International Journal of Innovation Management

This paper compares actual (A-type) software processes at work in flight simulation, one example of a software-intensive complex product system, with ideal, rational (B-type) processes as contained in company manuals, tools and procedures. The aim is to identify the causes and consequences of divergencies between A- and B-type processes in a complex product, and to draw implications for theory and practice. The paper also develops a simple partial model to show what A-type processes actually "look like" in practice. The evidence indicates that in response to industrial turbulence, uncertainty, technical complexity and difficulties in capturing user requirements, software engineers and project managers fall back heavily on A-type informal systems and "soft" management becomes essential to project success. Success under such circumstances, B-type rational systems are inadequate to the task at hand and soft factors such as goodwill, negotiation skills, rule breaking and informal communications become essential to project success. These findings contrast with most approaches to software engineering which try to impose highly rational processes and tend to ignore soft issues. The paper also indicates potential benefits of the rational approach, sometimes overlooked by contemporary organisational scholars, by arguing that it is the manner in which B-type processes are developed and implemented which leads to divergencies and difficulties, rather than the processes themselves which are an essential part of orderly progress. While the findings may not be relevant to more simple products and tasks, other complex product systems may well face similar divergencies and project management challenges.

This paper examines the changing composition of technological opportunities and corporate leadership that affected the major British and German pharmaceutical companies from 1930 to 1990. It draws on both the theory of technological change and Schumpeter's theory of profits and growth. Evidence is derived from US patent statistics for 15 indigenous British and German pharmaceutical companies in 47 technological activities. The findings suggest that shifts in both technological leadership and opportunities tend to be gradual rather than radical. The diffusion of technology reduces, but does not fully erode, firm-specific capabilities. However, well-established leaders risk becoming locked into declining technologies. Nevertheless, the composition of leadership in the pharmaceutical industry often persists for several decades. More recent changes, though, suggest that the once dominant group of leaders is on the verge of being replaced by a different group, to the extent that the shifting cycle reflects a changing technological paradigm.

This paper examines the attitudes of scientists in four research institutes within the UK Biotechnology and Biological Sciences Research Council towards links with industry. Interviews were conducted with scientists who ranged in their level of industrial contact from none at all to a high level of activity. They were classified into five groups by their attitudes and labelled as Enthusiastic, Interested, Prepared to be Supported, Disinterested and Academic High Ground. These attitudes are related to the level of activity. As expected, there was a relationship between attitude and level of activity. The first two groups had a high level of links, and the second two few. Those taking the Academic High Ground often had very good contacts with industry, despite their clear view of their academic and scientific roles.

Traditional linear models of innovation, in which it is assumed that government funding of R&D will axiomatically create innovative industries, have become obsolete. This paper suggests that innovation is in fact a complex and interactive process that involves cycles of learning. However, these "knowledge cycle" models of innovation are largely unvalidated empirically and further research will be required to extend the current theory. Knowledge cycle models appear to be superior to the linear models for a number of reasons. These include major roles for tacit (uncodified) knowledge as well as explicit (codified) knowledge, which are largely ignored in linear innovation models. An emphasis upon iterative collaboration between firms and researchers and the importance of firms participating more directly in the research and development (R&D) process may enhance the utilisation of these forms of knowledge.