0800-0177$01 .OO 0 1990 IEEE

178 IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT. VOL. 37. NO. 3, AUGUST 1990

RODUCT DL-T

R O T O N P C TLslWC

Fig. I . Hi-tech product innovation process

rael. The aim was to assist in determining policy and making decisions regarding support of R&D projects in Israeli firms.

The following sections of this paper will present the hi-tech product innovation process model employed, sample charac- teristics, research procedure, and findings.

The Hi-Tech Product Innovation Process The hi-tech product innovation process (described in Fig.

1) is a sequential and iterative process of innovation deci- sion making driven by a combination of preparatory market- inghusiness activities and R&D/engineering activities. The

process includes eight steps and seven decision nodes through which a new product is channelled from idea generation to full-scale commercial operation. At each decision node G means go on, S means stop, and R means return-go back to one of the previous steps.

Data Base The sample consisted of 81 new industrial products devel-

oped by 36 firms in the electronics industry (including soft- ware) and 19 in the chemical industry (including pharmaceuti- cals) in Israel, and launched into world markets between 1980

179 AYAL AND RABAN: DEVELOPING HI-TECH INDUSTRIAL PRODUCTS FOR WORLD MARKETS

TABLE I V A R l M A X ROTATED FACTOR LOADINGS (OVER (U 4 ) A N D EXPLAINED VARIANCE

PhaseIStage Activity 1 2 3 4 5 6 7

P r e R & D

During Development

During Market Testing

During Commercial Launch

V I .

VII.

11. 1) Assess. mrkt potential 2) Ident. main competitors 3) Ident. tariff and NTBS

5) Project sales volume 6) Assess. econ. viability 7) Concept test w/customers 8) Develop mktg. strategy 9) Mkt. analysis and segment

111. 4) Invest. mkt. structure

IV .

V. 10) Project sales volume 11) Assess. econ. vability 12) Mktg. prog. for launch 13) Mkt. monitoring 14) Certif. and patenting 15) Field testing 16) Trade fairs and exhib. 17) Certif. and patenting 18) Pdct. testing at customer 19) Update mktg. plan 20) Trade fairs and exhib. 21) Test marketing 22) Slct. dist. and agents 23) Prep. and del catalogs 24) Trade fairs and exhib.

VIII.

0.75 0.62

0.55 0.55 0.86

0.92

0.82 0.78

0.54 0.68 0.53 0.75

0.47 0.46 0.57 0.40 0.58

0.92

0.88 0.85

0.73 0.72

0.85 0.48

-0.75

- 0.53 0.72

0.69 0.52

25) Initial advertising 0.77 E i g e n v a 1 u e 4.5 3.1 2.8 2.3 2.2 1.8 1.7

% variance explained 18 12 1 1 9 9 7 7

and 1985. Products were not selected at random from a list of development projects in Israel during that period. Rather, a list of companies representing a broad cross-section of hi-tech manufacturers of varying size in these industries was devel- oped in cooperation with industry experts. Products were se- lected during interviews with senior executives of these com- panies. Development projects included in the sample have all achieved technological completion, and have been commer- cially launched. (Thus, projects that failed during develop- ment or were dropped after test marketing, were not included in the data base.) The projects were selected to represent both economic successes and failures, to enable the researchers to identify discriminating factors.

Research Procedure Data Collection: Following the selection of representative

products, series of interviews were conducted in each firm with senior executives familiar with the history of these prod- ucts.

Among other items, data were collected on the extent of performance of 25 specific preparatory marketing activities during the process. The extent of performance was measured on 0-6 nearly interval subjective evaluation scales, with 0 denoting not performed and 6 denoting performed fully and accurately.

In addition, success of the project was measured in two ways: 1) on the basis of subjective evaluations by management (1-6 scales, with 1 denoting abysmal failure and 6 resounding success); and 2 ) by the ratio of cumulative sales in the first three years on the market to the investment in R&D for the project. In fact, the two measures turned out to be highly correlated and thus only one of them (the subjective evaluation scale) was used for the analysis presented in this paper.

