Welcome to ICEB'21
  News
Conference photos
Presentation Certificates
Proceedings book Table of Contents
Home
ICEB Home
Journal of Business & Management
Prior conferences
Prior proceedings books
Ei Compendex accession numbers of ICEB papers
ICEB Publication Policy
ICEB member lists
Contact us
About ICEB'21
Welcome to ICEB'21
Hohai Business School
Conference venue
Important dates
Committees
Editorial board
Keynote speakers
Sponsoring journals
Program schedule
Annual conference events
Time Zone Map
ICEB2021 session overview
Program book
Best paper awards
Enter ZOOM meeting
2021 Informal proceedings zip file (Expired; please visit the formal version )
Registration
Registration guidelines
Registration forms
Payment gateway in UK
For physical conference in Nanjing, China
Initial submission
Call for papers
Conference tracks
Initial submission guidelines
Initial paper submission
Final submission
Final submission guidelines
Citation reference styles
Camera-ready template
Final paper submission
Travel information
Visa information
China Covid-19 quarantine rule
Local attractions
Airport transportation
Optional tours
Hotel room booking
ICEB Home


Keynote Speakers:

Patrick Y.K. Chau
Vice Provost for Research & Li Dak Sum Chair Professor in Information Systems and Operations Management
Nottingham University Business School China
Editor-in-Chief, Information & Management

Keynote I: "Computational Social Science in the Big Data Paradigm: A Revisit"

ABSTRACT: Two years ago, I gave a talk on “Computational Social Science in the Big Data Paradigm” in ICEB 2019. Two years later, things around Computational Social Science (CSS) have progressed quite a lot. In this talk, I would like to have a revisit of the research discipline by, once again, giving a tail on its past, its present and its future. To recap, CSS is a research discipline proposed by a group of researchers led by David Lazer from Harvard in a paper published in Nature in 2009. It is a multi-disciplinary field at the intersection of social, computational and complexity sciences whose subjects of study is human interactions and society itself. With the exponential growth of big data in recent years, CCS has also been expanded to the “Big Data paradigm” to attempt to solve issues and/or problems in not just the business arena but also the society at large. This talk will give an overview of the past, the present and the future of CSS in the big data paradigm.

Christy M.K. Cheung
Professor, Department of Finance and Decision Sciences
Director, Research Postgraduate Programme
Hong Kong Baptist University
Editor-in-Chief, Internet Research

Keynote II: "Inside Out and Outside In: How the COVID-19 Pandemic Affects Self-Disclosure on Social Media "

ABSTRACT: As social distancing and lockdown orders grew more pervasive, individuals increasingly turned to social media for support, entertainment, and connection to others. In this study, we posit that global health emergencies - specifically, the COVID-19 pandemic - change how and what individuals self-disclose on social media. We argue that IS research needs to consider how privacy (selffocused) and social (other-focused) calculus have moved some issues outside in (caused by a shift in what is considered socially appropriate) and others inside out (caused by a shift in what information should be shared for the public good). We identify a series of directions for future research that hold potential for furthering our understanding of online self-disclosure and its factors during health emergencies.

J. Christopher Westland
Professor, Department of Information and Decision Sciences
University of Illinois, Chicago, USA
Editor-in-Chief, Electronic Commerce Research

Keynote III: "Determinants of Liquidity in Cryptocurrency Markets"

ABSTRACT: This research identified predictors of cryptocurrency liquidity and explored whether cryptocurrency is a true cash equivalent. Liquidity is important because cryptocurrencies aim to be cash substitutes, and thus totally liquid. Greater liquidity is correlated with more profitable trading, better price discovery; and more profitable market operation. The research tested five hypotheses concerning liquidity and its predictors, for a set of cryptocurrencies that represent about 90% of volume and market capitalization, thus are generalizable. Price was strongly supported as a predictor of liquidity, while volume was not. Fungibility, in the sense of ‘mutual interchangeability of particular pairs of cryptocurrencies, was not found to be a good predictor of liquidity, leading us to question whether cryptocurrencies can truly be considered ’cash equivalents.’ I also tested whether price and volume embedded in own-price elasticity were predictors of liquidity, and rejected these hypotheses. Finally, an analysis involving step-wise regression unambiguously selected a combination (3) daily volume, and (4) own-price elasticity. Explanatory power was slightly better than other predictors, but lacking a structural model incorporating these predictors, the results here are suggestive of future research studies, and potential blockchain; electronic markets; liquidity.

page top | Copyright 2021