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Dear IS colleagues,
This year's popular SIGHPHIL@ICIS workshop tackles what's on every
IS
researcher's mind -- where are my IS theories? It is inconceivable
for a
field so relevant to this social media era of "fake news",
weaponized
information, disruptive technologies, Big Data, and what Soshana
Zuboff
calls "surveillance capitalism," not to be brimming with its own
theories
about all these phenomena. If you haven't watched Zuboff's
interview on the
latest PBS Frontline show "In the Age of AI" you don't want to
miss it! Our
IS researchers are positioned in the best possible intellectual
space to
respond to all these issues.
Is analytics being exported from the US and China to other
countries that
want to control their citizens? What is the future outlook for
jobs as AI
and analytics take over? "We thought we were searching Google. We
have no
idea that Google was searching us" ~ Zuboff. Facebook and other
social
media providers have been hiding the extent of their collection of
private
data. How can we make this more transparent to the average user?
What will
happen when analytics that have so far been successful at
suggesting what
next to buy or what next to watch become also good at deepening
inequalities, challenging democracies, and dividing nations and
the world.
Do we not have our own concepts and theories that can address such
issues?
These are but a few of the questions that remain unanswered and
our line-up
of speakers and panelists that include AIS LEOs Kalle Lyytinen,
Rudy
Hirschheim, Fellows Alan Hevner, Shirley Gregor, Leslie Willcocks,
Alan
Dennis, and other experts on analytics like Ahmed Abassi, Vasant
Dhar,
Foster Provost, Sumit Sarkar and Ramesh Sharda will address. The
goal of
the SIGPHIL workshop is to provide an opportunity to spend quality
time
with thought leaders of the IS community and listen to the
interesting
backstories and "behind the scenes" revelations in an informal and
friendly environment.
Because the workshop is held in the evenings of ICIS, you will
still be
able to attend it if you are attending any pre-ICIS programs and
of course,
you can always attend the workshop even if you are not attending
ICIS
conference, and dinner is included.
If you have already registered for ICIS, go to
https://icis2019.aisconferences.org/register/ to add the workshop
or just
add the workshop when you register for the ICIS conference. You
can also
register just for the workshop.
Don't hesitate to email me directly at
nhassan@d.umn.edu if you
have any
questions.
See you there!
Workshop co-chairs
Nik Rushdi Hassan, Varun Grover, Leslie Willcocks
SIGPHIL@ICIS 2019, Dec 15-16, Munich, Germany
The End of Theory in IS and Analytics: Does Big Data Really Make
the
Scientific Method Obsolete?
In conjunction with the 2019 International Conference of
Information
Systems (ICIS), the AIS Special Interest Group on Philosophy in
Information
Systems (AIS-SIGPHIL) will hold its 8th Concurrent SIGPHIL@ICIS
Research
Workshop during two evenings of the ICIS conference in Munich,
Germany at
the Internationales Congress Center München (ICM). Following last
year’s
SIGPHIL@ICIS, this year’s event continues the call for the edited
series on
“Advancing IS theories” by Nik Hassan and Leslie Willcocks. At the
same
time, the workshop provides an excellent opportunity to spend
quality time
with thought leaders of the IS community in an informal and
friendly
environment.
Workshop Presenters, Organizing Committee and Contributors (in
alphabetical
order)
Ahmed Abbasi, University of Virginia
Alan Dennis, Indiana University
Vasant Dhar, New York University
Varun Grover, University of Arkansas
Nik Rushdi Hassan, University of Minnesota Duluth
Alan Hevner, University of South Florida
Shirley Gregor, Australian National University
Rudy Hirschheim, Louisiana State University
Kalle Lyytinen, Case Western Reserve University
Foster Provost, New York University
Sumit Sarkar, University of Texas Dallas
Ramesh Sharda, Oklahoma State University
Leslie Willcocks, London School of Economics
Workshop Theme
The title of this workshop is paraphrased from the title of an
editorial by *Wired
Magazine’s* chief editor Chris Anderson (2008) who argued that
with big
data, we no longer have to settle for imperfect models, and since
the
scientific method relies on models from which we test hypotheses,
big data
has essential made the scientific method obsolete. Extending this
argument,
because theory is the goal of the scientific method, theory itself
becomes
unnecessary. Why do we need theory when big data can already help
us
predict? Not surprisingly this claim has attracted much attention
from both
industry and academia (Mayer-Schönberger and Cukier, 2013;
Kitchin, 2014).
Like many highly cited pieces, the *Wired* editorial has taken on
a life of
its own, as it is interpreted and reinterpreted by many to support
their
own stance on the topic of theory. Another article from *Wired
Magazine* (Steadman,
2013) featured how big data predicted Osama bin Laden’s location
from
publicly available data without any need for models or theories.