Analysis: A three-stage procedure was utilized to identify total patterns of preparatory marketinghusiness activities dur- ing the innovation process and relate them to various firm, market and project characteristics. In the first stage, the ex- tent of performance of the 25 specific activities was factor- analyzed, to derive orthogonal underlying dimensions and re- duce the raw data. Factor scores were generated for the seven major factors identified. In the second stage, factor scores for each project served as the input for a hierarchical clus- ter analysis program, to find clusters of projects with similar total activity patterns. Four major clusters of projects were identified through this procedure. In the last stage crosstab- ulation and analysis of variance were used to investigate the relationships between cluster membership on the one hand, and economic success, firm characteristics, market character- istics, and project characteristics on the other. Results and conclusions will be presented in the following sections.

FINDINGS

Basic Dimensions in Preparatory Marketing Activities

While the extent of performance of specific activities, and their relations to economic success and to firm/project/market characteristics are of interest by themselves, the focus of the present paper is on total activity patterns. The performance measures for the 25 activities listed in Table I were standard- ized, and principal-components factor analysis was conducted. Seven factors, accounting for 73% of the variance, were ex- tracted and rotated. Factor loadings after varimax rotation are presented in Table I. (Only loadings above 0.4 are presented, for clarity.)

Reviewing Table I, it is apparent that the factor structure is quite clear and well-defined. Activities that load on the same

180 IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT. VOL 37, NO 3, AUGUST 1990

I

AVERAGE O s FACTOR SCORES 0

45

I

FACTOR NUMBER

CLUSTER1 CLUSTER2 el CLUSTER3 19 CLUSTER4

Fig. 2. Prepatory marketing activity patterns. Average factor scores for four clusters.

factor, and thus are related by managerial practice-at least for the sample of projects studied here-are also related by common sense, and by the theoretical structure of Fig. 1.

Based on the loadings in Table I , we can identify sets of interrelated activities (or activity areas) as follows.

Factor 1 : Market research and economic assessment, early in the innovation process.

Factor 2: Handling of administrative and legal barriers and opportunities, throughout the process.

Factor 3: Marketing planning (and plans update) during the development and testing stages.

Factor 4: Early presentation at international exhibitions and trade fairs.

Factor 5 : Development of customer-based marketing strat- egy at early stage. While this factor seems simi- lar to F3, it is different in the sense that the activ- ities are conducted earlier, and focus on qualita- tive information and strategic implications based on a few selected customers rather than broader based quantitative surveys.

Factor 6: Marketing communications during commercial launch.

Factor 7: Reliance on distributors and agents (at the ex- pense of direct market connections).

The generated factor scores measure the combined perfor- mance in each one of these activity areas.

Total Activity Patterns The seven factor scores generated for each product, served

as inputs to a hierarchical cluster analysis procedure based on complete linkage. This procedure helps in deriving clusters that are as homogeneous as possible. The analysis resulted in the identification of four distinct clusters, comprising 51 projects. (Thirty of the original 81 projects were eliminated because of missing data.)

Mean factor scores for the four clusters, on all seven fac- tors, are presented in Fig. 2.

Fig. 2 shows substantial differences among clusters in mean factor scores. In fact, analysis of variance shows the differ- ences among clusters to be significant on all factors, with the exception of F1 (the differences on F3, F4, and F5 are sig- nificant at better than 0.001). On the basis of these values we can characterize the clusters, as follows.

Cluster 1 (comprising 15 projects): These projects are generally very low compared to all others on F3 (marketing planning), and on F2 (handling of administrative and legal

barriers and opportunities). They are relatively high on F7 (reliance on distributors/agents, at the expense of direct mar- ket connections). Overall, they can be characterized according to preparatory marketing activities as the underplanners.

