950 research outputs found

    Query refinement for patent prior art search

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    A patent is a contract between the inventor and the state, granting a limited time period to the inventor to exploit his invention. In exchange, the inventor must put a detailed description of his invention in the public domain. Patents can encourage innovation and economic growth but at the time of economic crisis patents can hamper such growth. The long duration of the application process is a big obstacle that needs to be addressed to maximize the benefit of patents on innovation and economy. This time can be significantly improved by changing the way we search the patent and non-patent literature.Despite the recent advancement of general information retrieval and the revolution of Web Search engines, there is still a huge gap between the emerging technologies from the research labs and adapted by major Internet search engines, and the systems which are in use by the patent search communities.In this thesis we investigate the problem of patent prior art search in patent retrieval with the goal of finding documents which describe the idea of a query patent. A query patent is a full patent application composed of hundreds of terms which does not represent a single focused information need. Other relevance evidences (e.g. classification tags, and bibliographical data) provide additional details about the underlying information need of the query patent. The first goal of this thesis is to estimate a uni-gram query model from the textual fields of a query patent. We then improve the initial query representation using noun phrases extracted from the query patent. We show that expansion in a query-dependent manner is useful.The second contribution of this thesis is to address the term mismatch problem from a query formulation point of view by integrating multiple relevance evidences associated with the query patent. To do this, we enhance the initial representation of the query with the term distribution of the community of inventors related to the topic of the query patent. We then build a lexicon using classification tags and show that query expansion using this lexicon and considering proximity information (between query and expansion terms) can improve the retrieval performance. We perform an empirical evaluation of our proposed models on two patent datasets. The experimental results show that our proposed models can achieve significantly better results than the baseline and other enhanced models

    Graphs behind data: A network-based approach to model different scenarios

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    openAl giorno dโ€™oggi, i contesti che possono beneficiare di tecniche di estrazione della conoscenza a partire dai dati grezzi sono aumentati drasticamente. Di conseguenza, la definizione di modelli capaci di rappresentare e gestire dati altamente eterogenei รจ un argomento di ricerca molto dibattuto in letteratura. In questa tesi, proponiamo una soluzione per affrontare tale problema. In particolare, riteniamo che la teoria dei grafi, e piรน nello specifico le reti complesse, insieme ai suoi concetti ed approcci, possano rappresentare una valida soluzione. Infatti, noi crediamo che le reti complesse possano costituire un modello unico ed unificante per rappresentare e gestire dati altamente eterogenei. Sulla base di questa premessa, mostriamo come gli stessi concetti ed approcci abbiano la potenzialitร  di affrontare con successo molti problemi aperti in diversi contesti. โ€‹Nowadays, the amount and variety of scenarios that can benefit from techniques for extracting and managing knowledge from raw data have dramatically increased. As a result, the search for models capable of ensuring the representation and management of highly heterogeneous data is a hot topic in the data science literature. In this thesis, we aim to propose a solution to address this issue. In particular, we believe that graphs, and more specifically complex networks, as well as the concepts and approaches associated with them, can represent a solution to the problem mentioned above. In fact, we believe that they can be a unique and unifying model to uniformly represent and handle extremely heterogeneous data. Based on this premise, we show how the same concepts and/or approach has the potential to address different open issues in different contexts. โ€‹INGEGNERIA DELL'INFORMAZIONEopenVirgili, Luc

