Open Data for Open Innovation: An Analysis of Literature Characteristics

Corrales-Garay, Diego; Mora-Valentín, Eva-María; Ortiz‐de‐Urbina‐Criado, Marta

doi:10.3390/fi11030077

Cited by 18 publications

(14 citation statements)

References 78 publications

(49 reference statements)

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“…The advantages of the two-case method are more robust findings, higher generalizability, and less bias than if only one case were analyzed. This approach is consistent with prior studies who use multiple case studies to examine the adoption of OGD [14,15,33,69].…”

Section: Case Study Approach and Industry Selectionsupporting

confidence: 75%

Mining Open Government Data for Business Intelligence Using Data Visualization: A Two-Industry Case Study

Gottfried

Hartmann

Yates

2021

JTAER

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The business intelligence (BI) market has grown at a tremendous rate in the past decade due to technological advancements, big data and the availability of open source content. Despite this growth, the use of open government data (OGD) as a source of information is very limited among the private sector due to a lack of knowledge as to its benefits. Scant evidence on the use of OGD by private organizations suggests that it can lead to the creation of innovative ideas as well as assist in making better informed decisions. Given the benefits but lack of use of OGD to generate business intelligence, we extend research in this area by exploring how OGD can be used to generate business intelligence for the identification of market opportunities and strategy formulation; an area of research that is still in its infancy. Using a two-industry case study approach (footwear and lumber), we use latent Dirichlet allocation (LDA) topic modeling to extract emerging topics in these two industries from OGD, and a data visualization tool (pyLDAVis) to visualize the topics in order to interpret and transform the data into business intelligence. Additionally, we perform an environmental scanning of the environment for the two industries to validate the usability of the information obtained. The results provide evidence that OGD can be a valuable source of information for generating business intelligence and demonstrate how topic modeling and visualization tools can assist organizations in extracting and analyzing information for the identification of market opportunities.

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Section: Case Study Approach and Industry Selectionsupporting

confidence: 75%

Mining Open Government Data for Business Intelligence Using Data Visualization: A Two-Industry Case Study

Gottfried

Hartmann

Yates

2021

JTAER

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“…The trend indicates a significant growth starting with 2017 with a maximum in 2019. It is our belief that the ascending trend illustrated for 2017-2020 will continue given that in the last couple of years open data initiatives favored data-driven innovation [49] and fostered the delivery of ML based smart solutions. The fact that we have only five records in 2021 is attributable to the searching time (June 2021); as a consequence, we cannot have a final number for the papers published in 2021.…”

Section: Resultsmentioning

confidence: 99%

“…Given the nature of imperfect data within open data sources and the seemingly random data points generated within a city, deep learning may be the most relevant tactic to use for such circumstances. Besides, unlike traditional ML algorithms, DL can deal with great amounts of data, therefore, providing high-level solutions to the smart city problems [49].…”

Section: Resultsmentioning

confidence: 99%

Open Data Based Machine Learning Applications in Smart Cities: A Systematic Literature Review

et al. 2021

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Machine learning (ML) has already gained the attention of the researchers involved in smart city (SC) initiatives, along with other advanced technologies such as IoT, big data, cloud computing, or analytics. In this context, researchers also realized that data can help in making the SC happen but also, the open data movement has encouraged more research works using machine learning. Based on this line of reasoning, the aim of this paper is to conduct a systematic literature review to investigate open data-based machine learning applications in the six different areas of smart cities. The results of this research reveal that: (a) machine learning applications using open data came out in all the SC areas and specific ML techniques are discovered for each area, with deep learning and supervised learning being the first choices. (b) Open data platforms represent the most frequently used source of data. (c) The challenges associated with open data utilization vary from quality of data, to frequency of data collection, to consistency of data, and data format. Overall, the data synopsis as well as the in-depth analysis may be a valuable support and inspiration for the future smart city projects.

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“…Although the assessment yielded 13 articles that contained a review of open data literature, five did not focus on open data policy-making and, therefore, were eliminated from further examination (Corrales-Garay et al, 2019;Crusoe & Melin, 2018;Hassan & Twinomurinzi, 2018;Lowry, 2015;Virkar & Pereira, 2018). We conclude that much research has been done in the field of open data in the past few decades, yet the number of conducted literature reviews concerning open data research is limited.…”

Section: Research Approachmentioning

confidence: 97%

Open data policy-making: A review of the state-of-the-art and an emerging research agenda

Hinnant

2019

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This section presents a selection of papers on open data policy-making from the 19th Annual International Conference on Digital Government Research 2018 (dg.o 2018). To position the research discussed in this section meaningfully, our introductory article aims to create an overview of the state-of-the-art of open data policy-making research and to derive an emerging research agenda from this overview. We found that much research has been done in the field of open data in the past few decades. However, the number of conducted systematic literature reviews concerning open data research is limited and literature reviews that have included aspects related to open data policy-making are even more rare. Our analysis of eight systematic literature review articles that include the topic of open data policy-making shows that these articles contain four main content-related elements: open data policy-making, a discussion of open data policy-making research, theory used in open data policy-making research and suggested areas for future research about open data policy-making. We discuss each of these areas and we outline emerging research directions categorized by three main topics: 1) open data policy-making and theory development, 2) open data policy-making effects, and 3) open data policy-making from a multi-actor perspective. Finally, we provide an overview of the papers included in this section on open data policy-making.

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Open Data for Open Innovation: An Analysis of Literature Characteristics

Cited by 18 publications

References 78 publications

Mining Open Government Data for Business Intelligence Using Data Visualization: A Two-Industry Case Study

Mining Open Government Data for Business Intelligence Using Data Visualization: A Two-Industry Case Study

Open Data Based Machine Learning Applications in Smart Cities: A Systematic Literature Review

Open data policy-making: A review of the state-of-the-art and an emerging research agenda

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