Cluster 2 (comprising 16 projects): These tend to be very high on F3 (marketing planning), and quite high on F6 (mar- keting communications during launch) and F1 (early market- ing research and economic assessment). They tend to be low on F4 (early presentation at exhibitions). The broad picture is systematic planners.

Cluster 3 (eight projects): These projects have extremely high values on F5 (development of customer-based marketing strategy at an early stage). They are also fairly high on F2 (handling of administrative barriers and opportunities) and F 1 (early economic assessment). On the other hand, they are rel- atively low on F6 (marketing communications during launch), F7 (indirect market connections), and F4 (early presentation at exhibitions). Overall, they can clearly be labeled the market guided developers. They differ from cluster 2 firms in the fact that they do most of their research and planning very early in the process (concept formulation study), in close contact with specific selected customers. Cluster 2 firms do much of their information gathering during the development stage relying on broader-based responses to better-defined product attributes. T-tests show significant differences between the two clusters on factors 4-7.

Cluster 4 (12 projects): Their most striking characteristic is very high values on F4 (early presentation at international exhibitions). They are also fairly high on F6 (marketing com- munications at launch). On the other hand they are relatively low on F5 (early customer-based marketing strategy), F7 (in- direct connections to market), and F1 (early economic assess- ment). We identify them as the impatient sellers.

A more detailed picture of the similarities and differences in the performance of preparatory marketing activities by mem- bers of the clusters is presented in Table 11. The table shows mean performance levels on the 25 activities for each clus- ter, and ANOVA results. Overall, 20 out of the 25 activities show significant differences among cluster means. The dif- ferences between the two successful clusters 2 and 3 are of particular interest, and the mean levels in Table I1 (as well as separate T-tests) show sizable differences between the two clusters on two pre-development activities (concept test with customers, and early development of marketing strategy), and two early launch activities (selecting distributors and agents, and preparing and delivering catalogs).

Relationships Between Activity Patterns and Economic Success

The first question, of course, is the relative success of these clusters. A crosstabulation of economic success (5-6 versus 1-4 on the subjective scale) on cluster is presented in Table 111. It is clearly demonstrated that cluster 3, the market guided developers, are most successful, followed by cluster 2, the systematic planners. The Underplanners of cluster 1 are least successful, as could be expected.

It might also be asked whether the subjective performance evaluations of preparatory marketing activities are based on

AYAL AND RABAN: DEVELOPING HI-TECH INDUSTRIAL PRODUCTS FOR WORLD MARKETS 181

TABLE I1 MEAN PERFORMANCE LEVELS OF PREPARATORY MARKETING ACTIVITIES BY CLUSTER

Cluster ~ ~~

Activity 1 2 3 4 F,.,, Significance

Assess market potential Identify main competitors Identify tariff and NT barriers Investigate mkt. structure and seg. Project sales volume Assess economic viability Concept test w/customers Develop mktg strategy Mkt. analysis and segment selection Project sales volume Assess economic viability Mktg. programming for launch Mkt. monitoring Certification and Patenting Field testing Trade fairs and exhibitions Certification and patenting Pdct testing at customer site Update mktg. plan Trade fairs and exhibitions Test marketing Select distributors and agents Prepare and deliver catalogs Trade fairs and exhibitions Initial Advertising

3.53 4.56 4.62 4.40 4.19 5.25 1.07 3.00 4.12 2.93 4.44 5.37 2.80 4.50 4.00 2.67 3.75 3.62 1.80 2.31 5.00 1.73 2.06 4.75 2.07 4.87 5.50 2.27 4.62 4.62 2.53 4.19 4.12 1.80 4.75 3.87 3.47 4.81 5.00 0.47 3.31 3.87 3.07 4.06 5.12 0.87 0.25 0.25 0.67 3.69 3.75 1.13 4.87 3.87 0.53 4.31 3.00 0.40 0.06 1.00 0.53 0.00 0.25 3.80 4.94 2.00 3.20 5.37 3.12 1.67 3.75 2.50 3.20 3.67 2.62

