1000 resultados para Entrepreneurial Discovery


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This series of research vignettes is aimed at sharing current and interesting research findings from our team of international Entrepreneurship researchers. This vignette, written by Professor Per Davidsson, examines the evidence on the effects of a firm’s level of “entrepreneurial orientation” on business performance, across different contexts.

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This paper evaluates the efficiency of a number of popular corpus-based distributional models in performing discovery on very large document sets, including online collections. Literature-based discovery is the process of identifying previously unknown connections from text, often published literature, that could lead to the development of new techniques or technologies. Literature-based discovery has attracted growing research interest ever since Swanson's serendipitous discovery of the therapeutic effects of fish oil on Raynaud's disease in 1986. The successful application of distributional models in automating the identification of indirect associations underpinning literature-based discovery has been heavily demonstrated in the medical domain. However, we wish to investigate the computational complexity of distributional models for literature-based discovery on much larger document collections, as they may provide computationally tractable solutions to tasks including, predicting future disruptive innovations. In this paper we perform a computational complexity analysis on four successful corpus-based distributional models to evaluate their fit for such tasks. Our results indicate that corpus-based distributional models that store their representations in fixed dimensions provide superior efficiency on literature-based discovery tasks.

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In vivo small molecules as necessary intermediates are involved in numerous critical metabolic pathways and biological processes associated with many essential biological functions and events. There is growing evidence that MS-based metabolomics is emerging as a powerful tool to facilitate the discovery of functional small molecules that can better our understanding of development, infection, nutrition, disease, toxicity, drug therapeutics, gene modifications and host-pathogen interaction from metabolic perspectives. However, further progress must still be made in MS-based metabolomics because of the shortcomings in the current technologies and knowledge. This technique-driven review aims to explore the discovery of in vivo functional small molecules facilitated by MS-based metabolomics and to highlight the analytic capabilities and promising applications of this discovery strategy. Moreover, the biological significance of the discovery of in vivo functional small molecules with different biological contexts is also interrogated at a metabolic perspective.

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This study focuses on the managerial question “should social enterprises become more entrepreneurial?” It adapts the Covin and Slevin (1989) entrepreneurial orientation scale to measure the adoption of entrepreneurship by a social enterprise, and develops a scale that combines a Vincentian based focus to serve the poor with a propensity to take a more entrepreneurial approach toward business as a measure of a social value orientation (SVO).

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Although the tourism industry has been dramatically altered due to the Internet, there has been limited research published about international entrepreneurial values and Internet use in tourism firms. The findings of this study point to a relationship between the values of Internet-enabled international entrepreneurs in small-sized to medium-sized enterprises and the inclination of the firm to develop and initiate international activity. The findings of this study suggest that Internet-enabled tourism entrepreneurs share similar construct values. Two effective but underutilized qualitative methods were used in this study. The first method, repertory test, is an efficient technique for exploring constructs in decision making; the second method, laddering analysis, facilitates understanding of the perceived consequences and personal values guiding behaviour.

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Guaranteeing the quality of extracted features that describe relevant knowledge to users or topics is a challenge because of the large number of extracted features. Most popular existing term-based feature selection methods suffer from noisy feature extraction, which is irrelevant to the user needs (noisy). One popular method is to extract phrases or n-grams to describe the relevant knowledge. However, extracted n-grams and phrases usually contain a lot of noise. This paper proposes a method for reducing the noise in n-grams. The method first extracts more specific features (terms) to remove noisy features. The method then uses an extended random set to accurately weight n-grams based on their distribution in the documents and their terms distribution in n-grams. The proposed approach not only reduces the number of extracted n-grams but also improves the performance. The experimental results on Reuters Corpus Volume 1 (RCV1) data collection and TREC topics show that the proposed method significantly outperforms the state-of-art methods underpinned by Okapi BM25, tf*idf and Rocchio.

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The author, Dean Shepherd, is of entrepreneurship—how entrepreneurs think, decide to act, and feel. He recently realized that while his publications in academic journals have implications for entrepreneurs, those implications have remained relatively hidden in the text of the articles and hidden in articles published in journals largely inaccessible to those involved in the entrepreneurial process. This series is designed to bring the practical implications of his research to the forefront.

