scholarly journals Chinese Computing and Computing China as Global Knowledge Production [Special Section]

2020 ◽  
Vol 6 (2) ◽  
Author(s):  
Angela Xiao Wu
Keyword(s):  
Big Data ◽  
Knowledge Production ◽  
Business Models ◽  
Social Research ◽  
Special Section ◽  
Data Extraction ◽  
Big Data Analytics ◽  
Global Scale ◽  
Global Knowledge ◽  
Critical Commentary

As data-driven technologies and business models pervade on a global scale, China’s enormous digital economy often signals its dominating power by dint of data extraction. Complicating this view, this critical commentary focuses on knowledge production, an important dimension for examining the ways in which postsocialist China transpires in global political economy in the age of Big Data analytics. First, I show how Chinese commercial surveillance analytics profits from legitimation lent by the West-centric hierarchical academe. Then, I move to transnational academic repurposing of Big Data from China, which becomes increasingly common. Such social research tends to yield specters of China that are untethered to the lived realities of those whose data are taken. Drawing on decolonial thinking and feminist care ethics, this commentary concludes by urging social scientists to “stay with the trouble,” making China “legible” in their computing of Chinese Big Data.

Big Data and Sustainable Consumption: A Review and Research Agenda

2021 ◽  
pp. 097226292110225
Author(s):  
Shobhana Chandra ◽  
Sanjeev Verma
Keyword(s):  
Big Data ◽  
Consumer Behaviour ◽  
Business Models ◽  
Sustainable Consumption ◽  
Big Data Analytics ◽  
Consumption Behaviour ◽  
Consumer Experiences ◽  
Systematic Understanding ◽  
The Impact

Big data (BD) is making advances in promoting sustainable consumption behaviour and has attracted the attention of researchers worldwide. Despite the increased focus, the findings of studies on this topic are fragmented, and future researchers need a systematic understanding of the existing literature for identification of the research scope. This study offers a systematic review of the role of BD in promoting sustainable-consumption behaviour with the help of a bibliometric analysis, followed by a thematic analysis. The findings suggest that businesses deploy BD to create sustainable consumer experiences, predict consumer buying patterns, design and alter business models and create nudges for sustainable consumption, while consumers are forcing businesses to develop green operations and supply chains to reduce the latter’s carbon footprint. The major research gaps for future researchers are in the following areas: the impact of big data analytics (BDA) on consumerism, the role of BD in the formation of sustainable habits and consumer knowledge creation for sustainable consumption and prediction of green consumer behaviour.

scholarly journals Guest Editorial: Special Section on Advances in Big Data Analytics for Management

2018 ◽  
Vol 15 (1) ◽  
pp. 10-12 ◽  
Author(s):  
Giuliano Casale ◽  
Yixin Diao ◽  
Marco Mellia ◽  
Rajiv Ranjan ◽  
Nur Zincir-Heywood
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Guest Editorial ◽  
Special Section ◽  
Big Data Analytics

scholarly journals Guest Editorial Special Section on Big Data Analytics in Intelligent Manufacturing

2019 ◽  
Vol 15 (4) ◽  
pp. 2382-2385 ◽  
Author(s):  
Kunpeng Zhu ◽  
Sanjay Joshi ◽  
Qing-Guo Wang ◽  
Jerry Fuh Ying Hsi
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Guest Editorial ◽  
Special Section ◽  
Big Data Analytics ◽  
Intelligent Manufacturing

scholarly journals I-Os in the Vanguard of Big Data Analytics and Privacy

2015 ◽  
Vol 8 (4) ◽  
pp. 555-563 ◽  
Author(s):  
Adam J. Ducey ◽  
Nigel Guenole ◽  
Sara P. Weiner ◽  
Hailey A. Herleman ◽  
Robert E. Gibby ◽  
...  
Keyword(s):  
Big Data ◽  
Informed Consent ◽  
Data Analytics ◽  
Data Privacy ◽  
Big Data Analytics ◽  
Global Scale ◽  
Data Sources ◽  
Critical Elements ◽  
The World

In this response to Guzzo, Fink, King, Tonidandel, and Landis (2015), we suggest industrial–organizational (I-O) psychologists join business analysts, data scientists, statisticians, mathematicians, and economists in creating the vanguard of expertise as we acclimate to the reality of analytics in the world of big data. We enthusiastically accept their invitation to share our perspective that extends the discussion in three key areas of the focal article—that is, big data sources, logistic and analytic challenges, and data privacy and informed consent on a global scale. In the subsequent sections, we share our thoughts on these critical elements for advancing I-O psychology's role in leveraging and adding value from big data.

