R&D PROJECT ASSESSMENT AS AN INFORMATION AND COMMUNICATION PROCESS

NOTES AND REFERENCES

1. The low level of R&D in Australia has recently come under increasing scrutiny. Recently published OECD technology indicators highlight the intensity of R&D activity in Australia relative to other countries. For the pertinent statistics and a discussion on the causes of low IR&D in Australia see L. Dwyer & T. Alchin, ‘R&D activity in Australia’, National Australia Bank Monthly Summary, January 1987.

2. A recent survey of Australia's top 500 companies reveals that among responding companies 87 per cent did not have an R&D sub-committee of the Board. See B. Hunt, ‘Australian Public Company Boards of Directors 1983 Survey’, The Australian Director, August/September 1984, pp. 12–18.

3. Australian Science and Technology Council, Public Investment in R&D in Australia, Canberra, AGPS, 1985.

4. OECD, Reviews of National Science and Technology Policy Australia, Paris, 1986; Department of Industry Technology and Commerce, Industry and Technology Policy — An Information Paper, March, 1987; L. Dwyer, Review Article, Science and technology policy in Australia: three studies’, Prometheus, 5, 2, December 1987, pp. 419–26.

5. Department of Industry, Technology and Commerce, op. cit.

6. N. Barker and J. Freeland, ‘Recent advances in R&D benefit measurement and project selection methods’, Management Science, 21, 10, June 1975, pp. 1164-1175; E.F. Winkofsky, R.M. Mason and W.E. Souder, ‘R&D budgeting and project selection: a review of practices and models’, TIMS Studies in the Management Sciences, 15, North-Holland Publishing Co., 1980.

7. Excellent reviews of these techniques are available. See, e.g., W. E. Souder, ‘A system for using R&D project evaluation methods’, Research Management, 21, 5, September 1987, pp. 21-37; B. Jackson, ‘Decision methods for selecting a portfolio of projects’, Research Management, 26, 5, October 1983, pp. 21-26; W. Souder and T. Mandakovic, ‘R&D project selection models’, Research Management, 29, 4, July-August 1986, pp. 36-42. For a useful review of ex-ante evaluation techniques involving multiple attributes or criteria in the context of agricultural research see J.R. Anderson and K.A. Parton, ‘techniques for guiding the allocation of resources among rural research projects: state of the art’, Prometheus, 1, 1, June 1983, pp. 180–201.

8. J.C. Higgins and K.M. Watts, ‘Some perspectives on the use of management science techniques in R&D management’, R&D Management, 16, 4, 1986, pp. 291, 296.

9. For elaboration of these points see T. E. Clarke, ‘Decision making in technologically based organizations: A literature survey of present practice’, IEEE Transactions on Engineering Management, EM-21, 1, February 1974, pp. 9–23.

10. See Clarke, op. cit.; B. Twiss, Management of Technological Innovation, 2nd ed., Longman Group Ltd., London, 1980.

11. For further discussion see J.R. Moore and N.R. Barker, ‘Computational analysis of scoring models for R&D project selection’, Management Science, 16, 6, December 1969, pp. B212-32.

12. See, e.g., A. Albala, ‘Stage approach for the evaluation and selection of R&D projects’, IEEE Transactions on Engineering Management, EM-22, 4, November 1975, pp. 153–163.

13. See Australian Bureau of Statistics, Research and Experimental Development All-Sector Summary, Australia 1984-85, Canberra, AGPS, 1987. Basic Research is experimental or theoretical work undertaken to acquire new knowledge, regardless of practical applications. Pure basic research is devoted to the advancement of knowledge per se. Strategic basic research is that directed into specified fields in the expectation of acquiring knowledge to solve recognized practical problems. For the most part basic research carried out by Australian industry is of a strategic, ‘mission oriented’ sort. Here the emphasis is on experimental or theoretical work directed into specified broad areas in the expectation of useful discoveries. Activities comprising this stage include long run research and the exploratory development of basic technologies. Applied Research is that undertaken to acquire new knowledge with a specific application in view, e.g., to determine possible uses for the findings of basic research or to determine new methods of achieving practical objectives. This stage includes research of a tactical or problem-oriented sort such as the identification of existing or potential processes and products, and their suitability for adaptation by the firm, patent surveys, studies of actual and potential resource constraints and bench scale research to determine technical parameters. Experimental Development is systematic work toward the creation of new or improved materials, devices, products, processes, systems or services. This stage typically involves the construction and operation of a prototype or pilot plant.

14. We may usefully distinguish between four different types of risk: i) real risk, as will be determined by future circumstances; ii) statistical risk, as determined by currently available data typically as measured actuarially for insurance premium purposes; iii) predicted risk, as predicted analytically from systems models structured from historical studies; and iv) perceived risk, as seen intuitively. For elaboration see C. Starr, R. Rudman and C. Whipple, ‘Philosophical basis for risk analysis’, Annual Review of Energy, 1, 1976, pp. 629-62. For the sorts of decisions made with respect to R&D project selection we can think of their ‘riskiness’ as based on perceived risk. As one of the referees has pointed out, the prevailing ‘organizational culture’ will affect the perception of risk/uncertainty attached to an R&D project. Following Kasper we can define organizational culture as “the fundamental value-and-belief perceptions, of thought patterns which filter perceptions, control the interpretations behaviour and actions of the organization members and which, in retrospect, serve as the horizon for justifications’ (H. Kasper, ‘Organisational-cultural aspects of the promotion of a favourable climate for innovation’, in H. Hubner (ed.), The Art and Science of Innovation Management, Elsevier Science Publishers, Amsterdam, 1986, p. 48). We can readily appreciate how organizational culture influences the perceived riskiness of alternative R&D projects.

