Communities, Knowledge, and Innovation: Indian Immigrants in the US Semiconductor Industry

This paper investigates the influence of technological, geographic, and ethnic communities on the innovativeness of Indian inventors. We study Indian inventors in the semiconductor industry in the US and examine their patenting profiles between 1975 and 1999 to identify the influences on the quantity and quality of their innovations. We find that inventors who rely on knowledge from technological and geographic communities enhance their innovativeness. Knowledge from the ethnic Indian community is related to inventor innovativeness in the form of an inverted U. The negative effect of knowledge gained from the ethnic community on innovativeness is pronounced for experienced inventors.innovation, knowledge, semiconductor industry

Intangible resources, agglomeration effect of FDI intensity, and firm performance: Evidence from Chinese semiconductor firms

This study analyzes the impact of intangible resources on firm performance in an emerging economy context. Intangible resources are considered essential to firms? competitive advantage; however, we argue that firms? intangible resources can be negatively related with performance in emerging economies, due to their weak intellectual property rights protection. Furthermore, we incorporate the resource-based view and geographical agglomeration perspective to propose that geographical locations with dense foreign direct investment can affect the appropriability of intangible resources, thereby moderating the relationship between intangible resources and firm performance. We find empirical evidence to support our argument by examining 70 semiconductor firms in China from 1999 to 2006 period.intangible resources, intellectual property, agglomeration, foreign direct investment, emerging economy

Developing the ecletic paradigm as a model of global strategy: an application to the impact of the Sep. 11 terrorist attacks on MNE performance levels

We expand the eclectic paradigm into a model of global strategic management and apply the latter to the analysis of the impact of the Sep. 11th terrorist attacks on the MNEs’ performance to investigate the effect of exogenous shocks on the global strategies of firms. First, we integrate MNE resources and capabilities, strategy, and structure with the eclectic paradigm. Then we focus specifically on location attractiveness to examine how MNEs adjust internal factors with the exogenous distortions caused by an extreme environmental shock. We suggest that this adjustment is carried out at four levels: resources and capabilities, strategy, structure, and choice of location which jointly determine MNEs’ performance. Although we restrict the application of this model of global strategic management to the post-Sep. 11th, our model may be applied to other extreme events that change, at least partly, the worldwide, or regional, economic order

Catch-up Via Agglomeration: A Study of Township Clusters

This study examines whether the premises of inter-firm competition and cooperation on cluster performance outcome hold true in the context of China. By examining 87 township clusters in Jiangsu Province, we find that cluster performance is co-determined by the intensity of inter-firm competition and cluster innovativeness. Our results also show that the cluster\u27s competitive intensity mediates the relationship between cluster size and cluster performance, and that a cluster\u27s R&D centers and inter-firm joint actions positively affects a cluster\u27s innovativeness, which in turn contributes to cluster performance. These findings not only provide additional support for strategy theories about clusters in a new context, but also shed novel insights into the unique phenomenon of Chinese township clusters

Supplier-Switching Inertia and Competitive Asymmetry: A Demand-Side Perspective

Building on strategic management, operations strategy, and supplier management literatures, this article presents a framework for supplier selection from the demand-side perspective. We highlight the role of a purchasing firm’s switching inertia in the supplier selection process and demonstrate the usefulness of our framework for the industrial automation industry. Empirical data for this study was collected from 171 corporate and plant-level executives in pharmaceutical, chemical, and paper-and-pulp manufacturing industries in the United States. A series of Web-based individually customized discrete choice experiments asked the respondents to either switch to the new supplier or stay with the existing supplier. Based on the results of these experiments, we demonstrate the existence of switching inertia in the supplier-selection process and discuss the managerial implications for incumbent and challenger supplier firms

Fast Tree Search for Enumeration of a Lattice Model of Protein Folding

Using a fast tree-searching algorithm and a Pentium cluster, we enumerated all the sequences and compact conformations (structures) for a protein folding model on a cubic lattice of size $4\times3\times3$. We used two types of amino acids -- hydrophobic (H) and polar (P) -- to make up the sequences, so there were $2^{36} \approx 6.87 \times 10^{10}$ different sequences. The total number of distinct structures was 84,731,192. We made use of a simple solvation model in which the energy of a sequence folded into a structure is minus the number of hydrophobic amino acids in the ``core'' of the structure. For every sequence, we found its ground state or ground states, i.e., the structure or structures for which its energy is lowest. About 0.3% of the sequences have a unique ground state. The number of structures that are unique ground states of at least one sequence is 2,662,050, about 3% of the total number of structures. However, these ``designable'' structures differ drastically in their designability, defined as the number of sequences whose unique ground state is that structure. To understand this variation in designability, we studied the distribution of structures in a high dimensional space in which each structure is represented by a string of 1's and 0's, denoting core and surface sites, respectively.Comment: 18 pages, 10 figure

