A plethora of definitions for innovation types has resulted in an ambiguity in the way the terms 'innovation' and 'innovativeness' are operationalized and utilized in the new product development literature. The terms radical, really-new, incremental and discontinuous are used ubiquitously to identify innovations. One must question, what is the difference between these different classifications? To date consistent definitions for these innovation types have not emerged from the new product research community. A review of the literature from the marketing, engineering, and new product development disciplines attempts to put some clarity and continuity to the use of these terms. This review shows that it is important to consider both a marketing and technological perspective as well as a macrolevel and microlevel perspective when identifying innovations. Additionally, it is shown when strict classifications from the extant literature are applied, a significant shortfall appears in empirical work directed toward radical and really new innovations. A method for classifying innovations is suggested so that practitioners and academics can talk with a common understanding of how a specific innovation type is identified and how the innovation process may be unique for that particular innovation type. A recommended list of measures based on extant literature is provided for future empirical research concerning technological innovations and innovativeness.

A plethora of definitions for innovation types has resulted in an ambiguity in the way the terms 'innovation' and 'innovativeness' are operationalized and utilized in the new product development literature. The terms radical, really-new, incremental and discontinuous are used ubiquitously to identify innovations. One must question, what is the difference between these different classifications? To date consistent definitions for these innovation types have not emerged from the new product research community. A review of the literature from the marketing, engineering, and new product development disciplines attempts to put some clarity and continuity to the use of these terms. This review shows that it is important to consider both a marketing and technological perspective as well as a macrolevel and microlevel perspective when identifying innovations. Additionally, it is shown when strict classifications from the extant literature are applied, a significant shortfall appears in empirical work directed toward radical and really new innovations. A method for classifying innovations is suggested so that practitioners and academics can talk with a common understanding of how a specific innovation type is identified and how the innovation process may be unique for that particular innovation type. A recommended list of measures based on extant literature is provided for future empirical research concerning technological innovations and innovativeness.

Managers need guidance on how to cope with turbulent environments in order to improve corporate performance. Research on environmental turbulence has suggested that firms adopt a less centralized, more organic structure in dynamic, uncertain environments. Little work has been done specifically, however, on how environmental turbulence affects strategy planning for new product development (NPD). In this article, we specify a baseline model with firm innovativeness, market orientation and top management risk taking as antecedents to NPD speed and corporate strategic planning; these in turn are modeled as antecedents to NPD program (not project) performance. Two conceptualizations of the role of environmental turbulence are examined: (1) that market turbulence and technological turbulence are additional direct antecedents to NPD program performance; and (2) that the baseline model is moderated by turbulence (that is, that the strengths of the paths differ depending on levels of turbulence). A crosssectional survey methodology including four diverse industries [automotive, electronics, publishing, and manufacturing/research and development (R&D) laboratories] was used to test the hypotheses. The latter conceptualization is supported. In particular, the paths from innovativeness to strategic planning and from risk taking to NPD speed are significantly greater in highly turbulent environments. A set of managerial recommendations and implications are provided. First, managers must recognize the possible improvements in new product performance by actively including NPD personnel in corporate strategic planning and also by involving corporate planners in NPD activities. Second, managers also should recognize that turbulent environments heighten the need to make risky investments, and sometimes, risky decisions; risk-taking decisions ought to be encouraged in such environments.

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Little has been written in the new product development literature about the simulation technique agent-based modeling, which is a by-product of recent explorations into complex adaptive systems in other disciplines. Agent-based models (ABM) are commonly used in other social sciences to represent individual actors (or groups) in a dynamic adaptive system. The social system may be a marketplace, an organization, or any type of system that acts as a collective of individuals. Agents represent autonomous decision-making entities that interact with each other and/or with their environment based on a set of rules. These rules dictate the behavioral choices of the agents. In these simulation models, heterogeneous agents interact with each other in a repetitive process. It is from the interactions between agents that aggregate macroscale behaviors or trends emerge. The simulated environment can be thought of as a ''virtual'' society in which actions taken by one agent may have an effect on the resulting actions of another agent.This article is an introduction to the ABM methodology and its possible uses for innovation and new product development researchers. It explores the benefits and issues with modeling dynamic systems using this methodology. Benefits of ABMs found in sociology and management studies have found that as the heterogeneity of individuals increase in a system or as network effects become more important in a system, the effectiveness of ABMs as a methodology increases. Additionally, the more adaptive a system or the more the system evolves over time, the greater the opportunity to learn more about the adaptive system using ABMs. Limitations to using this methodology include some knowledge of computer-programming techniques.Three potential areas of research are introduced: diffusion of innovations, organizational strategy, and knowledge and information flows. A common use of ABMs in the extant literature has been the modeling of the diffusion process between networked heterogeneous agents. ABMs easily allow the modeling of different types of networks and the impact of these networks on the diffusion process. A demonstrative example of an agent-based model to address the research question of how should manufacturers allocate resources to research (exploration) and development (exploitation) projects is provided. Future courses of study using ABMs also are explored.Ã I wish to sincerely thank Paul Rummel for his coding expertise in building this model and Amit Joshi for his research assistance in testing proofof-concept for several different ABMs.