    Study on open science: The general state of the play in Open Science principles and practices at European life sciences institutes

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    Nowadays, open science is a hot topic on all levels and also is one of the priorities of the European Research Area. Components that are commonly associated with open science are open access, open data, open methodology, open source, open peer review, open science policies and citizen science. Open science may a great potential to connect and influence the practices of researchers, funding institutions and the public. In this paper, we evaluate the level of openness based on public surveys at four European life sciences institute

    Systemic Bias in Patent Law

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    Knowledge in pollution-saving technological change

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    This thesis looks at the role that technical knowledge plays in the transition in industry away from pollution-intensive production methods. It uses econometric techniques and qualitative analysis to test three aspects of the relationship between knowledge and pollution-saving technological change-related outcomes, all in the context of US industry, and all with respect to conventional pollutants. The first paper observes that the level of industrial environmental R&D spending steadily declined from the late 1970s onward. Employing an estimation model with industry fixed effects, the hypothesis is tested that this decline was the result of the conditioning effect of greater flexibility in the design of the environmental policy on the environmental regulatory burden born by industry. The second paper investigates the sources of the change in SO2 intensity of electricity production undergone by electric power plants under the SO2 cap and trade program. Mixed methods including quantile regression are used to compare the effect of frontier technical knowledge on the extent of change undergone, relative to the effect of knowledge un-intensive techniques. The third paper investigates why a small number of inventions aimed at controlling pollution from automobiles turned out to be so much more technologically influential than the great majority of comparable inventions, which exerted very little technological influence at all. Negative binomial regression is used to test the effect of the composition of the stock of knowledge that the automobile companies brought to bear on the inventive process. These studies find that pollution-saving technological change is characterised more by the repurposing and adaptation of existing knowledge and by the churn among existing technologies, than by universal technological advance in dedicated environmental technologies. The implications for climate mitigation policy are discussed in the conclusions

    ํƒํ—˜์  ํ˜์‹ ์„ ์ด‰์ง„ํ•˜๋Š” ๊ธฐ์—…์˜ ๋‚ดยท์™ธ๋ถ€์  ์ง€์‹์š”์ธ์— ๋Œ€ํ•œ ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ํ˜‘๋™๊ณผ์ • ๊ธฐ์ˆ ๊ฒฝ์˜ยท๊ฒฝ์ œยท์ •์ฑ…์ „๊ณต, 2023. 