In other
words, “it just needs to work: prediction trumps explanation”
(Siegel,
2016, p. 90). Some of these researchers take big data research as
an
extreme form of empiricism to reignite long-standing debates
surrounding
the legitimacy of the social sciences and eagerly use big data to
claim the
status of the natural sciences for their own works. Big data has
supposedly
shifted the paradigm of research itself which previously could
only take
place as a result of trade-offs among generality, control and
realism (Chang,
Kauffman and Kwon, 2014). “Computational social science” (Lazer et
al.,
2009) using big data is free of those trade-offs. Others are more
cautious.
Mayer-Schönberger and Cukier (2013) consider preposterous the
claim that
generalizable rules are irrelevant. For example, they argue that
the
process of collecting big data itself is based on some kind of
theory.
For a field that is still struggling with theory and the role of
theory in
its research, this debate about the relevance and irrelevance of
theory
vis-a-vis analytics places our researchers in a difficult position
on at
least two levels. First, theory for description, explanation and
prediction
for any area within IS – analytics included – is itself being put
to
question by big data. Second, theory building within and for
analytics as a
subfield of IS too is unclear. At the first level, can practical
questions
that are targeted by analytics for specific instances (Will the
customer
buy? Can the MRI show anomalies?) be generalized? At the second
level, why
do we, IS researchers, need to worry about theory in analytics
when all of
its theoretical foundations were already built by scholars of
statistical
learning theory, computer science and operations research? Only a
few
scholars have addressed theory for analytics directly (Shmueli and
Koppius,
2011; Agarwal and Dhar, 2014; Abbasi, Sarker and Chiang, 2016)
while the
rest of the IS community remain silent. Of the few IS studies that
do
address theory, they mostly introduce the notion of theory
building within
analytics as a consequence of the predictive analytics process
rather than
explanatory theory in general or foundational analytics theories
that cut
across all analytics processes including data collection, data
preparation
and cleaning, exploratory data analysis, model building,
evaluation and
deployment. Anecdotal evidence suggest that the IS community may
have
gravitated towards the notion that theory is indeed irrelevant for
business
analytics. For instance, an editor for a prestigious IS conference
noted:
“we have witnessed explosive growth of the business analytics
field in this
decade, both in research and in practice. So, why is theory
building
mandatory for the growth and legitimacy of the field?” Such as a
state of
affairs is problematic, not the least because the majority of
programs in
business analytics (BA) in the Schools of Business around the
world are led
by IS scholars and researchers.
The discussion on theory in the field has left us unclear about
whether or
not theory does or does not play a major role and whether theory
for
business analytics matter at all. It may be clear to most within
the IS
field that our researchers are not expected to invent the next
Hadoop or
MapReduce, or even to write the next classification or clustering
algorithm. If those technologies are not where our efforts should
be
expended, what exactly is the role of the IS researcher, and by
extension,
the practice of BA that is most relevant to IS? Is the IS
researcher left
with the trite and uninspiring task of researching the adoption or
acceptance of big data analytics? Or can the IS researcher, as
Dhar (2013)
proposes, provide interesting answers to questions that we do not
yet know?
Or even better, as Pentland (2014) claims, we can solve
macro-level
problems using the micro-level big data that are being analyzed
and “build
a society that is better at avoiding market crashes, ethnic and
religious
violence, political stalemates, widespread corruption, and
dangerous
concentrations of power” (p. 17), all of which cannot do without
solid
theoretical foundations.
Edited Book Series: Advancing IS Theories
This struggle for theory is the theme for this year’s SIGPHIL@ICIS
workshop, focusing on theory in business analytics and supporting
the goals
of a planned series of volumes on information systems (IS) titled:
“Advancing Information Systems Theories.” The goal of this series
of volume
is to advance IS research beyond borrowed legitimization and
derivative
research towards fresh and original research that naturally comes
from its
own theories – information system theories. The first volume on
the process
of IS theorizing is in the final stages of review and near
publication. The
second volume concerns efforts that approach theories – what Weick
(1995)
calls “interim struggles.” This volume comes out of the
realization that
the process of theorizing can be long and arduous and like all
great
things, will not be built in a day, much less in an edited volume.
