The Role of Big Data in Social Science: A Case Study Using Hadoop

2021 ◽  
pp. 243-251
Author(s):  
Nour Alqudah ◽  
Mohammed Q. Shatnawi
Keyword(s):  
Big Data ◽  
Social Science

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 The Role of Strategic Management on Exporting Performance: A Case Study of Iranian Firms

2016 ◽  
Vol 7 (1) ◽  
pp. 174
Author(s):  
Homa Doroudi ◽  
Maryam Gharakhanlou
Keyword(s):  
Social Science ◽  
Significant Relationship ◽  
Export Performance ◽  
Methodological Approach ◽  
Strategy Implementation ◽  
Strategy Management ◽  
Main Study ◽  
Exporting Firms

The current study investigates the strategy implementation of Iranian export performance, in Februy, 2015. It was conducted among 150 managers of Iranian exporting firms in order to find any significant relationship between strategy management and success in exporting performance. The methodological approach adopted for this study is Structural Equational Model (SEM) approach. It was processed in two sections (Pilot and Main study).The salient instrument of the study was questionnaire. The data were coded in SPSS (Statistical Package for Social Science, Version 22) and Lisrel (Version 8.8) Software. Three types of analyses were conducted to identify any significant relationship between them. The results revealed that there are significant relationship in most of strategies and exporting performance.

The Role Of Big Data In The Formation Of Supply Chain Platforms For New Forms Of Online Retail

2022 ◽  
Vol 35 (1) ◽  
pp. 0-0
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Theoretical Frameworks ◽  
Online Retail ◽  
Positive Effects ◽  
Study Materials ◽  
Internet Age ◽  
Open Coding

In the context of internet age, data is growing explosively in the Chinese retail industry. However, there is insufficient research of the theoretical frameworks and interaction relationships between big data, supply chain platforms, and online retail. Through a literature review, an empirical case study survey of Alibaba, and grounded theory, this paper explores how big data helps shape supply chain platforms able to support new forms of online retail. The theoretical framework was validated by testing the reliability and the open coding to process of the case study materials. The results identify the overall antecedents to the formation of the supply chain platform and reveal significant positive effects between big data and new retail. The findings help firms build big-data driven supply chain platforms better able to support new forms of online retail.

The dual drivetrain model of digital transformation: role of industrial big-data-based affordance

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yi Liu ◽  
Wei Wang ◽  
Zuopeng (Justin) Zhang
Keyword(s):  
Big Data ◽  
Digital Transformation ◽  
Data Driven ◽  
Content Type ◽  
Industrial Big Data ◽  
Technology Affordance ◽  
Effective Use ◽  
Practical Implications

PurposeTo better understand the role of industrial big data in promoting digital transformation, the authors propose a theoretical framework of industrial big-data-based affordance in the form of an illustrative metaphor – what the authors call the “organizational drivetrain.”Design/methodology/approachThis study investigates the effective use of industrial big data in the process of digital transformation based on the technology affordance–actualization theoretical lens. A software platform and services provider with more than 4,000 industrial enterprise clients in China was selected as the case study object for analyzing the digital affordance and actualization driven by industrial big data.FindingsDrawing on a revelatory case study, the authors identify three affordances of industrial big data in the organization, namely developing data-driven customized projects, provisioning equipment-data-driven life cycle services, establishing data-based trust and determining affordance actualization actions driven by technology and market. In addition, the authors reveal the underlying drivetrain mechanisms to advance industrial big data affordance and actualization: stabilizing, enriching and pioneering.Originality/valueThis study builds a drivetrain model on digital transformation by industrial big data affordance actualization. The authors also provide practical implications that can help practitioners to implement digital transformation effectively and extract value from their investment.