2. ๊ฐ•์ง„์•„.Exploratory innovation, which refers to the generation of something valuable by using unfamiliar knowledge obtained from exploration, has received considerable attention as an important way to gain a competitive advantage and achieve sustainable growth. The increased importance of exploratory innovation resulted in the growing interest in underlying mechanisms of innovation, such as knowledge exploration (i.e., a nonlocal search beyond the firm's current expertise) and recombinatory search framework (i.e., an analytic tool that explains the link between knowledge elements and innovation). Recently, literature on exploratory innovation has investigated a firms internal and external knowledge environments on promoting exploratory innovation with the mechanisms of innovation as an implicit premise. However, the research on exploratory innovation is still in its early stages and has focused on individual topics rather than analyzing the entire process by which a firm creates exploratory innovation. In this regard, there is room to develop the previous discussions by taking a closer look at this field. This dissertation aims to increase the academic understanding of the mechanisms of exploratory innovation by investigating two key questions: First, what characteristics of a firm's intrinsic and embedded knowledge base promote exploratory innovation?; because the knowledge base is embedded in the organization and exists in a complex form changing over time, it is necessary to consider it a dynamic collection that includes knowledge elements and their combinations rather than a simple repository of knowledge elements, Second, what is the effective way to source external knowledge among alliance partner firms to create exploratory innovation?; when considering the external knowledge environment to promote exploratory innovation, it is necessary to understand not only the compositions of external knowledge resources but also the structural factors of interfirm networks, which affect accessibility and appropriability for external knowledge resources. From the internal focus, previous research on exploratory innovation has primarily focused on investigating and explaining a firms internal knowledge base as a simple repository of knowledge elements. Concerning the structure of knowledge, only recently has research begun to investigate characteristics of a knowledge base as a network of knowledge elements. In this regard, this dissertation examines the firms internal knowledge network and its effects on the subsequent exploratory innovation. Chapter 3 suggests a theoretical framework to express a firm's knowledge base as a single network composed of knowledge elements (i.e., component knowledge) and their combinations (i.e., architectural knowledge) and investigate the dynamics of such a knowledge network over time. Specifically, Chapter 3 distinguishes accumulated component and architectural knowledge, and investigates their impact on subsequent exploratory innovation, i.e., the creation of new elements and new combinations. The uncovered relationships between the two types of accumulated knowledge and the two types of exploratory innovations, help us comprehend the dynamics of the firms knowledge network. Using patent data of 111 US semiconductor companies from 2000โ€“2010, Chapter 3 empirically verifies an inverted U-shape relationship between the level of accumulated architectural knowledge and subsequent new knowledge combinations. As a firm accumulates experience of combining knowledge resources, new ways of knowledge application occur more frequently. This accumulated architectural knowledge helps organizational learning and broadens knowledge applicability to foster exploratory innovation. However, because of path-dependent attributes, knowledge application becomes rigid inertia that makes it harder to seek new ways. Furthermore, the relationships between accumulated component knowledge and new knowledge combinations, and between accumulated architectural knowledge and new knowledge elements were found to be positive. It shows that the accumulation of component knowledge can be essential for creating new knowledge combinations, and the accumulation of architectural knowledge also helps form new knowledge elements. In other words, learning about elements as a basis for new inventions should precede the creation of new inventions by combining elements. Additionally, accumulating knowledge