Note on cubature formulae and designs obtained from group orbits

In 1960, Sobolev proved that for a finite reflection group G, a G-invariant cubature formula is of degree t if and only if it is exact for all G-invariant polynomials of degree at most t. In this paper, we find some observations on invariant cubature formulas and Euclidean designs in connection with the Sobolev theorem. First, we give an alternative proof of theorems by Xu (1998) on necessary and sufficient conditions for the existence of cubature formulas with some strong symmetry. The new proof is shorter and simpler compared to the original one by Xu, and moreover gives a general interpretation of the analytically-written conditions of Xu's theorems. Second, we extend a theorem by Neumaier and Seidel (1988) on Euclidean designs to invariant Euclidean designs, and thereby classify tight Euclidean designs obtained from unions of the orbits of the corner vectors. This result generalizes a theorem of Bajnok (2007) which classifies tight Euclidean designs invariant under the Weyl group of type B to other finite reflection groups.Comment: 18 pages, no figur

An Analytical Approach to the Protein Designability Problem

We present an analytical method for determining the designability of protein structures. We apply our method to the case of two-dimensional lattice structures, and give a systematic solution for the spectrum of any structure. Using this spectrum, the designability of a structure can be estimated. We outline a heirarchy of structures, from most to least designable, and show that this heirarchy depends on the potential that is used.Comment: 16 pages 4 figure

Identifying Pathway Proteins in Networks using Convergence

One of the key goals of systems biology concerns the analysis of experimental biological data available to the scientific public. New technologies are rapidly developed to observe and report whole-scale biological phenomena; however, few methods exist with the ability to produce specific, testable hypotheses from this noisy ‘big’ data. In this work, we propose an approach that combines the power of data-driven network theory along with knowledge-based ontology to tackle this problem. Network models are especially powerful due to their ability to display elements of interest and their relationships as internetwork structures. Additionally, ontological data actually supplements the confidence of relationships within the model without clouding critical structure identification. As such, we postulate that given a (gene/protein) marker set of interest, we can systematically identify the core of their interactions (if they are indeed working together toward a biological function), via elimination of original markers and addition of additional necessary markers. This concept, which we refer to as “convergence,” harnesses the idea of “guilt-by-association” and recursion to identify whether a core of relationships exists between markers. In this study, we test graph theoretic concepts such as shortest-path, k-Nearest- Neighbor and clustering) to identify cores iteratively in data- and knowledge-based networks in the canonical yeast Pheromone Mating Response pathway. Additionally, we provide results for convergence application in virus infection, hearing loss, and Parkinson’s disease. Our results indicate that if a marker set has common discrete function, this approach is able to identify that function, its interacting markers, and any new elements necessary to complete the structural core of that function. The result below find that the shortest path function is the best approach of those used, finding small target sets that contain a majority or all of the markers in the gold standard pathway. The power of this approach lies in its ability to be used in investigative studies to inform decisions concerning target selection

Predicting the substrate specificity of a glycosyltransferase implicated in the production of phenolic volatiles in tomato fruit

The volatile compounds that constitute the fruit aroma of ripe tomato (Solanum lycopersicum) are often sequestered in glycosylated form. A homology-based screen was used to identify the gene SlUGT5, which is a member of UDP-glycosyltransferase 72 family and shows specificity towards a range of substrates, including flavonoid, flavanols, hydroquinone, xenobiotics and chlorinated pollutants. SlUGT5 was shown to be expressed primarily in ripening fruit and flowers, and mapped to chromosome I in a region containing a QTL that affected the content of guaiacol and eugenol in tomato crosses. Recombinant SlUGT5 protein demonstrated significant activity towards guaiacol and eugenol, as well as benzyl alcohol and methyl salicylate; however, the highest in vitro activity and affinity was shown for hydroquinone and salicyl alcohol. NMR analysis identified isosalicin as the only product of salicyl alcohol glycosylation. Protein modelling and substrate docking analysis were used to assess the basis for the substrate specificity of SlUGT5. The analysis correctly predicted the interactions with SlUGT5 substrates, and also indicated that increased hydrogen bonding, due to the presence of a second hydrophilic group in methyl salicylate, guaiacol and hydroquinone, appeared to more favourably anchor these acceptors within the glycosylation site, leading to increased stability, higher activities and higher substrate affinities