Research supported in part by NSF grants DMS—9208758 and SBR—9529926 to the National

Abstract not found

Despite much research, debate continues about the impact of risk taking on a firm’s future performance. Unlike prior studies, we propose that risk-return relationships evolve as firms age and learn, particularly in high-velocity settings where accumulated knowledge affects how firms respond to technological change. Discerning this requires three things absent from prior analyses: (1) studying an entire population; (2) modeling evolutionary processes; and (3) using separate models to capture how a firm’s gains and losses (i.e., its strong and weak performances) unfold across time. Using this framework, we found that (a) risk-return relationships generally evolved from positive to negative as firms aged; because (b) firms learned to avoid large losses at younger ages than they learned to sustain large gains; yet (c) the risk taking that followed below-aspiration performance moderated those effects such that major setbacks prompted large future gains and large future losses among older firms and downward spirals among younger ones. 1 Relationships between risk and return are central to our lives. In the hope of emotional or monetary rewards, some people take risks by climbing mountains, changing employers, or switching careers. Some executives take risks in pursuit of better pay and enhanced reputations, and some firms pursue risky strategies in a quest for higher sales and profits.

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Abstract—Studies analyzing the impact of technological innovations on how an industry develops focus on the different effects produced by radical innovations versus incremental innovations. The studies either examine the technology cycles or the market development cycles in isolation without really explaining the interaction between the two. In this paper, we present a model of industry development that takes into account the dynamics of innovation in the industry accentuated by the interplay between technology development cycles and application development cycles. The model proposes that these two cycles go through a linked, predictable double helix pattern. The personal computer (PC) industry is used as an example of this model’s operation, as the PC industry has gone through three distinct technology cycles (8-b, 16-b, and 32-b processors) and corresponding three distinct application cycles. We draw some important conclusions about how companies can use this understanding to develop better technology and market strategies to succeed in the various stages of the industry’s development.

Despite a voluminous literature and widespread belief in the inherent value of innovation, our understanding of the innovation construct remains rudimentary. Extant research on innovation spans across a variety of disciplines and levels of analysis. Yet, the divergence in core assumptions and methodologies across disciplines, coupled with few systematic efforts to integrate findings, have seriously hampered the crossfertilization of research ideas in the field of innovation. The motivation for this work stems from the fragmentary nature of innovation research and the need for a more holistic, ecumenical outlook on innovation. We believe that this paper takes a step toward overcoming disciplinary myopia and a further step toward synthesizing the diverse innovation literature. In this paper, we provide a synopsis of the innovation literature, with an emphasis on how innovation may potentially be reframed to develop a more comprehensive understanding of the construct. To this end, the paper is structured as follows. First, we outline various semantic definitions of innovation and their lexical variants. We then briefly discuss three confluent, but distinct terms (creativity, invention and organizational change) often mistakenly perceived to be synonymous with innovation. Next, we delineate the levels of analysis associated with innovation research and describe exemplary research emerging from these domains. Finally, we identify a number of promising research directions and propose an integrative cross-level model of innovation to energize future innovation research. 3

doi 10.1287/orsc.1090.0486