from the experience of combining various elements is important to extend a firms area of expertise by gaining new knowledge elements. The results highlight the important role of the firms accumulated knowledge resources in creating exploratory innovation and contribute to the research on the antecedents of exploratory innovation. From the external focus, this dissertation investigates how the focal firm is able to effectively discover and secure the necessary knowledge in the alliance portfolio to create exploratory innovation. Previous literature has primarily focused on examining the external knowledge environment for exploratory innovation, focusing on either the compositions of knowledge resources or the structural factors affecting firms' access to them. For a holistic approach, Chapter 4 proposes a new framework of knowledge flow and search flexibility, both are essential for exploratory innovation, to simultaneously examine the effects of a firm's network position and knowledge composition of the alliance portfolio. Using this framework, Chapter 4 empirically confirms that central and brokering positions have an inverted U-shape relationship with the creation of exploratory innovation through panel data of 142 pharmaceutical companies from 1996-2010. Specifically, a central position promotes smooth knowledge flow due to the focal firms high social status, allowing it to access valuable knowledge from its partners. However, exceeding a certain level, the central position decreases search flexibility due to the constraint on decision-making caused by strong relationships. A brokering position fosters search flexibility as it allows the focal firm to control information flows. However, exceeding a certain level, the lack of absorptive capacity negatively influences knowledge flow. Chapter 4 also verifies two combinations of network position and knowledge composition advantageous for increasing exploratory innovation: a central position with partners' wide scope of new knowledge, and a brokering position with partners' wide scope of shared knowledge. These results support the argument that the effects of network position and knowledge composition can complementarily interact with each other, thus potentially compensating the negative effects on either knowledge flow or search flexibility. Specifically, new knowledge breadth can increase the low search flexibility resulting from a central position. The central position allows firms to overcome the information overflow associated with increases in new knowledge breadth. The shared knowledge breadth with partners increases absorptive capacity, which helps firms to understand each other and increases the knowledge flow that is often insufficient for firms at a brokering position. At the same time, the brokering position can prevent firms from becoming too similar to its partners, which would harm the exploration of new ideas. From these results, Chapter 4 contributes to the literature by identifying interaction effects between social network theory and the knowledge-based view and suggests implications for designing a firm's alliance strategy. Overall, this dissertation increases the understanding of the mechanism of exploratory innovation by investigating a firms internal and external factors that influence the creation of exploratory innovation. It provides the following contributions and implications. First, based on the findings of Chapter 3, this dissertation extends the literature on a firms knowledge resources as a source of innovation by revealing the relationship between knowledge elements and combinations. Applying Henderson and Clark(1990)s framework, the firms knowledge network and its subsequent exploratory innovation can be depicted to accumulated component and architectural knowledge and creation of new knowledge elements and combinations. Furthermore, by linking the firms previously formed knowledge network and its subsequent innovation, these relationships allowed us to explore