3.42 1.18 4.33 0.53 3.25 3.08 3.17 3.28 2.33 3.78 2.42 1.27 1.17 5.91 1.83 3.72 3.58 9.92 2.58 6.90 2.33 3.55 2.67 6.93 3.75 1.92 3.25 5.47 4.92 2.54 2.75 5.63 3.92 6.31 4.00 10.25 2.17 9.16 4.17 27.95 2.67 10.37 3.25 4.63 4.33 4.60 5.00 7.53 3.25 0.41

NS NS

0.037 0.029 0.016

NS 0.002 0.018 o.Ooo1 0.001 0.021 0.001

NS 0.003 0.068 0.002 0.001 o.Ooo1 o.Ooo1 o.Ooo1 o.Ooo1 0.006 0.007 o.Ooo1

NS

TABLE 111 ECONOMIC SUCCESS OF PREPARATORY-ACTIVITY-BASED CLUSTERS

Cluster

Overall Success 1 2 3 4 Total

Successful 5 12 7 6 30 Unsuccessful 10 4 1 6 21 Total 15 16 8 12 51

Significance: x 2 = 9.3; DF = 3; p < 0.05

TABLE IV MEAN EXPENDITURES ~$ooo) ON PREPARATORY MARKETING ACTIVITIES

Cluster

Stage 1 2 3 4 F Significance

Pre-R & D 20 73 63 19 10.0 0.001 Duringdevelopment 26 158 162 140 5.5 0.003 During market-testing 52 267 292 156 4.9 0.005 Early launch 84 167 222 109 2.5 0.08

facts or hindsight. Table IV presents a one-way analysis of variance of the total dollar expenditures on preparatory mar- keting activities, by phase. It is clearly seen that the differences are real and significant. The successful clusters 3 and 2 have spent more than cluster 1 and 4, on preparatory marketing activities, especially in the early stages.

Relationships Between Cluster Membership and Firm Characteristics

Do the firms that developed the products in the four clus- ters differ in terms of their inherent characteristics? Table V investigates some relationships. It appears that the clusters do not differ in terms of industry. They, do, however, differ sig- nificantly in terms of firm size: Cluster 2 firms are larger than

the others- which may explain their systematic planning pro- cedures. Cluster 4 firms appear to be smallest, and thus their impatience to sell may be the result of less financial staying power. The firms also differ in terms of the portion of their personnel employed in marketing (most in cluster 2, least in cluster l), and in marketing management responsibility given to branches, subsidiaries, or joint ventures abroad (most in cluster 2 , with its larger firms, least in cluster 1). A notable finding is that the differences are basically between cluster 2 members and all the rest. There are no differences on the firm level between cluster 3- the most successful group- and cluster I , with the poorest record.

Relationships Between Cluster Membership and Market/Product Characteristics

If firm characteristics do not explain the differences in preparatory marketing activity patterns, maybe the explanation lies in market or product characteristics? Table VI compares the means of the four clusters on three market attributes and five product attributes. While some of the differences among means are not significant due to high within-group variances, the results are noteworthy at least as indications.

Reviewing Table VI in detail, the findings are as follows.

Relevant estimated market sizes vary widely. Size tends to be smaller for the more successful clusters 2 and 3, but the real difference is in the variances, which are huge for clusters 1 and 4. While some of the large estimates may be due to more homogenous markets, it appears that other large market size estimates in clusters 1 and 4 are due to less detailed understanding of market structure and customer needs. Market growth rates are highest for clusters 3 and 4,

182 IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT, VOL. 31. NO. 3. AUGUST 1990

TABLE V RELATIONSHIPS BETWEEN CLUSTER MEMBERSHIP AND FIRM CHARACTERISTICS

~~

Significance of Cluster

Characteristic 1 2 3 4 Association

Industry

Electronics 8 1 1 5 7 x 2 = 0.8; DF = 3 Chemicals 7 5 3 5 not significant Size ($M/year)

