Analyzing Entrepreneurial Social Networks with Big Data

Wang, Feng; Mack, Elizabeth A.; Maciewjewski, Ross

doi:10.1080/24694452.2016.1222263

Cited by 28 publications

(32 citation statements)

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“…Fifth, information on user interactions on social media can be used to infer spatial social networks and detect community boundaries (Shao, Zhang, & Li, ). Network analysis was more commonly conducted at the country level to identify social networks of entrepreneurs (Wang, Mack, & Maciewjewski, ) and boundaries of travelers’ communities (Thiemann et al, ; Liu, Sui, Kang, & Gao, ). Sixth, variations in spatial presence of human populations captured by social media check‐in data can reveal land use patterns, in which the accuracy of urban land use mapping can be substantially improved if remote sensing and social sensing (Jendryke et al, ; Liu, Gong, et al, ) data are integrated.…”

Section: Resultsmentioning

confidence: 99%

Big data‐enabled social sensing in spatial analysis: Potentials and pitfalls

Liao

Brown

Ding

et al. 2018

Transactions in GIS

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Over the past decade, there has been an unprecedented boom in the application of big data derived from location‐aware technology in GIScience. However, reflections on data quality lead to concerns about inaccurate or even erroneous spatial analysis results. This article contributes a meta‐analysis of big data‐enabled social sensing in spatial analysis. Big data‐enabled research has substantially enhanced our understanding of human population distribution and dynamics, land use, travel behavior, movement patterns, and spatial social networks from city to country to global levels. However, the effectiveness of big data‐enabled social sensing can be undermined by five critical issues related to research design: data volume, spatial coverage, locational information quality, and representativeness across socioeconomic and demographic groups. We suggest a framework for addressing the inherent bias in spatial big data at multiple stages of the research lifecycle and applying them to appropriate topics and scales of applications.

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Section: Resultsmentioning

confidence: 99%

Big data‐enabled social sensing in spatial analysis: Potentials and pitfalls

Liao

Brown

Ding

et al. 2018

Transactions in GIS

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“…Our research is explorative in terms of the core indicators used because this is the first attempt to apply the German web portal 'Presseportal' of the dpa (German Press Agency) subsidiary 'news aktuell' for research on regional entrepreneurial activity and media coverage. Since research on this topic is still rare (see also Wang et al 2017), opening up new approaches through Big Data could lead to much-needed progress.…”

Section: Introductionmentioning

confidence: 99%

New(s) data for entrepreneurship research? An innovative approach to use Big Data on media coverage

et al. 2019

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Although conventional register and survey data on entrepreneurship have enabled remarkable insights into the phenomenon, the added value has slowed down noticeably over the last decade. There is a need for fresh approaches utilising modern data sources such as Big Data. Until now, it has been quite unknown whether Big Data actually embodies valuable contributions for entrepreneurship research and where it can perform better or worse than conventional approaches. To contribute towards the exploration of Big Data in entrepreneurship research, we use a newly developed dataset based on publications of the German Press Agency (dpa) to explore the relationship between news coverage of entrepreneurship and regional entrepreneurial activity. Furthermore, we apply sentiment analysis to investigate the impact on sentiment of entrepreneurial press releases. Our results show mixed outcomes regarding the relationship between reporting of entrepreneurial events, i.e., media coverage, and entrepreneurial activity in German planning regions. At this stage, our empirical results reject the idea of a strong relationship between actual entrepreneurial activities in regions and the intensity of it being reported. However, the results also imply much potential of Big Data approaches for further research with more sophisticated methodology approaches. Our paper provides an entry point into Big Data usage in entrepreneurship research and we suggest a number of relevant research opportunities based on our results.

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“…Another possibility may be a social network service-based analysis. As shown in a Twitter-based regional entrepreneurship study (Wang, Mack and Maciewjewski, 2017), the DOD SBIR program-related big data (e.g., Facebook, Twitter, and Google Search) may be helpful to understand the network or regional context of technology transition. For instance, the use of Twitter data such as @dodsbir, @AF_SBIR_STTR, and @BusinessDefense may enable researchers to explore where the SBIR-and technology transition-related information moves and how those networks affect the DOD SBIR technology transition.…”

Section: Future Studymentioning

confidence: 99%

“…With the advance of geographic information system technology, in particular, scholars and policymakers tend to look into high-resolution geographic data. For instance, Wang, Mack and Maciewjewski (2017) used the latitude and longitude information of Twitter users with hashtags such as #smallbiz and #entrepreneur to analyze entrepreneurial networks.…”

Section: D1 Regional Differences In Entrepreneurial Innovationmentioning

confidence: 99%

Accelerating the Transition of Technologies Created through the U.S. Department of Defense Small Business Innovation Research Program

Ryu

2017

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For more information on this publication, visit http://www.rand.org/pubs/rgs_dissertations/RGSD405.html Published by the RAND Corporation, Santa Monica, Calif. © Copyright 2017 RAND Corporation R® is a registered trademark Limited Print and Electronic Distribution RightsThis document and trademark(s) contained herein are protected by law. This representation of RAND intellectual property is provided for noncommercial use only. Unauthorized posting of this publication online is prohibited. Permission is given to duplicate this document for personal use only, as long as it is unaltered and complete. Permission is required from RAND to reproduce, or reuse in another form, any of its research documents for commercial use. For information on reprint and linking permissions, please visit www.rand.org/pubs/permissions.html.The RAND Corporation is a research organization that develops solutions to public policy challenges to help make communities throughout the world safer and more secure, healthier and more prosperous. RAND is nonprofit, nonpartisan, and committed to the public interest. RAND's publications do not necessarily reflect the opinions of its research clients and sponsors.Support RAND Make a tax-deductible charitable contribution at www.rand.org/giving/contribute www.rand.org iii AbstractThe objective of this dissertation is to better understand the contextual effects on the success of the transition of technologies generated through the U.S. Department of Defense (DOD) Small Business Innovation Research (SBIR) program, and to pull out policy recommendations for facilitating the way the SBIR transitions technologies from abstract concepts to concrete engineering capabilities that the U.S. can use in its military systems. To that end, I explore how social (network), spatial (region), and industrial (technology) factors affect the SBIR's ability to do this. Specifically, I examine the contextual effects of network, regional, and industrial characteristics on the success of technology transition using various regression models. To incorporate multilevel factors, in particular, I employ hierarchical linear models with firm-and region/ industry-level variables related to state-level innovation capacity, anchor tenants, innovation brokers, technology life cycle, technology market, technological and network positions of SBIR awardees in relation to the DOD (including its research laboratories) and prime contractors.Findings of this dissertation include: small firms' larger SBIR awards, higher age, more cutting-edge technology or high-tech focus are more likely to lead to success in technology transition; small firms' higher technological distance (in particular, relative to prime contractors) and more central network position are more likely to facilitate technology transition; the number of defense labs in a state may matter in small firms' winning federal procurement contracts and improving their return on investment; the number of DOD mentors (i.e., large prime contractors who signed up for mentoring small busin...

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Analyzing Entrepreneurial Social Networks with Big Data

Cited by 28 publications

References 50 publications

Big data‐enabled social sensing in spatial analysis: Potentials and pitfalls

Big data‐enabled social sensing in spatial analysis: Potentials and pitfalls

New(s) data for entrepreneurship research? An innovative approach to use Big Data on media coverage

Accelerating the Transition of Technologies Created through the U.S. Department of Defense Small Business Innovation Research Program

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