scholarly journals Turning Crisis into Opportunities: How a Firm Can Enrich Its Business Operations Using Artificial Intelligence and Big Data during COVID-19

2021 ◽  
Vol 13 (22) ◽  
pp. 12656
Author(s):  
Yasheng Chen ◽  
Mohammad Islam Biswas
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Inventory Management ◽  
Social Science Research ◽  
Science Research ◽  
Theoretical Ground ◽  
Budget Planning ◽  
Business Operations ◽  
Big Data Applications ◽  
Business Cases

The COVID-19 pandemic has severe impacts on global health and social and economic safety. The present study discusses strategies for turning the COVID-19 crisis into opportunities to use artificial intelligence (AI) and big data in business operations. Based on the shared experience and theoretical ground, researchers identified five major business challenges during the COVID-19 pandemic: production and supply-chain disruption, appropriate business model selection, inventory management, budget planning, and workforce management. These five challenges were outlined with eight business cases as examples of companies that had already utilized AI and big data for their business operations during the COVID-19 pandemic. The outcomes of this study provide valuable insights into contemporary social science research and business management with AI and big data applications as a business response to any crisis in the future.

scholarly journals The role of administrative data in the big data revolution in social science research

2016 ◽  
Vol 59 ◽  
pp. 1-12 ◽  
Author(s):  
Roxanne Connelly ◽  
Christopher J. Playford ◽  
Vernon Gayle ◽  
Chris Dibben
Keyword(s):  
Big Data ◽  
Social Science ◽  
Administrative Data ◽  
Social Science Research ◽  
Science Research

scholarly journals Big data and Belmont: On the ethics and research implications of consumer-based datasets

2021 ◽  
Vol 8 (2) ◽  
pp. 205395172110481
Author(s):  
Remy Stewart
Keyword(s):  
Social Sciences ◽  
Big Data ◽  
Social Science Research ◽  
Research Participant ◽  
Science Research ◽  
Business Practices ◽  
Ethical Challenges ◽  
Marketing Risk ◽  
Brokerage Firms

Consumer-based datasets are the products of data brokerage firms that agglomerate millions of personal records on the adult US population. This big data commodity is purchased by both companies and individual clients for purposes such as marketing, risk prevention, and identity searches. The sheer magnitude and population coverage of available consumer-based datasets and the opacity of the business practices that create these datasets pose emergent ethical challenges within the computational social sciences that have begun to incorporate consumer-based datasets into empirical research. To directly engage with the core ethical debates around the use of consumer-based datasets within social science research, I first consider two case study applications of consumer-based dataset-based scholarship. I then focus on three primary ethical dilemmas within consumer-based datasets regarding human subject research, participant privacy, and informed consent in conversation with the principles of the seminal Belmont Report.

Parcels, points, and proximity: Can exhaustive sources of big data improve measurement in cities?

2018 ◽  
Vol 47 (4) ◽  
pp. 695-715
Author(s):  
Kevin Kane ◽  
Young-An Kim
Keyword(s):  
New York ◽  
Big Data ◽  
Urban Landscape ◽  
Smart Cities ◽  
Social Science Research ◽  
Science Research ◽  
Distance Measures ◽  
Urban Services ◽  
Improve Measurement ◽  
Business Establishment

While there has been no shortage of discussion of urban big data, smart cities, and cities as complex systems, there has been less discussion of the implications of big data as a source of individual data for planning and social science research. This study takes advantage of increasingly available land parcel and business establishment data to analyze how the measurement of proximity to urban services or amenities performed in many fields can be impacted by using these data—which can be considered “individual” when compared to aggregated origins or destinations. We use business establishment data across five distinctive US cities: Long Beach, Irvine, and Moreno Valley in California; Milwaukee, Wisconsin; and the New York borough of Staten Island. In these case studies, we show how aggregation error, a previously recognized concern in using census-type data, can be minimized through careful choice of distance measures. Informed by these regions, we provide recommendations for researchers evaluating the potential risks of a measurement strategy that differs from the “gold standard” of network distance from individually measured, point-based origins and destinations. We find limited support for previous hypotheses regarding measurement error based on the abundance or clustering of urban services or amenities, though further research is merited. Importantly, these new data sources reveal vast differences across cities, underscoring how accurate proximity measurement necessitates a critical understanding of the nuances of the urban landscape under investigation as measures appear heavily influenced by a city’s street layouts and historical development trajectories.

scholarly journals Affective economy of national-populist images: Investigating national and transnational online networks through visual big data

2019 ◽  
Vol 22 (5) ◽  
pp. 770-792
Author(s):  
Jenni Hokka ◽  
Matti Nelimarkka
Keyword(s):  
Social Media ◽  
Big Data ◽  
Quantitative Methods ◽  
Social Science Research ◽  
Historical Analysis ◽  
Science Research ◽  
Online Networks ◽  
Qualitative And Quantitative Methods ◽  
Computational Data

In our article, we investigate the affective economy of national-populist image circulation on Facebook. This is highly relevant, since social media has been an essential area for the spread of national-populist ideology. In our research, we analyse image circulation as affective practice, combining qualitative and quantitative methods. We use computational data analysis methods to examine visual big data: image fingerprints and reverse image search engines to track down the routes of thousands of circulated images as well as make discourse-historical analysis on the images that have gained most attention among supporters. Our research demonstrates that these existing tools allow social science research to make theory-solid approaches to understand the role of image circulation in creating and sustaining national and transnational networks on social media, and show how national-populist thinking is spread through images that catalyse and mobilise affects – fear, anger and resentment – thus creating an effective affective economy.