the dynamics of a knowledge network in which existing elements and combinations are influencing each other to form new knowledge elements and combinations over time. Second, based on the findings of Chapter 4, this dissertation extends the literature on alliance portfolios by simultaneously employing social network theory and the knowledge-based view. Most prior studies examined the characteristics of the alliance portfolio either by focusing on structural properties such as actors network position or by focusing on nodal properties such as actors knowledge resources. However, this separation limits the understanding of inter-relational effects between the network position and the knowledge composition in alliance portfolios. This study highlights this inter-relationship and suggests that the potential disadvantageous effects originating from a firms network position can, under specific conditions, be overcome through a suitable knowledge composition. Third, this dissertation contributes to innovation literature by addressing a new approach satisfying two key factors for the creation of exploratory innovation, i.e., knowledge flow and search flexibility. Prior studies state that a knowledge flow corresponds with strong relationships, while search flexibility is associated with weak relationships. As both knowledge flow and search flexibility are required for exploration, prior studies focused on finding the optimum level of organizational integration or the relevant strategic choice. However, this dissertation claims that a particular combination between a firms network position and the knowledge composition of its alliance portfolio can complement both factors shortcomings, ultimately satisfying both key factors simultaneously.ํƒํ—˜์  ์ง€์‹์„ ํ™œ์šฉํ•ด ๊ฐ€์น˜ ์žˆ๋Š” ๊ฒƒ์„ ๋งŒ๋“ค์–ด๋‚ด๋Š” ํƒํ—˜์  ํ˜์‹ ์€ ๊ฒฝ์Ÿ ์šฐ์œ„๋ฅผ ํ™•๋ณดํ•˜๊ณ  ์ง€์† ๊ฐ€๋Šฅํ•œ ์„ฑ์žฅ์„ ์ด๋ฃฐ ์ˆ˜ ์žˆ๋Š” ์ค‘์š”ํ•œ ๋ฐฉ๋ฒ•์œผ๋กœ ์ƒ๋‹นํ•œ ๊ด€์‹ฌ์„ ๋ฐ›์•„์™”๋‹ค. ํƒํ—˜์  ํ˜์‹ ์˜ ์ค‘์š”์„ฑ์ด ๋†’์•„์ง์— ๋”ฐ๋ผ ์ง€์‹ ํƒํ—˜๊ณผ ์žฌ์กฐํ•ฉ ๊ฒ€์ƒ‰ ํ”„๋ ˆ์ž„์›Œํฌ์™€ ๊ฐ™์€ ํ˜์‹ ์˜ ๊ธฐ๋ณธ ๋ฉ”์ปค๋‹ˆ์ฆ˜์— ๋Œ€ํ•œ ๊ด€์‹ฌ์ด ๋†’์•„์ง€๊ณ  ์žˆ์œผ๋ฉฐ ์ตœ๊ทผ์—๋Š” ์ด๋Ÿฌํ•œ ํ˜์‹  ๋ฉ”์ปค๋‹ˆ์ฆ˜์„ ๋ฐ”ํƒ•์œผ๋กœ ํƒํ—˜์  ํ˜์‹ ์„ ์ด‰์ง„ํ•˜๋Š”๋ฐ ํ•„์š”ํ•œ ๊ธฐ์—…์˜ ์ง€์‹ ํ™˜๊ฒฝ์— ์ดˆ์ ์„ ๋งž์ถ˜ ์—ฐ๊ตฌ๊ฐ€ ๋“ฑ์žฅํ•˜๊ณ  ์žˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ํƒํ—˜์  ํ˜์‹ ์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๋Š” ์•„์ง ์ดˆ๊ธฐ ๋‹จ๊ณ„์— ์žˆ์œผ๋ฉฐ ๊ธฐ์—…์ด ํƒํ—˜์  ํ˜์‹ ์„ ์ฐฝ์ถœํ•˜๋Š” ์ „์ฒด ๊ณผ์ •์„ ๋ถ„์„ํ•˜๊ธฐ๋ณด๋‹ค๋Š” ๊ฐœ๋ณ„ ์ฃผ์ œ์— ์ดˆ์ ์„ ๋งž์ถ”๊ณ  ์žˆ๋‹ค. ๊ทธ๋Ÿฐ ์ ์—์„œ ์ด ๋ถ„์•ผ๋Š” ์ข€ ๋” ๋ฉด๋ฐ€ํ•œ ์—ฐ๊ตฌ๋ฅผ ํ†ตํ•ด ๊ธฐ์กด ๋ฌธํ—Œ์˜ ๋…ผ์˜๋ฅผ ๋ฐœ์ „์‹œํ‚ฌ ์—ฌ์ง€๊ฐ€ ์žˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์€ ๋‹ค์Œ ๋‘ ๊ฐ€์ง€ ํ•ต์‹ฌ ์งˆ๋ฌธ์„ ์ œ์‹œํ•จ์œผ๋กœ์จ ํƒํ—˜์  ํ˜์‹  ๋ฉ”์ปค๋‹ˆ์ฆ˜์— ๋Œ€ํ•œ ํ•™๋ฌธ์  ์ดํ•ด๋ฅผ ๋†’์ด๋Š” ๊ฒƒ์„ ๋ชฉํ‘œ๋กœ ํ•œ๋‹ค. ์ฒซ์งธ, "๊ธฐ์—…์— ๋‚ด์žฌ๋œ ์ง€์‹ ๊ธฐ๋ฐ˜์˜ ์–ด๋–ค ํŠน์ง•์ด ํƒํ—˜์  ํ˜์‹ ์„ ์ด‰์ง„ํ•˜๋Š”๊ฐ€?"; ๊ธฐ์—…์˜ ์ง€์‹ ๊ธฐ๋ฐ˜์€ ์กฐ์ง, ์‹œ์Šคํ…œ, ์‚ฌ๋žŒ ๋“ฑ์— ๋‚ด์žฌ๋˜์–ด ๋ณต์žกํ•œ ํ˜•ํƒœ๋กœ ์กด์žฌํ•˜๊ณ  ์‹œ๊ฐ„์ด ์ง€๋‚จ์— ๋”ฐ๋ผ ๋ณ€ํ™”ํ•˜๊ธฐ ๋•Œ๋ฌธ์— ๋‹จ์ˆœํ•œ ์ง€์‹ ์š”์†Œ์˜ ์ €์žฅ์†Œ๊ฐ€ ์•„๋‹Œ ์ง€์‹ ์š”์†Œ ๋ฐ ์ด๋“ค์˜ ์กฐํ•ฉ์„ ํฌํ•จํ•˜๋Š” ๋™์  ์ง‘ํ•ฉ์ฒด๋ผ๊ณ  ๊ฐ„์ฃผํ•  ํ•„์š”๊ฐ€ ์žˆ๋‹ค. ๋‘˜์งธ, "ํƒํ—˜์  ํ˜์‹ ์„ ์ฐฝ์ถœํ•˜๊ธฐ ์œ„ํ•ด ์ œํœด ํŒŒํŠธ๋„ˆ ๊ธฐ์—…๋“ค ์‚ฌ์ด์—์„œ ๊ทธ๋“ค์ด ๊ฐ€์ง„ ์ง€์‹์„ ์–ป๋Š” ํšจ๊ณผ์ ์ธ ๋ฐฉ๋ฒ•์€ ๋ฌด์—‡์ธ๊ฐ€?"; ํƒํ—˜์  ํ˜์‹ ์„ ์ด‰์ง„ํ•˜๊ธฐ ์œ„ํ•ด ์™ธ๋ถ€ ์ง€์‹ ํ™˜๊ฒฝ์„ ๊ณ ๋ คํ•  ๋•Œ, ์™ธ๋ถ€ ์ง€์‹ ์ž์›์˜ ๊ตฌ์„ฑ๋ฟ ์•„๋‹ˆ๋ผ ์™ธ๋ถ€ ์ง€์‹ ์ž์›์˜ ์ ‘๊ทผ์„ฑ๊ณผ ์ „์œ ์„ฑ์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ๊ธฐ์—… ๊ฐ„ ๋„คํŠธ์›Œํฌ์˜ ๊ตฌ์กฐ์  ์š”์ธ๋„ ์ดํ•ดํ•  ํ•„์š”๊ฐ€ ์žˆ๋‹ค. ๋‚ด๋ถ€์  ๊ด€์ ์—์„œ, ํƒํ—˜์  ํ˜์‹ ์— ๋Œ€ํ•œ ๊ธฐ์กด ์—ฐ๊ตฌ๋Š” ๊ธฐ์—…์˜ ๋‚ด๋ถ€ ์ง€์‹ ๊ธฐ๋ฐ˜์„ ์ฃผ๋กœ ๋‹จ์ˆœํ•œ ์ง€์‹ ์š”์†Œ์˜ ์ €์žฅ์†Œ๋กœ ์กฐ์‚ฌํ•˜๊ณ  ์„ค๋ช…ํ•˜๋Š” ๋ฐ ์ดˆ์ ์„ ๋งž์ถ”์—ˆ์œผ๋ฉฐ ์ตœ๊ทผ์—์„œ์•ผ ์ง€์‹ ์š”์†Œ์˜ ๊ตฌ์กฐ์  ํŠน์„ฑ์„ ์กฐ์‚ฌํ•˜๊ธฐ ์œ„ํ•œ ์—ฐ๊ตฌ๊ฐ€ ์‹œ์ž‘๋˜์—ˆ๋‹ค. ์ด์™€ ๊ด€๋ จํ•˜์—ฌ ๋ณธ ๋…ผ๋ฌธ์€ ๊ธฐ์—…์˜ ๋‚ด๋ถ€ ์ง€์‹ ๋„คํŠธ์›Œํฌ๊ฐ€ ํƒํ—˜์  ํ˜์‹ ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ์‚ดํŽด๋ณธ๋‹ค. ์ œ3์žฅ์€ ๊ธฐ์—…์˜ ์ง€์‹ ๊ธฐ๋ฐ˜์„ ์š”์†Œ ์ง€์‹(=component knowledge)๊ณผ ์ด๋“ค์˜ ์กฐํ•ฉ(๊ตฌ์กฐ ์ง€์‹=architectural knowledge)์œผ๋กœ ๊ตฌ์„ฑ๋œ ๋„คํŠธ์›Œํฌ๋กœ ํ‘œํ˜„ํ•˜๊ณ  ์ด๋Ÿฌํ•œ ์ง€์‹ ๋„คํŠธ์›Œํฌ๊ฐ€ ์‹œ๊ฐ„์— ๋”ฐ๋ผ ์–ด๋–ป๊ฒŒ ๋ณ€ํ™”ํ•ด ๊ฐ€๋Š”์ง€ ์กฐ์‚ฌํ•˜๊ธฐ ์œ„ํ•œ ์ด๋ก ์  ํ”„๋ ˆ์ž„์›Œํฌ๋ฅผ ์ œ์•ˆํ•œ๋‹ค. ๊ตฌ์ฒด์ ์œผ๋กœ, ์ œ3์žฅ์—์„œ๋Š” ์ถ•์ ๋œ ์š”์†Œ ์ง€์‹ ๋ฐ ๊ตฌ์กฐ ์ง€์‹์„ ๊ตฌ๋ณ„ํ•˜๊ณ , ์ด๋กœ ์ธํ•ด ๋ฐœ์ƒํ•˜๋Š” ํƒํ—˜์  ํ˜์‹ , ์ฆ‰ ์ƒˆ๋กœ์šด ์š”์†Œ ์ง€์‹ ๋ฐ ์ƒˆ๋กœ์šด ๊ตฌ์กฐ ์ง€์‹(์š”์†Œ ์ง€์‹๊ฐ„ ์ƒˆ๋กœ์šด ์กฐํ•ฉ)์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ์กฐ์‚ฌํ•œ๋‹ค. ๋‘ ๊ฐ€์ง€ ์œ ํ˜•์˜ ์ถ•์ ๋œ ์ง€์‹๊ณผ ๋‘ ๊ฐ€์ง€ ์œ ํ˜•์˜ ํƒํ—˜์  ํ˜์‹  ์‚ฌ์ด์˜ ๋“œ๋Ÿฌ๋‚˜์ง€ ์•Š์€ ๊ด€๊ณ„๋Š” ํšŒ์‚ฌ์˜ ์ง€์‹ ๋„คํŠธ์›Œํฌ์˜ ๋™์  ํŠน์„ฑ์„ ์ดํ•ดํ•˜๋Š” ๋ฐ ๋„์›€์ด ๋œ๋‹ค. ์ œ3์žฅ์—์„œ๋Š” 2000~2010๋…„ ๋ฏธ๊ตญ 111๊ฐœ ๋ฐ˜๋„์ฒด ๊ธฐ์—…์˜ ํŠนํ—ˆ์ž๋ฃŒ๋ฅผ ์ด์šฉํ•˜์—ฌ ์ถ•์ ๋œ ๊ตฌ์กฐ ์ง€์‹์˜ ์ˆ˜์ค€๊ณผ ๊ทธ์— ๋”ฐ๋ฅธ ์ƒˆ๋กœ์šด ๊ตฌ์กฐ ์ง€์‹ ๊ฐ„์˜ ์—ญ U์žํ˜• ๊ด€๊ณ„๋ฅผ ์‹ค์ฆ์ ์œผ๋กœ ๊ฒ€์ฆํ•œ๋‹ค. ๊ธฐ์—…์ด ์ง€์‹ ๊ฒฐํ•ฉ ๊ฒฝํ—˜์„ ์ถ•์ ํ•จ์— ๋”ฐ๋ผ ์ƒˆ๋กœ์šด ์ง€์‹ ์ ์šฉ ๋ฐฉ๋ฒ•์„ ๋ฐœ๊ฒฌํ•˜๋ฉฐ, ์ด๋ ‡๊ฒŒ ์ถ•์ ๋œ ๊ตฌ์กฐ ์ง€์‹์€ ์กฐ์ง ํ•™์Šต ๋ฐ ์ง€์‹ ์ ์šฉ ๊ฐ€๋Šฅ์„ฑ์„ ํ™•์žฅํ•˜์—ฌ ํƒํ—˜์  ํ˜์‹ (๊ตฌ์กฐ ์ง€์‹์˜ ๋ฐœ์ƒ)์„ ์ด‰์ง„ํ•œ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๊ตฌ์กฐ ์ง€์‹์ด ์ถ•์ ๋ ์ˆ˜๋ก ์กฐ์ง ํ•™์Šต์˜ ๊ฒฝ๋กœ ์˜์กด์  ์†์„ฑ์— ๋”ฐ๋ฅธ ๊ฒฝ์ง๋œ ๊ด€์„ฑ์ด ์ƒ๊ธฐ๊ฒŒ ๋˜๊ณ  ์ƒˆ๋กœ์šด ์ง€์‹ ์‘์šฉ ๋ฐฉ๋ฒ•์„ ๋ชจ์ƒ‰ํ•˜๋Š” ๊ฒƒ์ด ์–ด๋ ต๊ฒŒ ๋œ๋‹ค. ํ•œํŽธ์œผ๋กœ, ์ถ•์ ๋œ ์š”์†Œ ์ง€์‹๊ณผ ์ƒˆ๋กœ์šด ๊ตฌ์กฐ ์ง€์‹, ๊ทธ๋ฆฌ๊ณ  ์ถ•์ ๋œ ๊ตฌ์กฐ ์ง€์‹๊ณผ ์ƒˆ๋กœ์šด ์š”์†Œ ์ง€์‹ ๊ฐ„์˜ ๊ด€๊ณ„๋Š” ์–‘(+)์˜ ๋ฐฉํ–ฅ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์ด๋Š” ์š”์†Œ ์ง€์‹ ์ถ•์ ์ด ์ƒˆ๋กœ์šด ์ง€์‹ ์กฐํ•ฉ(๊ตฌ์กฐ ์ง€์‹)์„ ๋งŒ๋“œ๋Š” ๋ฐ ํ•„์ˆ˜์ ์ผ ์ˆ˜ ์žˆ์œผ๋ฉฐ, ๊ตฌ์กฐ ์ง€์‹ ์ถ•์ ์ด ์ƒˆ๋กœ์šด ์š”์†Œ ์ง€์‹์„ ํ˜•์„ฑํ•˜๋Š” ๋ฐ ๋„์›€์ด ๋œ๋‹ค๋Š” ๊ฒƒ์„ ๋งํ•ด์ค€๋‹ค. ์ฆ‰, ์ƒˆ๋กœ์šด ๋ฐœ๋ช…(์ง€์‹ ์š”์†Œ์˜ ๊ฒฐํ•ฉ)์„ ํ•˜๊ธฐ ์œ„ํ•ด์„œ๋Š” ๊ฐœ๋ณ„ ์ง€์‹ ์š”์†Œ์— ๋Œ€ํ•œ ํ•™์Šต์ด ์„ ํ–‰๋˜์–ด์•ผ ํ•˜๋ฉฐ, ๋ฐ˜๋Œ€๋กœ ์ƒˆ๋กœ์šด ์š”์†Œ ์ง€์‹์„ ํš๋“ํ•จ์œผ๋กœ์จ ๊ธฐ์—…์˜ ์ „๋ฌธ ์˜์—ญ์„ ํ™•์žฅํ•˜๋ ค๋ฉด ๋‹ค์–‘ํ•œ ์š”์†Œ ์ง€์‹ ๊ฒฐํ•ฉ ๊ฒฝํ—˜์ด ์ค‘์š”ํ•˜๋‹ค๋Š” ๊ฒฐ๊ณผ๋ฅผ ๋„์ถœํ•˜์˜€๋‹ค. ์ด ๊ฒฐ๊ณผ๋Š” ๊ธฐ์—…์ด ์ถ•์ ํ•œ ์ง€์‹ ์ž์›์ด ํƒํ—˜์  ํ˜์‹  ์ฐฝ์ถœ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ๊ฐ•์กฐํ•˜๊ณ  ๊ธฐ์กด ํƒํ—˜์  ํ˜์‹ ์— ๋Œ€ํ•œ ๋ฌธํ—Œ ์—ฐ๊ตฌ์— ๊ธฐ์—ฌํ•œ๋‹ค. ์™ธ๋ถ€์  ๊ด€์ ์—์„œ๋Š”, ์–ด๋–ป๊ฒŒ ํฌ์ปฌ ๊ธฐ์—…์ด ํƒํ—˜์  ํ˜์‹ ์„ ์ฐฝ์ถœํ•˜๊ธฐ ์œ„ํ•ด ์ œํœด ํฌํŠธํด๋ฆฌ์˜ค์—์„œ ํ•„์š”ํ•œ ์ง€์‹์„ ํšจ๊ณผ์ ์œผ๋กœ ๋ฐœ๊ตดํ•˜๊ณ  ํ™•๋ณดํ•  ์ˆ˜ ์žˆ๋Š”์ง€ ์กฐ์‚ฌํ•œ๋‹ค. ๊ธฐ์กด ๋ฌธํ—Œ์€ ํƒํ—˜์  ํ˜์‹ ์„ ์œ„ํ•œ ์™ธ๋ถ€ ์ง€์‹ ํ™˜๊ฒฝ์„ ์„ค๋ช…ํ•˜๊ธฐ ์œ„ํ•ด ์ฃผ๋กœ ์ง€์‹ ์ž์›์˜ ๊ตฌ์„ฑ ๋˜๋Š” ์ง€์‹ ์ž์› ์ ‘๊ทผ์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ๊ตฌ์กฐ์  ์š”์ธ์— ์ดˆ์ ์„ ๋งž์ถ”์–ด ์กฐ์‚ฌํ•˜์˜€๋‹ค. ํ†ตํ•ฉ์  ์ ‘๊ทผ๋ฐฉ์‹์„ ์œ„ํ•ด, ์ œ4์žฅ์€ ๊ธฐ์—…์˜ ๋„คํŠธ์›Œํฌ ์œ„์น˜์™€ ์ œํœด ํฌํŠธํด๋ฆฌ์˜ค์˜ ์ง€์‹ ๊ตฌ์„ฑ ์˜ํ–ฅ์„ ๋™์‹œ์— ์กฐ์‚ฌํ•˜๊ธฐ ์œ„ํ•ด ํƒํ—˜์  ํ˜์‹ ์— ํ•„์ˆ˜์ ์ธ ๋‘ ๊ฐ€์ง€ ์š”์†Œ, ์ฆ‰, ์ง€์‹ ํ๋ฆ„(knowledge flow)๊ณผ ๊ฒ€์ƒ‰ ์œ ์—ฐ์„ฑ(search flexibility) ํ”„๋ ˆ์ž„์›Œํฌ๋ฅผ ์ œ์•ˆํ•œ๋‹ค. ์ œ4์žฅ์€ ์ด๋Ÿฌํ•œ ํ‹€์„ ์ด์šฉํ•˜์—ฌ 1996~2010๋…„ 142๊ฐœ ์ œ์•ฝํšŒ์‚ฌ์˜ ํŒจ๋„ ์ž๋ฃŒ๋ฅผ ํ†ตํ•ด ์ค‘์‹ฌ ์œ„์น˜ ๋ฐ ์ค‘๊ฐœ ์œ„์น˜๊ฐ€ ํƒํ—˜์  ํ˜์‹ ์˜ ์ฐฝ์ถœ๊ณผ ์—ญ U์žํ˜• ๊ด€๊ณ„๋ฅผ ๋งบ๊ณ  ์žˆ์Œ์„ ์‹ค์ฆ์ ์œผ๋กœ ํ™•์ธํ•œ๋‹ค. ์ค‘์‹ฌ ์œ„์น˜๋Š” ๋†’์€ ์‚ฌํšŒ์  ์ง€์œ„๋กœ ์ธํ•ด ์›ํ™œํ•œ ์ง€์‹ ํ๋ฆ„์„ ์ด‰์ง„ํ•˜์—ฌ ํŒŒํŠธ๋„ˆ๋กœ๋ถ€ํ„ฐ ๊ท€์ค‘ํ•œ ์ง€์‹์„ ์ ‘ํ•  ์ˆ˜ ์žˆ๋„๋ก ๋•๋Š”๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ผ์ •ํ•œ ์ˆ˜์ค€์„ ๋„˜์–ด์„œ๋ฉด ์ค‘์‹ฌ ์œ„์น˜๋Š” ๊ฐ•ํ•œ ๊ด€๊ณ„์—์„œ ๋ฐœ์ƒํ•˜๋Š” ์˜์‚ฌ ๊ฒฐ์ • ์ œ์•ฝ์œผ๋กœ ์ธํ•ด ๊ฒ€์ƒ‰ ์œ ์—ฐ์„ฑ์„ ๋–จ์–ด๋œจ๋ฆฐ๋‹ค. ์ค‘๊ฐœ ์œ„์น˜๋Š” ํฌ์ปฌ ๊ธฐ์—…์ด ์ •๋ณด ํ๋ฆ„์„ ์ œ์–ดํ•  ์ˆ˜ ์žˆ๋„๋ก ํ•˜๊ธฐ ๋•Œ๋ฌธ์— ๊ฒ€์ƒ‰ ์œ ์—ฐ์„ฑ์„ ๋†’์ธ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ผ์ • ์ˆ˜์ค€์„ ๋„˜์–ด์„œ๋Š” ๊ฒฝ์šฐ ํก์ˆ˜๋Šฅ๋ ฅ ๋ถ€์กฑ์œผ๋กœ ์ง€์‹ ํ๋ฆ„์— ๋ถ€์ •์ ์ธ ์˜ํ–ฅ์„ ๋ฏธ์น˜๊ฒŒ ๋œ๋‹ค. ์ œ4์žฅ์—์„œ๋Š” ๋˜ํ•œ ํƒํ—˜์  ํ˜์‹ ์„ ์ฆ๊ฐ€์‹œํ‚ค๋Š”๋ฐ ์œ ๋ฆฌํ•œ ๋„คํŠธ์›Œํฌ ์œ„์น˜์™€ ์ง€์‹ ๊ตฌ์„ฑ์˜ ๋‘ ๊ฐ€์ง€ ์กฐํ•ฉ, ์ฆ‰ ์ค‘์‹ฌ ์œ„์น˜์™€ ๋„“์€ ๋ฒ”์œ„์˜ ์ƒˆ๋กœ์šด ์ง€์‹์„ ๋ณด์œ ํ•œ ํŒŒํŠธ๋„ˆ, ์ค‘๊ฐœ ์œ„์น˜์™€ ๋„“์€ ๋ฒ”์œ„์˜ ๊ณต์œ  ์ง€์‹์„ ๋ณด์œ ํ•œ ํŒŒํŠธ๋„ˆ ์‚ฌ์ด์˜ ๊ด€๊ณ„๋ฅผ ๊ฒ€์ฆํ•œ๋‹ค. ์ด๋Ÿฌํ•œ ๊ฒฐ๊ณผ๋Š” ๋„คํŠธ์›Œํฌ ์œ„์น˜์™€ ์ง€์‹ ๊ตฌ์„ฑ์˜ ์˜ํ–ฅ์ด ์ƒํ˜ธ ๋ณด์™„์ ์œผ๋กœ ์ƒํ˜ธ ์ž‘์šฉํ•˜์—ฌ ์ง€์‹ ํ๋ฆ„ ๋˜๋Š” ๊ฒ€์ƒ‰ ์œ ์—ฐ์„ฑ์— ๋Œ€ํ•œ ๋ถ€์ •์ ์ธ ์˜ํ–ฅ์„ ์ž ์žฌ์ ์œผ๋กœ ๋ณด์ƒํ•  ์ˆ˜ ์žˆ๋‹ค๋Š” ์ฃผ์žฅ์„ ๋’ท๋ฐ›์นจํ•œ๋‹ค. ๋„“์€ ๋ฒ”์œ„์˜ ์ƒˆ๋กœ์šด ์ง€์‹์€ ์ค‘์‹ฌ ์œ„์น˜์—์„œ ๊ธฐ์ธํ•˜๋Š” ๋‚ฎ์€ ๊ฒ€์ƒ‰ ์œ ์—ฐ์„ฑ์„ ์ฆ๊ฐ€์‹œํ‚ฌ ์ˆ˜ ์žˆ๋‹ค. ์ค‘์‹ฌ ์œ„์น˜์˜ ๊ตฌ์กฐ์  ์žฅ์ ์„ ์ด์šฉํ•ด ํฌ์ปฌ ๊ธฐ์—…์€ ์ƒˆ๋กœ์šด ์ง€์‹์˜ ๋ฒ”์œ„๊ฐ€ ์ฆ๊ฐ€ํ•  ๋•Œ ๋ฐœ์ƒํ•  ์ˆ˜ ์žˆ๋Š” ์ •๋ณด์˜ ๋ฒ”๋žŒ์„ ๊ทน๋ณตํ•  ์ˆ˜ ์žˆ๋‹ค. ํŒŒํŠธ๋„ˆ์™€ ๋„“์€ ๋ฒ”์œ„์˜ ์ง€์‹์„ ๊ณต์œ ํ•  ๊ฒฝ์šฐ ์ƒ๋Œ€์ ์ธ ํก์ˆ˜ ๋Šฅ๋ ฅ์„ ์ฆ๊ฐ€์‹œ์ผœ ๊ธฐ์—…์ด ์„œ๋กœ๋ฅผ ์ดํ•ดํ•˜๋Š” ๋ฐ ๋„์›€์ด ๋˜๊ณ  ์ค‘๊ฐœ ์œ„์น˜์— ์žˆ๋Š” ๊ธฐ์—…์—๊ฒŒ ์ข…์ข… ๋ถ€์กฑํ•œ ์ง€์‹ ํ๋ฆ„์„ ์ฆ๊ฐ€์‹œํ‚จ๋‹ค. ๋™์‹œ์—, ์ค‘๊ฐœ ์œ„์น˜๋Š” ํฌ์ปฌ ๊ธฐ์—…์ด ํŒŒํŠธ๋„ˆ๋“ค๊ณผ ๋„ˆ๋ฌด ๋น„์Šทํ•ด์ง€๋Š” ๊ฒƒ์„ ๋ง‰์•„์คŒ์œผ๋กœ์จ ์ƒˆ๋กœ์šด ์•„์ด๋””์–ด ํƒ๊ตฌ์— ๋ฏธ์น  ์ˆ˜ ์žˆ๋Š” ๋ถ€์ •์  ์˜ํ–ฅ์„ ์ค„์ธ๋‹ค. ์ด๋Ÿฌํ•œ ๊ฒฐ๊ณผ๋กœ๋ถ€ํ„ฐ, ์ œ4์žฅ์€ ์†Œ์…œ ๋„คํŠธ์›Œํฌ ์ด๋ก ๊ณผ ์ง€์‹ ๊ธฐ๋ฐ˜ ๊ด€์ ์˜ ์ƒํ˜ธ์ž‘์šฉ ํšจ๊ณผ๋ฅผ ํ™•์ธํ•จ์œผ๋กœ์จ ๊ธฐ์กด ๋ฌธํ—Œ ์—ฐ๊ตฌ์— ๊ธฐ์—ฌํ•˜๊ณ  ๊ธฐ์—…์˜ ์ œํœด ์ „๋žต ์„ค๊ณ„์— ๋Œ€ํ•œ ์‹œ์‚ฌ์ ์„ ์ œ์‹œํ•œ๋‹ค. ์ข…ํ•ฉํ•˜๋ฉด, ๋ณธ ๋…ผ๋ฌธ์€ ํƒํ—˜์  ํ˜์‹  ์ฐฝ์ถœ์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ๊ธฐ์—…์˜ ๋Œ€๋‚ด์™ธ์  ์ง€์‹ ์š”์ธ์„ ์กฐ์‚ฌํ•จ์œผ๋กœ์จ ํƒํ—˜์  ํ˜์‹  ๋ฉ”์ปค๋‹ˆ์ฆ˜์— ๋Œ€ํ•œ ์ดํ•ด๋ฅผ ๋†’์ด๊ณ  ๋‹ค์Œ๊ณผ ๊ฐ™์€ ์‹œ์‚ฌ์ ์„ ๋„์ถœํ•œ๋‹ค. ์ฒซ์งธ, ๋ณธ ๋…ผ๋ฌธ์€ ์ œ3์žฅ์˜ ์—ฐ๊ตฌ๊ฒฐ๊ณผ๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ์ง€์‹ ์š”์†Œ์™€ ์ง€์‹ ๊ฒฐํ•ฉ์˜ ๊ด€๊ณ„๋ฅผ ๋ฐํžˆ๊ณ  ํ˜์‹ ์˜ ์›์ฒœ์œผ๋กœ์„œ์˜ ์ง€์‹ ์ž์›์— ๋Œ€ํ•œ ๊ธฐ์กด ๋ฌธํ—Œ์„ ํ™•์žฅํ•œ๋‹ค. ํ—จ๋”์Šจ๊ณผ ํด๋ผํฌ(1990)์˜ ํ”„๋ ˆ์ž„์›Œํฌ๋ฅผ ์ ์šฉํ•˜๋ฉด, ํšŒ์‚ฌ์˜ ์ง€์‹ ๋„คํŠธ์›Œํฌ์™€ ํƒํ—˜์  ํ˜์‹ ์€ ์ถ•์ ๋œ ์š”์†Œ ์ง€์‹ ๋ฐ ๊ตฌ์กฐ ์ง€์‹๊ณผ ์ƒˆ๋กœ ๋ฐœ์ƒํ•˜๋Š” ์š”์†Œ ์ง€์‹ ๋ฐ ๊ตฌ์กฐ ์ง€์‹์œผ๋กœ ๋ฌ˜์‚ฌ๋  ์ˆ˜ ์žˆ๋‹ค. ์ด๋Ÿฌํ•œ ๊ด€๊ณ„๋ฅผ ํ†ตํ•ด ํ˜•์„ฑ๋œ ๊ธฐ์—…์˜ ์ง€์‹ ๋„คํŠธ์›Œํฌ์™€ ํ˜์‹  ์ฐฝ์ถœ์„ ์—ฐ๊ณ„ํ•จ์œผ๋กœ์จ ์‹œ๊ฐ„์ด ์ง€๋‚จ์— ๋”ฐ๋ผ ๊ธฐ์กด ์š”์†Œ ์ง€์‹๊ณผ ๊ตฌ์กฐ ์ง€์‹์ด ์„œ๋กœ ์˜ํ–ฅ์„ ๋ฏธ์ณ ์ƒˆ๋กœ์šด ์š”์†Œ ์ง€์‹ ๋ฐ ๊ตฌ์กฐ ์ง€์‹์„ ํ˜•์„ฑํ•˜๋Š” ์ง€์‹ ๋„คํŠธ์›Œํฌ์˜ ๋™์  ํŠน์„ฑ์„ ํƒ์ƒ‰ํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๋‘˜์งธ, ๋ณธ ๋…ผ๋ฌธ์€ ์ œ4์žฅ์˜ ์—ฐ๊ตฌ๊ฒฐ๊ณผ๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ์†Œ์…œ ๋„คํŠธ์›Œํฌ ์ด๋ก ๊ณผ ์ง€์‹๊ธฐ๋ฐ˜ ๊ด€์ ์„ ๋™์‹œ์— ์ ์šฉํ•˜์—ฌ ์ œํœด ํฌํŠธํด๋ฆฌ์˜ค์— ๋Œ€ํ•œ ๊ธฐ์กด ๋ฌธํ—Œ์„ ํ™•์žฅํ•œ๋‹ค. ๋งŽ์€ ์„ ํ–‰ ์—ฐ๊ตฌ๋Š” ํ–‰์œ„์ž์˜ ๋„คํŠธ์›Œํฌ ์œ„์น˜์™€ ๊ฐ™์€ ๊ตฌ์กฐ์  ํŠน์„ฑ์— ์ดˆ์ ์„ ๋งž์ถ”๊ฑฐ๋‚˜ ํ–‰์œ„์ž์˜ ์ง€์‹ ์ž์›๊ณผ ๊ฐ™์€ ๋…ธ๋“œ์  ํŠน์„ฑ์— ์ดˆ์ ์„ ๋งž์ถ”์–ด ์ œํœด ํฌํŠธํด๋ฆฌ์˜ค ํŠน์„ฑ์„ ์กฐ์‚ฌํ•˜์˜€๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ œํœด ํฌํŠธํด๋ฆฌ์˜ค์—์„œ ๋„คํŠธ์›Œํฌ ์œ„์น˜์™€ ์ง€์‹ ๊ตฌ์„ฑ ๊ฐ„ ์ƒํ˜ธ ๊ด€๊ณ„์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๋Š” ์ œํ•œ์ ์ด์—ˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ์ด๋Ÿฌํ•œ ์ƒํ˜ธ ๊ด€๊ณ„๋ฅผ ์ดˆ์ ์„ ๋งž์ถ”๊ณ , ํŠน์ • ์กฐ๊ฑด ํ•˜์—์„œ๋Š” ๊ธฐ์—…์˜ ๋„คํŠธ์›Œํฌ ์œ„์น˜์—์„œ ๋น„๋กฏ๋˜๋Š” ๋ถ€์ •์  ์˜ํ–ฅ์„ ์ ์ ˆํ•œ ํŒŒํŠธ๋„ˆ์˜ ์ง€์‹ ๊ตฌ์„ฑ์„ ํ†ตํ•ด ๊ทน๋ณตํ•  ์ˆ˜ ์žˆ์Œ์„ ์‹œ์‚ฌํ•œ๋‹ค. ์…‹์งธ, ๋ณธ ๋…ผ๋ฌธ์€ ํƒํ—˜์  ํ˜์‹  ์ฐฝ์ถœ์„ ์œ„ํ•œ ๋‘ ๊ฐ€์ง€ ํ•ต์‹ฌ ์š”์†Œ, ์ง€์‹ ํ๋ฆ„๊ณผ ๊ฒ€์ƒ‰ ์œ ์—ฐ์„ฑ์„ ๋งŒ์กฑ์‹œ์ผœ์•ผ ํ•œ๋‹ค๋Š” ์ƒˆ๋กœ์šด ๋…ผ์˜๋ฅผ ์ œ์•ˆํ•จ์œผ๋กœ์จ ๊ธฐ์กด ํ˜์‹  ๋ฌธํ—Œ์—ฐ๊ตฌ์— ๊ธฐ์—ฌํ•œ๋‹ค. ์ด์ „ ์—ฐ๊ตฌ์—์„œ ์ง€์‹ ํ๋ฆ„์€ ๊ฐ•ํ•œ ๊ด€๊ณ„์—์„œ ๋ฐœํ˜„ํ•˜๊ธฐ ์‰ฝ๊ณ  ๊ฒ€์ƒ‰ ์œ ์—ฐ์„ฑ์€ ์•ฝํ•œ ๊ด€๊ณ„์— ๋น„๋กฏ๋œ๋‹ค๊ณ  ๋งํ•œ๋‹ค. ํƒํ—˜์  ํ˜์‹ ์„ ์œ„ํ•ด์„œ๋Š” ์ง€์‹ ํ๋ฆ„๊ณผ ๊ฒ€์ƒ‰ ์œ ์—ฐ์„ฑ์ด ๋ชจ๋‘ ์š”๊ตฌ๋˜๊ธฐ ๋•Œ๋ฌธ์— ์„ ํ–‰์—ฐ๊ตฌ๋Š” ์ตœ์ ์˜ ์กฐ์งํ†ตํ•ฉ ์ˆ˜์ค€์ด๋‚˜ ์ ์ ˆํ•œ ์ „๋žต์  ์„ ํƒ์„ ์ฐพ๋Š” ๋ฐ ์ดˆ์ ์„ ๋งž์ท„๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๋ณธ ๋…ผ๋ฌธ์€ ์ œํœด ํฌํŠธํด๋ฆฌ์˜ค์—์„œ ํฌ์ปฌ ๊ธฐ์—…์˜ ๋„คํŠธ์›Œํฌ ์œ„์น˜์™€ ํŒŒํŠธ๋„ˆ ์ง€์‹ ๊ตฌ์„ฑ ๊ฐ„ ํŠน์ • ์กฐํ•ฉ์ด ๋‘ ์š”์†Œ์˜ ๋‹จ์ ์„ ๋ณด์™„ํ•˜์—ฌ ๊ถ๊ทน์ ์œผ๋กœ ๋‘ ๊ฐ€์ง€ ํ•ต์‹ฌ ์š”์†Œ๋ฅผ ๋™์‹œ์— ๋งŒ์กฑ์‹œํ‚ฌ ์ˆ˜ ์žˆ์Œ์„ ๋ฐํžŒ๋‹ค.Chapter 1. Introduction 1 1.1 Background 1 1.2 Research objectives 8 1.3 Research outline 11 Chapter 2. Literature review 15 2.1 Review on the literature on exploratory innovation 15 2.2 Exploratory innovation created from a firm's internal knowledge base 23 2.2.1 The concept of knowledge network 23 2.2.2 Firm's innovation based on its knowledge network 28 2.3 Exploratory innovation through external knowledge sourcing in alliance portfolio 35 2.3.1 Two theoretical lenses to examine the effects of a firm's alliance portfolio on its exploratory innovation 35 2.3.2 Two preconditions to absorb and create new knowledge for exploratory innovation 37 Chapter 3. Exploratory innovation through managing firm's internal knowledge network 40 3.1 Introduction 40 3.2 Research Hypotheses 45 3.2.1 Degree of accumulation in architectural knowledge and newly explored component knowledge 45 3.2.2 Accumulated architectural knowledge and new knowledge elements 47 3.2.3 Accumulated component knowledge and new knowledge combinations 49 3.2.4 Accumulated component knowledge and new knowledge elements 52 3.3 Methods 56 3.3.1 Data and sample 56 3.3.2 Dependent variable 59 3.3.3 Independent Variables 60 3.3.4 Control Variables 61 3.3.5 Empirical model specification 62 3.4 Results 68 3.5 Discussion 72 Chapter 4. Exploratory innovation through gaining knowledge from alliance portfolio 75 4.1 Introduction 75 4.2 Research Hypothesis 81 4.2.1 Central position and exploratory innovation 81 4.2.2 Brokering position and exploratory innovation 84 4.2.3 Central position with partners' wide scope of new knowledge and exploratory innovation 86 4.2.4 Brokering position with partners' wide scope of shared knowledge and exploratory innovation 89 4.3 Methods 94 4.3.1 Data and sample 94 4.3.2 Dependent variable 96 4.3.3 Independent variables 98 4.3.4 Control variables 100 4.3.5 Empirical model specification 102 4.4 Results 107 4.5 Discussion 114 Chapter 5. Conclusive remarks 117 5.1 Contributions and implications 117 5.2 Limitations and future research 123 Bibliography 129 Abstract (Korean) 156๋ฐ•

    Data Science for Entrepreneurship Research:Studying Demand Dynamics for Entrepreneurial Skills in the Netherlands

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    The recent rise of big data and artificial intelligence (AI) is changing markets, politics, organizations, and societies. It also affects the domain of research. Supported by new statistical methods that rely on computational power and computer science --- data science methods --- we are now able to analyze data sets that can be huge, multidimensional, unstructured, and are diversely sourced. In this paper, we describe the most prominent data science methods suitable for entrepreneurship research and provide links to literature and Internet resources for self-starters. We survey how data science methods have been applied in the entrepreneurship research literature. As a showcase of data science techniques, based on a dataset of 95% of all job vacancies in the Netherlands over a 6-year period with 7.7 million data points, we provide an original analysis of the demand dynamics for entrepreneurial skills in the Netherlands. We show which entrepreneurial skills are particularly important for which type of profession. Moreover, we find that demand for both entrepreneurial and digital skills has increased for managerial positions, but not for others. We also find that entrepreneurial skills were significantly more demanded than digital skills over the entire period 2012-2017 and that the absolute importance of entrepreneurial skills has even increased more than digital skills for managers, despite the impact of datafication on the labor market. We conclude that further studies of entrepreneurial skills in the general population --- outside the domain of entrepreneurs --- is a rewarding subject for future research
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