Search results

Applied econometric analysis is often performed using data collected from large-scale surveys. These surveys use complex sampling plans in order to reduce costs and increase the estimation efficiency for subgroups of the population. These sampling plans result in unequal inclusion probabilities across units in the population. The purpose of this paper is to derive the asymptotic properties of a design-based nonparametric regression estimator under a combined inference framework. The nonparametric regression estimator considered is the local constant estimator. This work contributes to the literature in two ways. First, it derives the asymptotic properties for the multivariate mixed-data case, including the asymptotic normality of the estimator. Second, I use least squares cross-validation for selecting the bandwidths for both continuous and discrete variables. I run Monte Carlo simulations designed to assess the finite-sample performance of the design-based local constant estimator versus the traditional local constant estimator for three sampling methods, namely, simple random sampling, exogenous stratification and endogenous stratification. Simulation results show that the estimator is consistent and that efficiency gains can be achieved by weighting observations by the inverse of their inclusion probabilities if the sampling is endogenous.

The paper aims to investigate the emergence of science policy in the states of the USA, drawing attention to the fact that every state has a science and technology agency and multiple programs that attempt to raise the level of science and technology in the state and attract resources from elsewhere.

The paper builds upon the authors' previous study of high‐tech growth and renewal in Boston and Silicon Valley through analysis of documents and interviews with key actors in universities, S&T policy units of the Governor's association to detail the bottom‐up initiatives exemplifying the US innovation policy model.

The path dependent elements in US science and technology policy are an enhanced role for universities, an ambivalent role for national government and industry and a growing role for state and local government. Federal research funds, largely confined to support of agricultural research before the Second World War, became available for a variety of civilian and military purposes, on an ongoing basis, after the war. An assisted linear model of coordinated innovation mechanisms has been constructed on this base to translate inventions into economic activity through university‐industry‐government interactions.

The paper shows that S&T policy at the state level fills gaps in university‐industry relations, leverages federal R&D spending and enhances local comparative and competitive advantage.

The purpose of this paper is to reveal the characteristics and evolution path of entrepreneurial university in China, through a case study of Huazhong University of Science and Technology (HUST) which has the reputation of “Chinese miniature of higher education” and in which entrepreneurial practice displays “Chinese characteristics”.

The paper has originated from the ongoing thinking after completion of the author's Doctoral thesis. So, a large number of materials collected during the dissertation research will be used in this case, much of which are first‐hand, from investigations, site visits and interviews. As in the Doctoral thesis, a case study approach is also adopted in this paper.

Through the inspection of HUST, it has been found that its entrepreneurial practice is in full compliance with the criteria of the entrepreneurial university. To some extent, the theory of entrepreneurial university is being revised by the practice of HUST. However, there are some “Chinese characteristics” which should be given special attention, such as university‐run enterprises, government‐pulled triple helix, the tradition of government‐run universities and so on.

The paper will help the policy makers clarify the development situation of entrepreneurial university in China, which involves choosing the transformation paths and future directions, and to achieve a good way of building entrepreneurial university in accordance with China's national conditions. At the same time, it will promote international understanding of the entrepreneurial university in China.

Knowing financial and economic information beforehand benefits in planning and developing policies for every country especially for a developing country like Thailand and for other Asian countries. Unfortunately, missing data or non-response plays an essential role in many areas of studies including finance and economics. Eradication of missing data in a proper way before further analysis can gain remarkable outcomes and can be effective for planning policies. This review on the generalized regression estimators for population total can be applied to financial, economic and other data when missing data are present.

The generalized regression estimators for estimating population total, including the variance estimators under unequal probability sampling without replacement with missing data are explored under the reverse framework. Applications to financial and economic data in Thailand are also reviewed.

The review of literatures related to the proposed estimator shows the best performance, giving smaller variances in all scenarios.

The generalized regression estimators can assist in estimating financial and economic data that contain missing values with different missing mechanisms and can be used in other applications which help gain more superior estimators.

The science policy has been at the core business of contemporary nations, and determining how to establish a constructive contract between the wider society and the science/academia community has become a continuous challenge and major task. The purpose of this paper is to draw on Bush’s (1945) classical works and other scholarly stances in an attempt to reveal how research funding discourses and practices in Taiwanese universities have taken shape and been implemented.

Against this broader context, the authors examine the main elements of official documents and significant statistical evidence from recent years.

In summary, basic research investment has comparatively underperformed while the business sector has contributed relatively higher expenditures to the university sector at the international level. A strong state-led approach has been identified as the key characteristics of research funding policy for industrial development/economic growth or social problem solving. Although not making an effort to “save the nation,” the state has been dominant in steering the direction of priority areas and issues for university research in order to achieve better international competitiveness and, in turn, nation building.

The authors examine the impact of the interplay between science’s social contract with society and rhetorical devices on the institutionalization of the university research funding policy framework in Taiwan. The exploration of this interplay leads the authors to elaborate tensions between the government, industry, universities, and research communities in Taiwan. As in other contexts, the race between social accountability and academic autonomy has evolved to be a significant element of these tensions in Taiwan. For better reflecting the public needs or social demands, a greater autonomy for the science community is desirable and favorable for long-term development.