0-2 5 2 3 8 ,yz = 46.1; DF = 9 2-7 9 0 3 4 p < o . 0 0 1 7-50 1 6 2 0 50 + 0 8 0 0 % Employees in Marketing

9 x 2 = 7.9; D F = 3 6 10% 14 8 6 > 10% 1 8 2 Foreign Branch Responsibility For Marketing Management

3 p < 0.05

< 33% > 33%

14 6 6 10 x 2 = 13.5; DF = 3 1 10 2 2 p < O . 0 0 5

TABLE VI RELATIONSHIPS BETWEEN CLUSTER MEMBERSHIP AND MEAN MARKET i PRODUCT CHARACTERISTICS

Significance of Bartlett’s Variance Homog. Clusters

Characteristic 1 2 3 4 F Significance Test

(S.D.) (292) (292) (292) (958) Market growth rate (%) 14.5 13.7 36.6 23.9 1.9 N.S. 0.006

(9.3) (11.2) (36.1) (17.8) Concentration (combined 52 50 62 74 3.9 0.015 N.S.

Global Market Size ($M) 420 14 1 282 497 0.3 N.S. o.Ooo1

share of three largest) (19) (18) (26) (13) Typical purchase size 83 247 232 77 1.4 N.S. 0.006

Estimated life-cycle 7.9 8.1 7.1 5.1 1 . 1 N.S. 0.044 (years) (4.3) (5.4) (2.7) (2.2)

relative to competitors (1.2) (1.4) (0.7) (1.2)

($ooo) (198) (387) (267) (139)

Product performance, 1.3 1.2 2.6 2.0 2.9 0.046 N.S.

( - 3 to + 3 scale, 0 = same) Standardization 5 .3 4.4 4.6 5.4 (1 -6 scale, (0.8) (1.5) (1.3) (1.0) 2.2 0.10 N.S . 6 = standard) Ease of comp. copy 3.3 2.6 2.6 3.2 (1-6 scale, (1.5) (1.4) (1 4 (2.0) 0.6 N.S. N.S. 6 = very easy)

with their more ambitious projects (see projects charac- teristics, below). Clusters 3 , and particularly 4, aim at somewhat more concentrated markets-maybe at the takeoff stage of the life cycle. Wide variability occurs in typical purchase size by end user (from hundreds of dollars to over one million), and the clusters are far from homogenous on this character- istic. Still, the more successful clusters seem to offer larger-ticket systems or to face more concentrated buy- ing. All of the R&D products in the sample have relatively short life cycles. The differences among clusters are not significant. Most of the managers, in all clusters, assessed their products as superior to the competition. The most suc-

cessful cluster 3 however, were unanimous in judging their products to be far superior to the direct competi- tors.

7) The less successful clusters 1 and 4 generally offered standardized products to the whole market. Clusters 2 and 3 tended to adapt the product more to specific needs.

8) Most products were judged to be moderately difficult to copy. No significant differences were found among clusters, though 2 and 3 tended to judge their products as better defended.

Overall, it appears that the differences in Table VI relate more to marketing strategy than to market characteristics. The successful clusters 2 and 3 investigate the market in more detail, segment it, carve their niches, and adapt their products more to specific customer needs. These findings are in line

183 AYAL AND RABAN: DEVELOPING HI-TECH INDUSTRIAL PRODUCTS FOR WOKLD MARKETS

TABLE VI1 RELATIONSHIPS BETWEEN CLUSTER MEMBERSHIP A N D

PROJECT CHARACTERISTICS

Cluster Significance of Characteristic 1 2 3 4 Association

R & D Investment < $300K 11 IO 4 5 x 2 = 11.7 $300 K-999 K 2 5 0 3 d.f. = 6 2 $1 M 2 1 4 4 p < 0.07 Development Time

< 12 months 9 1 4 3 5 x 2 = 16.1 13-24 months 1 2 1 4 d.f. = 6 > 24 months 5 0 4 3 p < 0.015

with much of the literature cited in the introduction, and are particularly appropriate for relatively small firms fighting in global markets to survive and succeed among giants.

