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There is nothing more diﬃcult to plan, more doubtful of success, nor more dangerous to manage than the creation of a new order of things …
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Niccolo Machiavelli, The Prince 1961 
Despite the attention paid to innovation in the 1990s, which is evident in the increased number of books and articles, the theory itself seems to have changed little since the 1970s. Drazin and Schoon-hoven (1996) concluded that research on innovation still has an adaptationist perspective. Its three basic assumptions are ‘(a) innovation is universally desirable for organizations, (b) once an organization increases its size beyond a critical mass it becomes more inert, less capable of meaningful organizational change, and only haltingly proﬁcient at innovation, and (c) certain structures and practices can overcome inertia and increase the generation rate of innovation.’ While this assessment may be accurate overall, it fails to take into account that the development of multilevel models and associated sophisticated statistical modeling and analysis techniques may ultimately lead to incremental or even radical change in innovation theories.
This research paper reviews the current state of research on innovation and points out pathways for new insights where researchers incorporate ﬁndings from learning and institutional theories into multilevel innovation models. In particular, diﬀusion of innovation models has much to gain from combining traditional trait with context theories and from modeling eﬀects simultaneously. Incorporating more sophisticated theories of power and organizational memory may further revitalize research on generating and managing innovation in organizations. It is important to keep in mind that the importance of innovation for organizational success and survival may be greatly exaggerated or even mythical as von Braun’s (1997) analysis of Research and Development (R&D) activities in highly industrialized nations shows. Indeed, he calls the focus on industrial innovation during the post-Cold War the Arms Race of the ’90s.
Deﬁnitions vary in the extent to which they distinguish between that which is objectively or subjectively new. Some authors call only successful commercial exploitation of new ideas an innovation, while others do not distinguish between a novel idea as an invention and commercial innovation. For the latter any ‘idea, practice, or object that is perceived as new by an individual or other unit of adoption … is an innovation’ (Rogers 1995). Even if one were to distinguish strictly between invention and innovation, the question to whom something is new or not, still remains. Is the exploitation of a new idea only an innovation once, when it is ﬁrst discovered? Or is the pacemaker still an innovation in Germany, even though it is already widely being used in Australia or the US? Rogers (1995) prefers a broad understanding of the concept. He terms any idea or practice that is new to an adopter as an innovation. Thus, even where a practice such as activity-based cost accounting (ABC) is used within an industry, for a company that is newly adopting ABC, ABC is an innovation.
2. Types Of Innovations
Innovations can be classiﬁed along a variety of dimensions. The most common typology distinguishes among product, process, and, more recently, also service innovations. Product innovations refer to changes in the oﬀerings of an organization, for example, the introduction of CD and later DVD players. Process innovations refer to changes in the manufacturing or delivery of such oﬀerings, for example, Just-in-Time Manufacturing or Total Quality Management (TQM) (JIT). The distinctions between product and process innovations, however, seem to become blurred when it comes to service innovations, since product and process go together in the production and delivery of a service. Especially among the process innovations one could also distinguish between technological and administrative procedures. Does the change involve a new technology of transforming input into output or does it involve a new way of administering or organizing the process using existing technologies?
Another typology (e.g., Anderson and Tushman 1990) classiﬁes innovations by the extent to which they rely on existing competencies or change existing products or processes. At one extreme are incremental, at the other extreme are radical innovations. Radical innovations are those that lead to hitherto unheard of products or processes. For example, enlarging the capacity of 5 inch disks to provide 20 MB of memory involved incremental changes, while developing the 3 inch disks represented a radical innovation. Radical innovations often involve competency-destroying changes and are associated with organizational reorientation (cf. Tushman and Rosenkopf 1996).
3. Models Of Innovation
Brown and Eisenhardt’s (1995) review distinguishes between rational plan, communication web, and disciplined problem-solving approaches to product development. The main diﬀerence among the various models concerns the underlying assumptions about the overall process, that is, whether it is rationally planned or evolves over time.
3.1 Rational Processes
Rational models assume that innovation proceeds along a strategic planning process involving information gathering, analysis and evaluation, and action. According to the rational perspective successful innovation is ‘the result of (a) careful planning of a superior product for an attractive market and (b) the execution of that plan by a competent and well-coordinated cross-functional team that operates with (c) the blessings of senior management’ (Brown and Eisenhardt 1995). Practitioner-oriented prescriptions sometimes list up to 16 steps in the strategically planned, rational product development process ranging from initial screening to detailed market studies, trial production, and ultimately market launch (Cooper 1993). Thus, it seems that the rational process is highly complex, especially if one were to consider turbulent or dynamic environments in the market assessments. Nonetheless, rational models can be found in many organizations and have inspired a large number of research projects. Summarizing research on successful and failed product innovations that had followed a rational plan Brown and Eisenhardt (1995) list product advantages, market attractiveness, and internal organization as the main factors for successful, rational product innovation.
