Innovation is becoming increasingly popular as a concept as well as a field of research. As a field, it has accumulated a significant amount of scientific knowledge. Based on bibliometric data from four major social science disciplines-economics, sociology, psychology, and management-this study presents a 'global view' of the field by combining longitudinal and structural perspectives. It identifies major research traditions in the field, determines the content and disciplinary composition of each tradition, and maps the changes in the intellectual structure of the field over time. The study suggests that innovation research is becoming increasingly compartmentalized between economics and management disciplines and each segment is becoming increasingly selfcontained. A strategy along with a framework is suggested for making research contribution to the field. 65 8 The citation and co-citation matrices represent unweighted/ binary and weighted networks, respectively. There are two possible approaches to analyze this data; standard multivariate analysis and network analysis. The data for this study, in both forms, involved a significant amount of overlap across disciplines. In network analysis, this problem pertains to the detection of overlapping communities, which is only beginning to be explored (Fortunato, 2010). The use of the network approach in this case would require a combination of several network analytical algorithms to complement each other. The complexities and limitations of this approach necessitated the use of more compact methods which are well-known, widely used, and approved by the concerned scientific community. 9 The list of top 100 publications is given in Table B1 in Appendix B. Incidentally, these publications included the 50 most cited publications in each period. It is worth noting that this set includes 18 of the 'top twenty contributions' and 44 of the 'core innovation literature' identified in a similar bibliometric study of six handbooks and textbooks of innovation by Fagerberg and Sapprasert (Innovation: exploring the knowledge base.

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Pipelined MPSoCs provide a high throughput implementation platform for multimedia applications, with reduced design time and improved flexibility. Typically a pipelined MPSoC is balanced at design-time using worst-case parameters. Where there is a widely varying workload, such designs consume exorbitant amount of power. In this paper, we propose a novel adaptive pipelined MPSoC architecture that adapts itself to varying workloads. Our architecture consists of Main Processors and Auxiliary Processors with a distributed run-time balancing approach, where each Main Processor, independent of other Main Processors, decides for itself the number of required Auxiliary Processors at run-time depending on its varying workload. The proposed run-time balancing approach is based on off-line statistical information along with workload prediction and run-time monitoring of current and previous workloads' execution times. We exploited the adaptability of our architecture through a case study on an H.264 video encoder supporting HD720p at 30 fps, where clock-and power-gating were used to deactivate idle Auxiliary Processors during low workload periods. The results show that an adaptive pipelined MPSoC provides energy savings of up to 34% and 40% for clock-and power-gating based deactivation of Auxiliary Processors respectively with a minimum throughput of 29 fps when compared to a design-time balanced pipelined MPSoC.

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