Relationships Between Cluster Membership and Project Characteristics

Apart from the preparatory marketing activities, did the de- velopment processes of the products represented in the four clusters differ markedly? Table VI1 investigates the invest- ments in technical development, and the development time for the clusters. It appears that both clusters 1 and 2 mostly comprised small projects (though cluster 2 members were ap- parently more efficient in development). Clusters 3 and 4 in- cluded more ambitious projects- a fact which further explains the haste of cluster 4 members in going to market.

CONCLUSION

The hi-tech Israeli firms investigated in the current study differ in many characteristics. Only a few of these differences have been presented in this paper. Yet it has been found that it is possible to look at a whole range of preparatory marketing activities throughout the innovation process, and identify dis- tinct patterns. Four clusters of products have been identified. The overall patterns are not the result of industry practice, and are only partially explained by firm size, market and product characteristics or project size. For example, it is not surprising to find that the relatively small projects undertaken by rela- tively large firms in cluster 2 are completed earlier and have a better batting average than the small firm projects in cluster 1. But why were the firms in cluster 3, which were not larger than cluster 1 or 4, but did undertake ambitious projects, most suc- cessful? The answer has to be found in their total approach to the hi-tech product innovation process- being market-guided developers.

Planning the business and marketing aspects of the innova- tion requires research and forethought no less than planning the technological aspects. The effort should start before the technological development effort. In some cases the expendi- tures involved in these activities may be substantially larger than the costs of technological development. We should also factor in the costs of time-delaying market entry rather than cutting the final corners in assuring customer readiness of the product. This is particularly true for products aimed primarily at export markets, which are less familiar, and more expen- sive to research and administer. Yet the proof of the pudding

is in the eating- products where such investments have been made (clusters 3 and 2) were significantly more successful than the others. Note, in addition, that the most significant differences in costs of preparatory marketing activities occur at the pre-R&D stage, and the dollar amounts involved in these stages are only on the order of 5-10 percent of total innovation process costs. This is not too high a price to pay for a much higher probability of success. Finally, it should be pointed out that the present study included only new-product projects which have actually been completed and commercial- ized. When the probabilities of technological completion and of commercialization are included (e.g., [ 1 l]), the differences are bound to be even larger.