Introduction to special section: Insights to digital oilfield data using artificial intelligence and big data analytics

2019 ◽  
Vol 7 (3) ◽  
pp. SFi-SFi
Author(s):  
Vikram Jayaram ◽  
Atish Roy ◽  
Bill Barna ◽  
Deepak Devegowda ◽  
Jacqueline Floyd ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Data Analytics ◽  
Special Section ◽  
Big Data Analytics ◽  
Digital Oilfield

Big data analytics for investigating Taiwan Line sticker social media marketing

2019 ◽  
Vol 32 (2) ◽  
pp. 589-606 ◽  
Author(s):  
Shu-Hsien Liao ◽  
Szu-Yu Hsu
Keyword(s):  
Social Media ◽  
Big Data ◽  
Business Model ◽  
Data Analytics ◽  
Business Models ◽  
Model Development ◽  
Big Data Analytics ◽  
Social Media Marketing ◽  
Content Type ◽  
Purchase Behaviors

Purpose Line sticker, a social media, it allows users to exchange multimedia files and engage in one-to-one and one-to-many communication with text, pictures, animation and sound. The purpose of this paper is to examine various Taiwan user experiences in the Line sticker use behaviors. Further, this research looks at how the situations of Line sticker proprietors and their affiliates are disseminated for formulating social media marketing (SMM) in its business model concerns. Design/methodology/approach This study examines the experience of various Taiwanese Line stickers users utilizing a market survey, a total of 1,164 valid questionnaire data, and the questionnaire is divided into five sections with 30 items in terms of the database design. All questions use nominal and order scales. This study develops a big data analytics approach, including cluster analysis and association rules, based on a big data structure and a relational database. Findings The authors divide Taiwan Line sticker users into three clusters by their profiles and then find each group’s social media utilization and online purchase behaviors for investigating the Line sticker SMM and business models. Originality/value This is the first study to offer a big data analytics to investigate and analyze the varieties in the use of Line sticker by exploring users’ behaviors for further SMM and business model development.

Positioning big data analytics capabilities towards financial service agility

2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Abeeku Sam Edu
Keyword(s):  
Big Data ◽  
Financial Services ◽  
Data Analytics ◽  
Business Models ◽  
Empirical Test ◽  
Big Data Analytics ◽  
Predictive Modelling ◽  
Financial Service ◽  
Content Type ◽  
It Capability

PurposeEnterprises are increasingly taking actionable steps to transform existing business models through digital technologies for service transformation such as big data analytics (BDA). BDA capabilities offer financial institutions to source financial data, analyse data, insight and store such data and information on collaborative platforms for a quick decision-making process. Accordingly, this study identifies how BDA capabilities can be deployed to provide significant improvement for financial services agility.Design/methodology/approachThe study relied on survey data from 485 banking professionals' perspectives with BDA usage, IT capability development and financial service agility. The PLS-SEM technique was used to evaluate the underlying relationship and the applicability of the research framework proposed.FindingsBased on the empirical test from this study, distinctive BDA usage grounded on the concept of IT capability viewpoint proof that financial service agility could be enhanced provided enterprises develop technical capabilities alongside other relevant resources.Practical implicationsThe study further highlights the need for financial service managers to identify BDA technologies such as data mining, query and reporting, data visualisation, predictive modelling, streaming analytics, video analytics and voice analytics to focus on financial knowledge gathering and market observation. Financial managers can also deploy BDA tools to develop a strategic road map for data management, data transferability and knowledge discovery for customised financial products.Originality/valueThis study is a useful contribution to the burgeoning discussion with emerging technologies such as BDA implication to improving enterprises operations.

Decoding Big Data Analytics for Emerging Business Through Data-Intensive Applications and Business Intelligence

2020 ◽  
pp. 66-80
Author(s):  
Vinay Kellengere Shankarnarayan
Keyword(s):  
Big Data ◽  
Business Intelligence ◽  
Business Models ◽  
Big Data Analytics ◽  
Research Area ◽  
Future Perspective ◽  
Massive Data ◽  
Data Intensive ◽  
Tools And Techniques ◽  
Data Intensive Applications

In recent years, big data have gained massive popularity among researchers, decision analysts, and data architects in any enterprise. Big data had been just another way of saying analytics. In today's world, the company's capital lies with big data. Think of worlds huge companies. The value they offer comes from their data, which they analyze for their proactive benefits. This chapter showcases the insight of big data and its tools and techniques the companies have adopted to deal with data problems. The authors also focus on framework and methodologies to handle the massive data in order to make more accurate and precise decisions. The chapter begins with the current organizational scenario and what is meant by big data. Next, it draws out various challenges faced by organizations. The authors also observe big data business models and different frameworks available and how it has been categorized and finally the conclusion discusses the challenges and what is the future perspective of this research area.

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