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on Big Data |
| By: | Niebel, Thomas; Rasel, Fabienne; Viete, Steffen |
| Abstract: | This paper analyzes the relationship between firms' use of big data analytics and their innovative performance in terms of product innovations. Since big data technologies provide new data information practices, they create novel decision-making possibilities, which are widely believed to support firms' innovation process. Applying German firm-level data within a knowledge production function framework we find suggestive evidence that big data analytics is a relevant determinant for the likelihood of a firm becoming a product innovator as well as for the market success of product innovations. These results hold for the manufacturing as well as for the service sector but are contingent on firms' investment in IT-specific skills. Subsequent analyses suggest that firms in the manufacturing and service sector rely on different data sources and data-related firm practices in order to reap the benefits of big data. Overall, the results support the view that big data analytics have the potential to enable innovation. |
| Keywords: | big data,data-driven decision-making,product innovation,firm-level data |
| JEL: | D22 L20 O33 |
| Date: | 2017 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:zewdip:17053&r=big |
| By: | Leonid Gokhberg (National Research University Higher School of Economics); Ilya Kuzminov (National Research University Higher School of Economics); Pavel Bakhtin (National Research University Higher School of Economics); Elena Tochilina (National Research University Higher School of Economics); Alexander Chulok (National Research University Higher School of Economics); Anton Timofeev (National Research University Higher School of Economics); Alina Lavrinenko (National Research University Higher School of Economics) |
| Abstract: | The paper discloses a new approach to emerging technologies identification, which strongly relies on capacity of big data analysis, namely text mining augmented by syntactic analysis techniques. It discusses the wide context of the task of identifying emerging technologies in a systemic and timely manner, including its place in the methodology of foresight and future-oriented technology analysis, its use in horizon scanning exercises, as well as its relation to the field of technology landscape mapping and tech mining. The concepts of technology, emerging technology, disruptive technology and other related terms are assessed from the semantic point of view. Existing approaches to technology identification and technology landscape mapping (in wide sense, including entity linking and ontology-building for the purposes of effective STI policy) are discussed, and shortcomings of currently available studies on emerging technologies in agriculture and food sector (A&F) are analyzed. The opportunities of the new big-data-augmented methodology are shown in comparison to existing results, both globally and in Russia. As one of the practical results of the study, the integrated ontology of currently emerging technologies in A&F sector is introduced. The directions and possible criteria of further enhancement and refinement of proposed methodology are contemplated, with special attention to use of bigger volumes of data, machine learning and ontology-mining / entity linking techniques for the maximum possible automation of the analytical work in the discussed field. The practical implication of the new approach in terms of its effectiveness and efficiency for evidence-based STI policy and corporate strategic planning are shortly summed up as well |
| Keywords: | Emerging technologies, foresight, strategic planning, STI policy, Russian Federation, agriculture, food sector, text mining, tech mining, STI landscape mapping, horizon scanning |
| JEL: | O1 O3 |
| Date: | 2017 |
| URL: | http://d.repec.org/n?u=RePEc:hig:wpaper:76sti2017&r=big |
| By: | Rui Luo; Weinan Zhang; Xiaojun Xu; Jun Wang |
| Abstract: | In this paper, we show that the recent integration of statistical models with deep recurrent neural networks provides a new way of formulating volatility (the degree of variation of time series) models that have been widely used in time series analysis and prediction in finance. The model comprises a pair of complementary stochastic recurrent neural networks: the generative network models the joint distribution of the stochastic volatility process; the inference network approximates the conditional distribution of the latent variables given the observables. Our focus here is on the formulation of temporal dynamics of volatility over time under a stochastic recurrent neural network framework. Experiments on real-world stock price datasets demonstrate that the proposed model generates a better volatility estimation and prediction that outperforms stronge baseline methods, including the deterministic models, such as GARCH and its variants, and the stochastic MCMC-based models, and the Gaussian-process-based, on the average negative log-likelihood measure. |
| Date: | 2017–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1712.00504&r=big |
| By: | Dengler, Sebastian (Tilburg University, School of Economics and Management) |
| Abstract: | This doctoral thesis aims to advance our understanding of major topics of concern in the 21st century using theoretical as well as empirical economic methodologies. All three topics do and will continue to affect people’s lives as they can substantially shape the functioning of our societies. Thematically linked, Chapter 2 and 3 both focus on privacy choices and their consequences in the context of big data algorithms that target individual consumers. In contrast, Chapter 3 and 4 are linked methodologically as both present results from economic laboratory experiments, where the former focuses on cognitive challenges of individual decision-makers and the latter on challenges to coordination and cooperation between decision-makers. Chapter 2 presents results from a theoretical model where consumers face a monopolistic seller who is not only capable of perfect price discrimination but also more strategically sophisticated than the consumers. The model shows that consumers use a costly privacy-protective sales channel even in the absence of an explicit taste for privacy if they are not too strategically sophisticated. Chapter 3 presents results from an economic laboratory experiment related to the model developed before. Finding substantial deviations from Nash equilibrium predictions. Addressing cognitive constraints often present in privacy choices, some evidence for two alternative explanations is found: level-k thinking and reinforcement learning. A policy treatment resembling privacy-by-default mechanisms leads to a strong increase in hiding behavior. Chapter 4 presents results from an economic laboratory experiment of a dynamic resource extraction game that mimics the global multi-generation planning problem for climate change and fossil fuel extraction. The findings from this experiment suggest that successful cooperation does not only need to overcome a gap between individual incentives and public interests. There is also a fundamental heterogeneity between subjects with respect to beliefs and preferences about the way in which this should be achieved. |
| Date: | 2017 |
| URL: | http://d.repec.org/n?u=RePEc:tiu:tiutis:2e48fcbf-1584-416d-ae88-23099889fa59&r=big |