Trade-Offs, Limitations, and Promises of Big Data in Social Science Research

2014 ◽  
Vol 31 (4) ◽  
pp. 331-338 ◽  
Author(s):  
Patricia White ◽  
R. Saylor Breckenridge
Keyword(s):  
Big Data ◽  
Social Science ◽  
Social Science Research ◽  
Science Research ◽  
Trade Offs

scholarly journals Empowering Social Science Research in the Big-Data Era: Addressing Quantyphobia in IR Researches

2019 ◽  
Vol 2 (2) ◽  
pp. 259
Author(s):  
Farizal Mohd Razalli
Keyword(s):  
Big Data ◽  
Quantitative Methods ◽  
International Politics ◽  
Research Output ◽  
Social Science Research ◽  
Science Research ◽  
Global Changes ◽  
Intellectual Community ◽  
Mechanical Field ◽  
Research Outcome

This paper tries to explore the employment of quantitative approach in political researches focusing on international relations (IR) or international politics. A debate emerged in the90s on whether IR or the field of international politics should be driven by quantitative(positivistic) approach at the expense of qualitative (interpretivist) approach. The debate then expanded to explicitly argue for an increased use of formal methods that are mathematically-based to study IR phenomena. It triggered then a quick reaction fromhardcore IR specialists who warned against mathematizing IR for fear of turning the field into a mechanical field that crunches numbers. Such a fear is further substantiated by theobservation that many quantitative works in IR have moved farther away from developing theory to testing hypotheses. Some scholars have even suggested that it is epistemologicallyrealism vs. instrumentalism; something that is unsurprising given the dominance of realism inIR for many years. This paper does not suggest that heavy emphasis on qualitative approach leads to a inferior research output. However, it does suggest an transformative incapability among IR scholars to accommodate to contemporary global changes. The big-data analyticshave affected the intellectual community of late with the influx of data. These data are bothqualitative and quantitative. Nonetheless, analyzing them requires one to be familiar with quantitative methods lest one risks not being able to offer a research outcome that is not only sound in its argumentation but also robust in its analytical logic. Furthermore, with so much data on the social media, it is almost unthinkable for meaningful interpretation tobe made without even the simplest descriptive statistical methods. The key findings revealthat in ensuring its relevance, international political researches have to start adapting to the contemporary changes by building new capability apart from upscaling existing capacity.

scholarly journals Big data method and its application in innovation education research

2021 ◽  
Vol 290 ◽  
pp. 03022
Author(s):  
Yankui Song ◽  
Chuijiao Jie ◽  
Zhijin Xu
Keyword(s):  
Big Data ◽  
Social Science ◽  
Timing Analysis ◽  
Social Science Research ◽  
Science Research ◽  
Development Trend ◽  
Scientific Basis ◽  
Source Analysis ◽  
Knowledge Mapping ◽  
Big Data Technology

Big data technology is a new stage of information development. In recent years, it has been widely used in many fields, especially in social science research. This paper analyzes the development status and significance of the combination of big data technology and social science research, on the basis of summarizing and combing the concept of big data and its important role. Taking the application of big data method in the research of innovation education as an example, this paper makes a series of visualization analysis with Citespace software on the related literature with the theme of “big data and innovation education” collected by CNKI, such as annual analysis, literature source analysis, co-occurrence analysis of authors, organization analysis, keyword clustering analysis and keyword timing analysis. This paper also draws the corresponding knowledge mapping, clarifies its research status, hot spots and development trend, and provides scientific basis for the research of innovation education. Thus the paper believes that the research on big data and innovation education needs to strengthen interdisciplinary communication and cooperation, refine and deepen the research theme and content.

scholarly journals Social Science Perspective on Artificial Intelligence

2020 ◽  
Vol 2 (3) ◽  
Author(s):  
Simon Lindgren ◽  
Jonny Holmström
Keyword(s):  
Artificial Intelligence ◽  
Social Science ◽  
Social Science Research ◽  
Science Research ◽  
Building Blocks ◽  
Social Actors ◽  
Socially Constructed ◽  
Societal Context ◽  
The Rich ◽  
Human Actors

In this article, we discuss and outline a research agenda for social science research on artificial intelligence. We present four overlapping building blocks that we see as keys for developing a perspective on AI able to unpack the rich complexities of sociotechnical settings. First, the interaction between humans and machines must be studied in its broader societal context. Second, technological and human actors must be seen as social actors on equal terms. Third, we must consider the broader discursive settings in which AI is socially constructed as a phenomenon with related hopes and fears. Fourth, we argue that constant and critical reflection is needed over how AI, algorithms and datafication affect social science research objects and methods. This article serves as the introduction to this JDSR special issue about social science perspectives on AI.

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