How to foster innovation: A data-driven approach to measuring economic competitiveness

Kuhlman, Caitlin; Ramamurthy, Karthikeyan Natesan; Sattigeri, Prasanna; Lozano, Aurélie; Lei, Chong; Reddy, Chandan K.; Mojsilović, Aleksandra; Varshney, Kush R.

doi:10.1147/jrd.2017.2741820

Cited by 18 publications

(12 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The results obtained showed that for the selected countries, innovation performance depends on a developed research system, improved conditions for entrepreneurship, and a higher degree of innovation performances. Through job creation and the development of new products and services, innovation contributes to the increase of competitiveness and represents a key factor leading countries' economic growth (Kuhlman et al 2017). Innovation represents an important pillar for global competitiveness (Ghoniem and El Khouly 2012) that contributes also to the improvement of international competitiveness as found by Özçelik and Taymaz (2004) in their study on Turkey.…”

Section: Literature Reviewmentioning

confidence: 99%

The Quality of Entrepreneurial Activity and Economic Competitiveness in European Union Countries: A Panel Data Approach

Rusu

Dornean

2019

Administrative Sciences

View full text Add to dashboard Cite

To increase competitiveness, a country has to outperform its competitors in terms of research and innovation, entrepreneurship, competition, and education. In this paper, we aim to test the relationship between the quality of entrepreneurial activity and the economic competitiveness for the European Union countries by using panel data estimation techniques. Our research considers a sample of 28 EU countries over the period 2011–2017. For the empirical investigation we apply panel data regression models. The results obtained show that business, macroeconomic environment and the quality of entrepreneurship are significant determinants of economic competitiveness of EU countries. Thus, we identify significant positive relations between innovation rate, inflation rate, FDI and economic competitiveness, and significant negative relations between expectations regarding job creation, tax rate, costs and competitiveness. Our study completes the literature by analyzing the relationship between the quality of entrepreneurship and the competitiveness of countries, for an extensive sample formed by all the 28 countries members of the European Union for a period of seven recent years.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

The Quality of Entrepreneurial Activity and Economic Competitiveness in European Union Countries: A Panel Data Approach

Rusu

Dornean

2019

Administrative Sciences

View full text Add to dashboard Cite

show abstract

“…Our brief literature review below shows that applications of artificial Neural Networks (NNs) and Deep Learning Neural Networks (DLNNs) in economics are scarce, but they have provided useful insights where they were used. To the best our knowledge, there are two papers closely related to ours (Al Shami, 2011;Kuhlman, et al 2017)). Both empirical studies of NNs falls within the paradigm of unsupervised learning, while the proposal in this article falls within the paradigm of supervised learning.…”

Section: Introductionmentioning

confidence: 92%

Using deep learning neural networks to predict the knowledge economy index for developing and emerging economies

Andrés

Otero

Amavilah

2021

Expert Systems with Applications

View full text Add to dashboard Cite

Missing values and the inconsistency of the measures of the knowledge economy remain vexing problems that hamper policy-making and future research in developing and emerging economies. This paper contributes to the new and evolving literature that seeks to advance better understanding of the importance of the knowledge economy for policy and further research in developing and emerging economies. In this paper we use a supervised machine deep learning neural network (DLNN) approach to predict the knowledge economy index of 71 developing and emerging economies during the 1995-2017 period. Applied in combination with a data imputation procedure based on the K-closest neighbor algorithm, DLNN is capable of handling missing data problems better than alternative methods. A 10-fold validation of the DLNN yielded low quadratic and absolute error (0,382 +-0,065). The results are robust and efficient, and the model's predictive power is high. There is a difference in the predictive power when we disaggregate countries in all emerging economies versus emerging Central European countries. We explain this result and leave the rest to future endeavors. Overall, this research has filled in gaps due to missing data thereby allowing for effective policy strategies. At the aggregate level development agencies, including the World Bank that originated the KEI, would benefit from our approach until substitutes come along.

show abstract

“…The topic of economic impact appears in two documents that study the economic effects of using open data [56], [57]. The impacts on competitiveness are analyzed in two documents that examine how open-access data can improve competitiveness in a knowledge-based economy [76]; indices are also proposed that use open data to measure the innovation of various countries around the world [93].…”

Section: Open Data Impact Process For Open Innovation a Topics Imentioning

confidence: 99%

A Research Agenda on Open Data Impact Process for Open Innovation

Corrales-Garay

Ortiz‐de‐Urbina‐Criado

Mora-Valentín

2020

IEEE Access

View full text Add to dashboard Cite

Open data and open innovation are two topics currently attracting the attention of academics. But no previous studies consider these fields in combination while using a bibliometric approach. Thus, the aim of this paper is to understand the relationship between open innovation and open data. Two research questions have been formulated: 1) What are the main topics studied in the literature that combine both lines of research? and 2) How can the open innovation paradigm be integrated in the open data impact process? To address the first question, a co-word analysis is used to identify the main topics investigated in the open innovation and open data literature. Based on our results, to answer the second research question, the topics are grouped and analyzed considering a model of the open data impact process. Finally, some future research lines to analyze the open data impact process for open innovation are presented. For example, future research could focus on questions such as (1) What kind of applications can be created through the reuse of open data?; and (2) How do open innovation processes influence the reuse of open data? INDEX TERMS Open data, open innovation, co-word analysis, theoretical model. the Ph.D. degree in management from Rey Juan Carlos University, Spain. She is currently an Associate Professor of management with Rey Juan Carlos University. Her research has been published in the

show abstract

How to foster innovation: A data-driven approach to measuring economic competitiveness

Cited by 18 publications

References 7 publications

The Quality of Entrepreneurial Activity and Economic Competitiveness in European Union Countries: A Panel Data Approach

The Quality of Entrepreneurial Activity and Economic Competitiveness in European Union Countries: A Panel Data Approach

Using deep learning neural networks to predict the knowledge economy index for developing and emerging economies

A Research Agenda on Open Data Impact Process for Open Innovation

Contact Info

Product

Resources

About