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The author, Dean Shepherd, is of entrepreneurship—how entrepreneurs think, decide to act, and feel. He recently realized that while his publications in academic journals have implications for entrepreneurs, those implications have remained relatively hidden in the text of the articles and hidden in articles published in journals largely inaccessible to those involved in the entrepreneurial process. This series is designed to bring the practical implications of his research to the forefront.

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The author, Dean Shepherd, is of entrepreneurship—how entrepreneurs think, decide to act, and feel. He recently realized that while his publications in academic journals have implications for entrepreneurs, those implications have remained relatively hidden in the text of the articles and hidden in articles published in journals largely inaccessible to those involved in the entrepreneurial process. This series is designed to bring the practical implications of his research to the forefront.

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This PhD research investigates the critical resources and Internet capabilities utilized by firms for leveraging global performance in entrepreneurial firms. Firm resources have been identified as important firm assets, which contribute to the firm's competitive global position. The Internet is a critical resource for a new generation of small and medium sized enterprise (SME) in pursuing international opportunities. By facilitating international business, the Internet has the ability to increase the quality and speed of communications, lower transaction costs, and facilitate the development of networks. Despite the increasing numbers of firms utilizing the Internet to pursue international opportunities, limited research remains. Adopting multiple case study methodology and structural equation modelling, the research identified the firm-level resources, which coincide with capabilities in a model predicting how international performance in firms is achieved.

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Event report on the Open Access and Research 2013 conference which focused on recent developments and the strategic advantages they bring to the research sector.

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Automated process discovery techniques aim at extracting process models from information system logs. Existing techniques in this space are effective when applied to relatively small or regular logs, but generate spaghetti-like and sometimes inaccurate models when confronted to logs with high variability. In previous work, trace clustering has been applied in an attempt to reduce the size and complexity of automatically discovered process models. The idea is to split the log into clusters and to discover one model per cluster. This leads to a collection of process models – each one representing a variant of the business process – as opposed to an all-encompassing model. Still, models produced in this way may exhibit unacceptably high complexity and low fitness. In this setting, this paper presents a two-way divide-and-conquer process discovery technique, wherein the discovered process models are split on the one hand by variants and on the other hand hierarchically using subprocess extraction. Splitting is performed in a controlled manner in order to achieve user-defined complexity or fitness thresholds. Experiments on real-life logs show that the technique produces collections of models substantially smaller than those extracted by applying existing trace clustering techniques, while allowing the user to control the fitness of the resulting models.

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More and more traditional manufacturing companies form or join inter-organizational networks to bundle their physical products with related services to offer superior value propositions to their customers. Some of these product-related services can be digitized completely and thus fully delivered electronically. Other services require the physical integration of external factors, but can still be coordinated electronically. In both cases companies and consumers face the problem of discovering appropriate product-related service offerings in the network or market. Based on ideas from the web service discovery discipline we propose a meet-in-the-middle approach between heavy-weight semantic technologies and simple boolean search to address this issue. Our approach is able to consider semantic relations in service descriptions and queries and thus delivers better results than syntax-based search. However – unlike most semantic approaches – it does not require the use of any formal language for semantic markup and thus requires less resources and skills for both service providers and consumers. To fully realize the potentials of the proposed approach a domain ontology is needed. In this research-in-progress paper we construct such an ontology for the domain of product-service bundles through analysis and synthesis of related work on service description. This will serve as an anchor for future research to iteratively improve and evaluate the ontology through collaborative design efforts and practical application.

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Building on the attention-based view, we argue that companies need a challenging mechanism to focus their absorptive capacity attention on corporate entrepreneurship versus mainstream activities or other purposes. We suggest entrepreneurial management as the attential driver for deploying absorptive capacity towards corporate entrepreneurship. From our analysis of a sample of 331 supplier companies providing products and services to the mining industry of Australia and Iran, we observe that absorptive capacity positively affects corporate entrepreneurship. The data also demonstrate that the effect of absorptive on corporate entrepreneurship increases when firms adopt the entrepreneurial culture and reward systems. However, the entrepreneurial growth and resource orientations negatively moderate the relationship between absorptive capacity and corporate entrepreneurship.