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Keynote Speakers:
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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.
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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
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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.
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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.
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