So,
although they may not be called theories with a capital “T,” they
nevertheless approximate theory and should not be dismissed. They
may be
called “principles,” “propositions,” “models,” “paradigms,”
“concepts,”
“frameworks” or what have you. They are the products of theorizing
and are
precursors to strong theory, and as long as they are fresh and
original,
they go a long way in advancing IS theories. A demonstrative list
of
chapters for Vol. II is provided below:
Introduction: The products of IS theorizing (Hassan, Mathiassen
&
Lowry)
1
The prospects of theory for business analytics
20
A review of information theory in information systems (McKinney)
40
Design principles in design science (Gregor and Hevner)
60
IS Concepts: Declaring IS to the world
80
Mapping an IS research framework
100
Models and contexts of discovery in IS
120
IS constructs and variables
140
A collection of IS propositions
160
[Tentative] Program Schedule Sunday, Dec 15, 2019 (Location: TBD)
7:30pm-7:40pm: Introductions by Nik Hassan: The goals for the
workshop and
the notion of products of theorizing
7:40pm – 8:10pm First Plenary Keynote by Varun Grover and Kalle
Lyytinen on
“Role of theory in the environment of big data”
8:10pm-8:40pm Second Keynote by Rudy Hirschheim on “Big Data is
anathema to
theory and understanding” followed by Q&A
10 min Coffee Break
9:00-9:30pm Third Keynote by Alan Hevner and Shirley Gregor (via
Skype) on
“The Scientific Method is Alive and Kicking in Design Science
Research for
Analytics”
9:30pm-10:00pm Panel response to Keynotes: Panelist Sumit Sarkar
and Ramesh
Sharda
Monday, Dec 16, 2019 (Location: TBD)
Dinner 5:30-7:00pm (Venue: TBD)
7:00pm-7:15pm: Brief introduction by Leslie Willcocks: Where are
the IS
Theories in Analytics?
7:15pm-7:50pm Third Keynote, Ahmed Abbasi on “The Pendulum has
Swung: From
Big Data Hubris to AI Hubris” with Discussion and Q&A
7:50-8:30pm Skype Guest Speakers Vasant Dhar and Foster Provost on
“Prospects of Theory with Big Data Analytics”
10 min Coffee Break
8:45pm-9:30pm Workshop wrap-up discussion by Alan Dennis, Rudy
Hirschheim
and Leslie Willcocks
References
Abbasi, A., S. Sarker and R. Chiang. (2016). “Big data research in
information systems: Toward an inclusive research agenda.”
*Journal of the
Association for Information Systems*, *17*(2), i–xxxii.
Agarwal, R. and V. Dhar. (2014). “Big data, data science, and
analytics:
The opportunity and challenge for IS research.” *Information
Systems
Research*, *25*(3), 443–448.
Anderson, C. (2008). “The end of theory.” *Wired*, *16*(7), 71.
Chang, R. M., R. J. Kauffman and Y. Kwon. (2014). “Understanding
the
paradigm shift to computational social science in the presence of
big
data.” *Decision Support Systems*, *63*, 67–80.
Dhar, V. (2013). “Data science and prediction.” *Communications of
the ACM*,
*56*(12), 64–73.
Kitchin, R. (2014). *The Data Revolution: Big Data, Open Data,
Data
Infrastructures & Their Consequences*. Thousand Oaks, CA: SAGE
Publications.
Lazer, D., A. Pentland, L. Adamic, S. Aral, A.-L. Barabási, D.
Brewer, … M.
Van Alstyne. (2009). “Computational social science.” *Science*,
*323*(5915),
721–723.
Mayer-Schönberger, V. and K. Cukier. (2013). *Big Data: A
Revolution That
Will Transform How We Live, Work, and Think*. New York: Houghton
Mifflin
Harcourt.
Pentland, A. (2014). *Social Physics: How Good Ideas Spread -- the
Lessons
from a New Science*. New York: Penguin Press.
Shmueli, G. and O. Koppius. (2011). “Predictive analytics in
information
systems research.” *MIS Quarterly*, *35*(3), 553–572.
Siegel, E. (2016). *Predictive Analytics: The Power to Predict Who
Will
Click, Buy, Lie, or Die*. New York: Wiley.
Steadman, I. (2013). “Big data, language and the death of the
theorist
(Wired UK).” Retrieved from
http://www.wired.co.uk/news/archive/2013-01/25/big-data-end-of-theory
(visited on September 27, 2019)
Weick, K. E. (1995). “What theory is not, theorizing Is.”
*Administrative
Science Quarterly*, *40*(3), 385–390.
--
Nik Rushdi Hassan, PhD and Assoc. Professor of MIS
Head, Dept of Management Studies
Labovitz School of Business and Economics
University of Minnesota Duluth
1318 Kirby Drive, LSBE 385A
Duluth MN 55812
Office Phone: (218) 726-7453
Fax: (218) 726-7578
Home Page: www.d.umn.edu/~nhassan
Email: nhassan@d.umn.edu
LinkedIn: www.linkedin.com/in/nikrushdi/
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