3.2 Evolutionary Models
Underlying evolutionary theories is a repeated variation-selection-retention cycle. Evolutionary theories assume that variation—diﬀerent ways of doing something—exists within any environment and that managers then selectively retain a product or process from such variation and thereby innovate within their organization. For example, organizations may selectively retain an administrative innovation such as idiosyncratic jobs (Miner 1991) and thereby change their processes and products over time.
3.3 Linking Organizational And Industry Innovation
Most innovation models work on two levels. At the organizational level, innovation is a means of organizational adaptation to changing environments. However, organizational innovation can also be a means of industry evolution (e.g., Anderson and Tushman 1990; Greve et al. 1995). In fact, the link between organizational innovation and industry evolution follows a well-established research tradition ranging from waves of creative destruction models (Schumpeter 1934) to organizational learning and population level learning (Miner and Haunschild 1995). Even theories, such as those of population ecology, whose focus is on the population rather than individual organization level entities, are innovation models at heart. Population ecologists argue that some new organizational forms are more likely to survive than others (e.g., Hannan and Freeman 1989) and thereby ultimately lead to innovation within a population of organizations. The linkage between organizational innovation and industry evolution is particularly evident in punctuated equilibrium and technology cycle theories.
3.4 Punctuated Equilibrium And Technology Cycle Models
Proponents of punctuated equilibrium theory suggest that organizations ‘evolve through alternating periods of convergence and reorientation’ (Tushman and Rosenkopf 1996), that is, alternating periods of incremental and radical innovations. Instability and reorientation punctuate stable, convergent periods. Environmental change stimulates technological breakthrough innovations which disrupt stable patterns of interaction and power relations, and thereby lead to organizational or industry-wide reorientation and divergence. Empirical studies have shown that reorientation and turbulence may stem from technological, legislative or wartime jolts, or performance crises and cannot be predicted. After the implementation of radical innovations in the reorientation periods, incremental innovations dominate the convergence periods. These convergence periods can vary dramatically in length.
Technology cycle theories (e.g., Anderson and Tushman 1990) similarly point to alternating periods of radical and incremental change, but mainly operate at the industry level. Tushman and co-workers show that periods of increased discontinuities in an industry alternate with periods of increased fermentation. Periods of discontinuities experience design competition and substitution, while periods of incremental change follow the emergence of a dominant design and are characterized by elaboration of dominant designs. Decisions about a dominant design are not necessarily technology-driven but involve social or political processes which ‘abjudicate among multiple technological possibilities’ (Anderson and Tushman 1990). Thus, the ultimate dominant design, ‘is an outcome of the social or political dynamics of compromise and accommodation between actors or unequal inﬂuence.’
Tushman and Anderson (1986) use data from the minicomputer, cement, and airline industries to study technological innovation in those industries. When they diﬀerentiate between competence-enhancing and competency-destroying technologies, they notice an interesting pattern. Apparently, competence-destroying technologies stem from new players, that is, inﬂuences exogenous to the industry, while competence-enhancing technologies are developed by existing ﬁrms, that is endogenous forces. Thus, certain variation may result from environmental change, while other variation grows out of seeds planted within the industry.
4. Diﬀusion Of Innovation
The heterogeneous diﬀusion model by Strang and his co-workers (Greve et al. 1995) brings together two distinct conceptual models of diﬀusion processes and allows simultaneous analysis of multilevel eﬀects. While Strang and his co-workers model organizational and network inﬂuences, the model itself could also be applicable to reconcile research streams that focus on speciﬁc traits of innovations with those that focus on contextual variables, either at the individual adopters, the organizational, or the population level.
4.1 Trait Models
One stream within the diﬀusion of innovation literature focuses on the traits of particular innovations. Rogers’s (1995) ﬁve-factor model is a well-known example. The ﬁve factors are relative advantage, compatibility, complexity, trialability, and observability. Relative advantage refers to ‘the degree to which an innovation is perceived as being better than the idea that it supersedes’ (Rogers 1995). Compatibility is the ‘degree to which an innovation is perceived as consistent with the existing values, past experiences, and needs of potential adopters’ (Rogers 1995). Some innovations are very simple and easy to implement, while others are complex, consist of several inter-related subprocesses, and may be diﬃcult to implement in an organization. Rogers (1995) refers to this as the complexity of an innovation, which he deﬁnes as the ‘degree to which an innovation is perceived as relatively diﬃcult to understand and use.’ According to Rogers, managers are more likely to adopt an innovation after they had a chance to experiment with the innovation. Rogers (1995) refers to this characteristic as trialability and deﬁnes it as ‘the degree to which an innovation may be experimented with on a limited basis.’ Last, but not least, being able to observe the experiences of another organization or individual also has positive eﬀects for the diﬀusion of an innovation, a concept that Rogers calls observability.