I21

(91

1191

1201

REFERENCES 1. Ayal and J. Raban, “Export Management Structure and Successful High-Technology Innovation,” in P. J . Rosson and S. D. Reid, Man- aging Export Entry and Expansion. New York: Praeger, 1987, pp. 358-372. N. Baker, R. Green, Stephen G. Bean, and A. S . Bean, “A Multivari- ate Analysis of Environmental, Organizational, and Process Variables in the Process of Organized Technological Innovation,” I1 Technical Summary, University of Cincinnati, Cincinnati, OH, 1984. Booz, Allen, and Hamilton, Management of New Products, New York: Booz Allen and Hamilton, 1968. -, New Products Management in the 80’s. New York: Booz, Allen, and Hamilton, 1982. J . M. Choffray, and G. L. Lilien. “Strategies Behind the Successful New Product Launch,” Business Marketing, pp. 85-94, Nov. 1984. R. C. Cooper. “Project NewProd: Factors in New Products Success,” European Journal of Marketing, vol. 14, no. 516, pp. 277-292, 1980. - , “The New Product Process: An Empirically Based Classification Scheme,” R&D Management, vol. 13, no. I , 1983. -, “The New Product Process: A Decision Guide for Manage- ment,” Journal of Marketing Management, vol. 3. no. 3, pp. 238-55, 1988. R. C. Cooper and E. J . Kleinschmidt, “An Investigation into the New Product Process: Steps, Deficiencies, and Impact,” Journal of Prod- uct Innovation Management, vol. 3. pp. 71-85, 1986. C. M. Crawford, “Marketing Research and the New Product Failure Rate,” Journal of Marketing, vol. 41, pp. 51-61, Apr. 1977. W. R. Dillon, R. Calantone, and P. Worthing, “The New Product Problem: An Approach for Investigating New Product Failures,” Man- agement Science, vol. 25: 12, Dec., 1979. D. Hamburg, Economics of Research and Development. New York: Random House, 1966. D. S. Hopkins, New Product Winners and Losers, Conf. Board Re- port No. 773, 1980. J. Jawkes, D. Sawyers, and R. Stillerman, The Sources of Invention. London: Macmillan, 1958. M. A. Maidique and B. J . Zirger, “A Study of Success and Failure in Industrial Innovation: The Case of the U.S.A. Electronics Industry,” IEEE Trans. Eng. Manag., vol. EM-31. no. 4, pp. 192-203, Nov. 1984. E. Mansfield and S. Wagner, “Organizational and Strategic Factors Associated with Probabilities of Success in Industrial R&D,” Journal of Business, vol. 48, no. 2, pp. 179-198. Apr., 1975. S. Myers and D. G. Marquis, Successful Industrial Innovation, Na- tional Science Foundation, NSF 69-17, 1969. A. H. Rubenstein, A. K. Chakrabdrti, R. D. O’Keefe, W. E. Souder, and H. C. Young, “Factors Influencing Innovation Success at the Project Level,” Research Management, pp. 15-20, May 1976. R. Rothwell. C . Freeman, A. Horsley, V. Y. P. Jervis, A. B. Robert- son, and J. Townsend, “SAPPHO Updated: Project SAPPHO Stage 11,” Research Policy, vol. 3, pp. 258-291, 1974. C. Voss, “Determinants of Success in the Development for Applica- tions Software,” J. Product Innovation Management, vol. 2 , pp. 122-129, 1985. E. Yoon, and G. L. Lilien, “New Industrial Product Performance: The Effccts of market Characteristics and Strategy,” JPIM, vol. 3, pp. 134-144, 1985.

184 IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT. VOL. 37. NO. 3. AUGUST 1990

lgal Ayal received the B.Sc. degree in mathemat- ics and physics from the Hebrew University of Jerusalem, Jerusalem, Israel, the M.B.A. degree in operations research from Tel Aviv University, Tel Aviv, Israel, and the D.B.A. degree from Harvard Business School, Cambridge, MA.

He has over 20 years of experience as a consul- tant to top management of business firms (particu- larly hi-tech) in Israel, including the Israeli govern- ment, and the USA. He is presently chairman of the Marketing Area at the Recanti Graduate School of

Business Administration, Tel Aviv- University. In addition, he has taught at the University of Connecticut, Storrs, CT, the University of California, Los Angeles, Rutgers University, New Brunswick, NJ, and New York University, New York, NY.

Dr. Ayal has been published extensively in the areas of international mar- keting strategy, strategic planning, new product development. and marketing simulation models. His current research interests continue to cover broad ar-

eas of strategy, with particular emphasis on product and market selection and development.

Joel Raban received the B.A. and M.A. degrees in economics, and the Ph.D. degree in business ad- ministration from Tel Aviv University, Tel Aviv, Israel.

He is currently a Senior Researcher at the Inter- disciplinary Center for Technological Analysis and Forecasting, Tel Aviv University. His research fo- cuses on international marketing of R&D intensive products, and on modelling the innovation process of new brands in existing product categories. Hi5 work experience includes consulting to government

agencies on policies designed to encourage the growth of R&D intensive com- panies through international marketing.

Dr. Raban is a member of the American Marketing Association.