A Multi-Scale Study of US Drought Awareness

2020 ◽  
Author(s):  
Jonghun Kam
Keyword(s):  
Big Data ◽  
Public Awareness ◽  
National Level ◽  
Spatiotemporal Analysis ◽  
Economic Losses ◽  
Social Response ◽  
Us States ◽  
Multi Scale

<p>Big data have meaningful, but hidden, information about our society's behavior and response to influential events. Particularly, water-related disasters, such as drought and flood, cause rapid increase in public awareness/interest when they already happen. Despite the improved prediction skill, lack of timely social response to these disasters exacerbates economic losses and fatalities. </p><p>In this presentation, I will introduce the utility of Google Trends data in monitoring and understanding the dynamic patterns of social response to drought at the state and national level. The first part of this presentation will show a case study of the dynamics of Californian awareness during the 2011–17 California Drought. The second part of this seminar will show a spatiotemporal analysis of US national drought awareness among the 49 US states. In closing, I will discuss the role of big data in transforming our society to a water-related disaster-ready environment.</p>

The Use of Case Studies in Law and Social Science Research

2018 ◽  
Vol 14 (1) ◽  
pp. 381-396 ◽  
Author(s):  
Lisa L. Miller
Keyword(s):  
Social Science ◽  
Case Studies ◽  
Case Study Research ◽  
Social Science Research ◽  
Science Research ◽  
Scientific Work ◽  
Starting Point ◽  
Social Scientific

This article reviews classic and contemporary case study research in law and social science. Taking as its starting point that legal scholars engaged in case studies generally have a set of questions distinct from those using other research approaches, the essay offers a detailed discussion of three primary contributions of case studies in legal scholarship: theory building, concept formation, and processes/mechanisms. The essay describes the role of case studies in social scientific work and their express value to legal scholars, and offers specific descriptions from classic and contemporary works.

scholarly journals Documenting Sea Change

2020 ◽  
Vol 11 (1) ◽  
pp. 82-99
Author(s):  
Kathleen M. Sullivan
Keyword(s):  
Big Data ◽  
Social Science ◽  
Significant Role ◽  
Data Infrastructure ◽  
The Third ◽  
Ocean Governance ◽  
The Arts ◽  
Ocean Sciences ◽  
The Relationship

This review examines social science and practitioner literature regarding the relationship between ocean sciences big data projects and ocean governance. I contend that three overarching approaches to the study of the development of ocean sciences big data techne (the arts of data creation, management, and sharing) and data technologies can be discerned. The first approach traces histories of ocean sciences data technologies, highlighting the significant role of governments in their development. The second approach is comprised of an oceanic contribution to the study of ontological politics. The third takes a human-social centered approach, examining the networks of people and practices responsible for creating and maintaining ocean sciences big data infrastructure. The three approaches make possible a comparative reflection on the entangled ethical strands at work in the literature.

Transformation in Data Storing Technique- Big Data: A Literature Review

2021 ◽  
Vol 9 (9) ◽  
pp. 752-762
Author(s):  
Adarsh Neema
Keyword(s):  
Big Data ◽  
High Speed ◽  
Data Gathering ◽  
Big Data Analytics ◽  
Structured Data ◽  
Unstructured Data ◽  
Customer Expectation ◽  
Internet Users

Abstract: Loss of data implies loss of valuable information. An appropriate gathering of data and finding hidden patterns out of it is key for any business organization to thrive fiscally. With exponential rise in the internet users from the early 2000’s, traditional databases fell short to collect the enormous amount of unstructured data/ semi-structured data, which contained extremely insightful information. Today, data accumulated is not only enormous, but also collected with high speed, having variety, which necessitated special database/software for data gathering and making key decisions based on that. These gigantic amounts of data generated can advocate companies to examine the market trends, market demands, and customer expectation, which endorses them to make relevant foremost decisions. This study discusses the stymie in conventional databases to process the immense data and entailment of advanced databases/software. Furthermore, a case study is presented later to understand the role of big data analytics in business and technical firms. Keywords: Big Data, structured data, unstructured data, NoSQL, Hadoop.

Sign in / Sign up

Export Citation Format

Share Document