15. The stages of basic research, applied research, experimental development and commercial investment can be thought of either as stages in the life cycle of a typical product or, alternatively, as different project types.

16. E. Mansfield, ‘How economists see R&D ’, Harvard Business Review, 59, 6, November-December 1981, p. 102. As one of the referees emphasizes, the cost of reversibility of the project must be considered. If the project is irreversible or reversible only at substantial cost then initial investment is likely to be lower. On the other hand, a larger initial investment may be justified if it is expected to yield significant information on the particular project or on other projects. These sorts of considerations point up the complexities of R&D decision-making and suggest that over-simplified pictures, such as Figure 1, must be treated with caution.

17. cf. Albala, op. cit.

18. A leading critic in this area is William E. Souder. The contents of this section are heavily indebted to Souder's views contained in Souder, 1978, op. cit.; Souder and Mandakovic 1986, op. cit.; W. Souder, Achieving organizational consensus with respect to R&D project selection criteria’, Management Science, 21, 6, February 1975, pp. 669-681; W. Souder, ‘Effectiveness of nominal and interacting group decision processes for integrating R&D and marketing’, Management Science, 23, 6, February 1975, pp. 595–605.

19. See Souder and Mandakovic, op. cit.

20. Mansfield, op. cit., p. 101.

21. For a useful overview of both methods see Souder and Mandakovic, op. cit

22. For a more detailed discussion see Souder, 1977, op. cit.; Souder, 1978, op. cit.

23. Field studies suggest that this sort of behavioural decision aid can fill the need for a structured problem analysis and team building forum for organizational project selection and evaluation. See, e.g., W. Souder, ‘Field studies with a Q-sort/nominal-group process for selecting R&D projects’, Research Policy, 4, 1975, pp. 172-88. Further results indicate that consensus and collaboration problems between R&D and marketing may be alleviated by replacing interacting decision-making processes, which are typically used by many organizations, with a combined nominal-interacting process. For details see Souder, 1977, op. cit.

24. D. Kocaoglu, ‘A participative approach to program evaluation’, IEEE Transactions on Engineering Management, EM-30, 3, August 1983, pp. 112–118.

25. e.g., Souder and Mandakovic, op. cit.

26. Souder and Mandakovic, op. cit., p. 41.

27. There is ongoing research into the structural characteristics of innovative and creative organizations and the characteristics of an innovative and creative organizational climate. A recent paper by Link and Zmud compares innovative efficiency as proxied by a measure of R&D efficiency between firms with organic and mechanistic R&D organizational structures where ‘structure’ refers to information flows and decision-making channels (A.N. Link and R.W. Zmud, ‘Organization structure and R&D efficiency’, R&D Management 16, 4, 1986, pp. 317-23.) The authors’ results support the hypothesis that organic structures (those more open to individual initiative and discretion) experience greater efficiency in basic research, process related R&D, and long-term R&D activities, than do mechanistic structures. The authors suggest that further research should begin to explore the effectiveness of alternative managerial strategies for creating appropriate organizational structures for R&D groups.

28. A referee reminds us that Kenneth Arrow has highlighted the tradeoff between economies of scale and gains from specialisation in monitoring the external environment and internal communication costs. See, e.g., K. Arrow, The Limits of Organization, W. W. Norton and Co., New York, 1974. The design of complex organizations with their multiple tasks and divergent information needs requires a greater understanding of the influence of different organizational strategies on information flows and the effectiveness of such exchanges. While some useful results have been obtained, for example, R. Katz and M. Tushman, ‘Communication patterns, project performance and task characterists: An empirical evaluation and integration in an R&D setting’, Organizational Behaviour and Human Performance, 23, 1979, pp. 139-162, more research needs to be undertaken to determine the influence of formal organizational structures on communication patterns and its relevance for the use of organizational decision methods in R&D project assessment.

29. Souder and Mandakovic, op. cit., p. 41.

30. See, for example, the results of the P.A. Technology Survey, ‘Attitudes to New Technology — An International Survey’, conducted by Market Opinion Research International for P.A. Technology Australia, Spring 1984. More than 200 senior executives in Australia, USA, West Germany, Britain and Japan in five industrial sectors, viz, engineering equipment/machines, medical equipment/disposables, domestic electrical appliances, industrial materials/chemicals and food/beverages, were questioned on their attitudes to technology. The survey shows that Australian business managers typically underestimate the strategic importance of technological investment and resources by comparison with their counterparts overseas. In particular, 60 per cent of the executives sampled in Australia reported that their firms monitor technology primarily by literature scans, while 26 per cent did so by staff attendance at seminars.

31. A. Clarke, ‘Issues in applying university-based research’, Science, Technology and the Economy, University of NSW Occasional Papers, No. 11, Sydney: University of NSW, 1986.