4.2 Institutional Models
Another stream of diﬀusion research is framed within institutional concepts. This research stream suggests that managers not only consider the traits of an innovation, but also the context. As much of the neo- institutional research has shown, managers do not only consider the instrumental outcomes of a practice, but also the noninstrumental outcomes such as broad, increased legitimacy and isomorphism with the generally accepted practices of the industry or population of organizations (cf. Powell and DiMaggio 1991).
One of the major claims of institutional theorists is that organizational survival depends on legitimacy deﬁned as ‘a generalized perception or assumption that the actions of an entity are desirable, proper, or appropriate within some socially constructed system of norms, values, beliefs, and deﬁnitions’ (Suchman 1995). Managers have an important role in building, increasing, and maintaining organizational legitimacy. For example, they may only adopt innovations that are legitimate and enhance the organization’s legitimacy. Empirical research shows that managers copy innovations from large organizations or from organizations personally known to them. Moreover, managers are likely to implement innovations that have been endorsed by exogenous institutions or have become part of professional knowledge (cf. Powell and DiMaggio 1991).
Adapting Strang and co-workers’ multilevel heterogeneous diﬀusion model may lead to new insights into the diﬀusion of innovation research by distinguishing the relative impact of traits and organizational or population contexts. Using such a model and the associated methodologies would also allow the incorporation of individual decision-makers’ inﬂuences into the modeling, for instance, their preferences and personalities such as risk-taking or risk avoiding.
5. Enhancing Innovation In Organizations
The literature on facilitating innovation in organizations falls into two streams. One stream looks at introducing and managing processes that might lead to innovation, while the other examines speciﬁc factors that may enhance or impede the innovativeness of an organization. Among those innovativeness factors one could further distinguish between factors that aﬀect the development of particular products and those that operate on the organization as a whole. Lately, the relationship between innovation and strategy has been emphasized, for instance, in research on the eﬀect of mergers and acquisitions, exiting certain niches or transforming an inert mature organization into an innovative one.
Rothwell (1994) has written comprehensive reviews on success factors, strategies, and trends for innovativeness. For example, he lists good internal and external communication, eﬀective linkages with external sources of know-how, treating innovation as a company-wide task, high quality production, careful planning and control systems, strong market orientation, top management commitment, and long-term commitment to major projects among other critical elements of successful innovation strategies. Dougherty and Hardy (1996) reinforce Rothwell’s (1994) ﬁndings, but also add the importance of managing power in product development. Going beyond the eﬀect of powerful individuals such as powerful product champions and powerful team leaders, they introduce multi-dimensional views on power. They suggest that generating successful sustainable innovation involves managing power on all dimensions, that is, managing resources, processes, and meaning.
Knowledge and learning theories provide further insights. In particular, applying organizational memory concepts seems fruitful. For example, Moorman and Miner (1998) consider the eﬀect of procedural and declarative memory on improvization where improvization represents one form of creative activity in product development teams. Many novel designs consist of a recombination of existing parts, processes, and routines, that is, they result from improvization based on a recombination of knowledge stored in memory.
6. Critical Views On Innovation
Lately, critical views questioning the generally assumed adaptive capacity of innovation have emerged. For instance, von Braun (1997) writes that increased R&D spending in OECD countries has not necessarily led to progress, but simply to change. According to him some ancient wind-powered drainage systems may be characterized as more progressive or advanced than today’s diesel or electrically-driven pumps in regard to eﬃcient energy utilization. He especially draws attention to the Acceleration Trap and R&D Spiral. While his analyses are somewhat general, it is interesting to read that ‘it proved diﬃcult, if not impossible to discover a positive relationship between R&D growth and sales growth. This held true even if one assumed a delayed eﬀect of R&D spending increase on sales growth’ (von Braun 1997). Contrary to the generally portrayed positive image of accelerating product development and then managing product releases carefully, von Brown points out that the relationship between accelerated product development and shortened product life cycles is highly complex in competitive environments. Von Braun suggests unanticipated negative impacts on overall organizational revenue and survival and ﬁnishes his book with recommendations on how to avoid falling into the acceleration trap.
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