The science policy is a rarely addressed but critical issue for the past two decades. Along with the increasing demand on value for money publicly funded research, societal accountability, and the international competition caused by league tables and cutting-edge technology innovation, this paper draws on classical and current mainstream discourses of science’s contract with society by investigating into Taiwanese research funding in the higher education sector. All findings are highly original for theoretical and practical implications.

This paper aims to propose a performance measurement system to evaluate the key aspects of entrepreneurial activities in Brazilian universities.

This study was developed in two phases. Both phases consisted of a survey sent to Brazilian universities (public, private and not-for-profit) whose technology transfer offices (TTO) had contributed to the annual report by the Ministry of Science, Technology and Innovation (MCTI, 2015), which evaluates the implementation of the Innovation Law. Multiple correspondence analysis was used to analyze the answers.

A set of 13 indicators and 13 characteristics of the organizational structure of the institutions was identified for the purpose of evaluating the level of development of the entrepreneurship activities.

The main limitation of this study relates to the low quality of the survey responses. It was not possible to qualitatively validate all the selected indicators. This is because universities are still not internally organized, because the higher authorities do not enforce the collection and treatment of data based on the existing legislation.

The results of this study, with the definition of indicators, can be used to inform public policy for the stimulation of entrepreneurship in other countries and regions.

This study aims to introduce a method based on link analysis to investigate the structure of the R&D support infrastructure associated with science parks in order to determine whether this webometric approach gives plausible results.

Three science parks from Yorkshire and the Humber in the UK were analysed with webometric and social network analysis techniques. Interlinking networks were generated through the combination of two different data sets extracted from three sources (Yahoo!, Bing, SocSciBot).

These networks suggest that institutional sectors, representing business, universities and public bodies, are primarily tied together by a core formed by research institutions, support structure organisations and business developers. The comparison of the findings with traditional indicators suggests that the web‐based networks reflect the offline conditions and policy measures adopted in the region, giving some evidence that the webometric approach is plausible to investigating science park networks.

This is the first study that applies a web‐based approach to investigate to what extent the science parks facilitate a closer interaction between the heterogeneous organisations that converge in R&D networks. This indicates that link analysis may help to get a first insight into the organisation of the R&D support infrastructure provided by science parks.

The purpose of this paper is to show how non-linear causal modelling knowledge, already accumulated by other disciplines, is central to unravelling wicked problem scoping and definition in social marketing.

The paper is an illustrative case study approach, highlighting three real-world exemplars of causal modelling for wicked problem definition.

The findings show how the traditional linear research methods of social marketing are not sensitive enough to the dynamics and complexities of wicked problems. A shift to non-linear causal modelling techniques and methods, using interaction as the unit of analysis, provides insight and understanding into the chains of causal dependencies underlying social marketing problems.

This research extends the application of systems thinking in social marketing through the illustration of three non-linear causal modelling techniques, namely, collective intelligence, fuzzy cognitive mapping and system dynamics modelling. Each technique has the capacity to visualise structural and behavioural properties of complex systems and identify the central interactions driving behaviour.

Non-linear causal modelling methods provide a robust platform for practical manifestations of collaborative-based strategic projects in social marketing, when used with participatory research, suitable for micro, meso, macro or systems wide interventions.

The paper identifies non-linear causality as central to wicked problem scoping identification, documentation and analysis in social marketing. This paper advances multi-causal knowledge in the social marketing paradigm by using fuzzy, collective and interpretative methods as a bridge between linear and non-linear causality in wicked problem research.

The purpose of this paper is to propose and analyze the use of local surrogate models to improve differential evolution’s (DE) overall performance in computationally expensive problems.

DE is a popular metaheuristic to solve optimization problems with several variants available in the literature. Here, the offspring are generated by means of different variants, and only the best one, according to the surrogate model, is evaluated by the simulator. The problem of weight minimization of truss structures is used to assess DE’s performance when different metamodels are used. The surrogate-assisted DE techniques proposed here are also compared to common DE variants. Six different structural optimization problems are studied involving continuous as well as discrete sizing design variables.

The use of a local, similarity-based, surrogate model improves the relative performance of DE for most test-problems, specially when using r-nearest neighbors with r = 0.001 and a DE parameter F = 0.7.

The proposed methods have no limitations and can be applied to solve constrained optimization problems in general, and structural ones in particular.

The proposed techniques can be used to solve real-world problems in engineering. Also, the performance of the proposals is examined using structural engineering problems.

The main contributions of this work are to introduce and to evaluate additional local surrogate models; to evaluate the effect of the value of DE’s parameter F (which scales the differences between components of candidate solutions) upon each surrogate model; and to perform a more complete set of experiments covering continuous as well as discrete design variables.

Business Intelligence has gained a significant attraction in the recent past and facilitates managers for efficient business decision-making. Over the years, the attraction toward the cryptocurrency (CC) market has increased. Since the CC market is highly volatile, it is extremely sensitive to shocks and web data related to large events happening around the globe.

This research study provides a business intelligence model to predict five top-performing CCs. In this study, deep learning, linear regression and support vector regression (SVR) are used to predict CC prices. The sentiment of some mega-events is also used to enhance the performance of these models.

The results show that models of business intelligence such as deep learning and SVR provide better results. Moreover, the results show that the incorporation of social media sentiment data significantly improves the performance of the proposed models. The overall accuracy of the model improves approximately twofold when multiple event sentiments were incorporated.

The use of social media sentiment of global and local events for different countries along with deep learning for CC forecasting.