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Call for Papers for a Special Issue of the Journal of Information
Technology on:
"Next-Generation Information Systems Research Methods"
Special Issue Editors:
Ivo Blohm, University of St. Gallen - Switzerland (corresponding
Special Issue Editor:
ivo.blohm@unisg.ch<mailto:ivo.blohm@unisg.ch>)
Susanna Ho, Australian National University - Australia
Shaila Miranda, University of Oklahoma - USA
Jan Marco Leimeister, University of St. Gallen - Switzerland
This special issue is interested in "next-generation" research
methods for studying information technology (IT) phenomena -
particularly in the information system (IS) research field. So
far, IS researchers have applied a range of quantitative,
qualitative and engaged methods to study IT-related phenomena.
Quantitative IS research has often followed a positivist approach
of hypothesis testing, with sometimes "first-generation"
regression models distinguished from "second-generation"
structural equation models (Gerow et al. 2011). Data used in such
methods stems from surveys, experiments, panel studies, etc. The
primary objective of this research stream typically is theory
testing. Qualitative IS research has ranged from positivist to
interpretivist, seeking rich situated insights via case studies,
grounded theory or ethnographies. One objective of this research
stream is theory construction. Critical and emancipatory IS
research adds a strong value orientation and theoretical basis to
qualitative research. Another type of IS research seeks to
generate new knowledge with "engaged" methods like design science
or action research. The objective of this stream that
differentiates it from the first two is the focus on solving
important individual, organizational or societal challenges; or
extending the boundaries of human and organizational capabilities
by creating new and innovative artifacts (Baskerville and
Wood-Harper 1996; Hevner et al. 2004).
In recent years, the scope of and possibilities for IS research
have tremendously widened. Digital IT have become mainstream at
the individual, organizational and societal levels (Burton-Jones
et al. 2021). Digital IT is key to innovation in various domains
from medicine to education, from psychology to the arts. Hence,
these domains have become of interest and relevance to IS
researchers, yet also overlap with other fields and are hence
interdisciplinary in nature. These innovations are increasing the
volume and variety of trace data available to researchers but
necessitate a shift in our research practices (Johnson et al.
2019). Further, big data, machine learning (ML) and artificial
intelligence (AI) technologies provide a plethora of powerful
approaches for collecting and analysing data at a scale not
possible before such that we might need to adapt our research
methods and tools to make this data actionable and to generate
novel knowledge. These ML technologies are being applied not only
to theory testing but also to theory construction - in conjunction
with or independent of qualitative methods (Miranda et al. 2022).
This special issue focuses on the next generation of research
methods to study IT - new methods relevant to the IS research
field that account for a) the interdisciplinary nature and wider
scope of IT phenomena, and/or b) the novel capacities afforded by
new technologies/techniques (e.g., ML/AI).
The way that novel technologies and techniques afford new
possibilities is quite apparent in quantitative IS research. In
contrast to "first-generation" regression-based approaches and
"second-generation" structural equation modelling, a new
generation of research methods including the predictive analytics
(Shmueli and Koppius 2011), data mining (Smith 2020), ML (Shrestha
et al. 2020) or explainable AI (Gunning et al. 2019) come with
substantial new opportunities and challenges. For instance, these
methods potentially allow for more fine-grained measurements and
analyses to extend our knowledge of existing phenomena and may
help us study novel phenomena including those that were,
methodologically speaking, "out of reach" (George et al. 2016).
These technological advancements enable us to progress our
research toolkit and inform new ways of generating knowledge and
theorizing (Burton-Jones et al. 2021; Shrestha et al. 2020).
Increased recognition of abduction as a counterpart to deductive
and inductive reasoning (Behfar and Okhuysen 2018; Sætre and Van
de Ven 2021) pits concerns about practices such as HARKing (Kerr
1998) - hypothesizing after the results are known (aka p-hacking)
- believed to lead to logically and scientifically flawed
hypotheses, against concerns about stifling the advancement of
knowledge (Pratt et al. 2019). ML/AI techniques are now so
powerful that they can test millions of models on large data sets
to find the "best-matching" model (of all possible) for what needs
to be explained. This is a fundamental gear change in what can be
done with quantitative data sets, the promise and validity of
which are hotly debated (Kitchin 2014; Smith 2020).
In terms of changes to the nature of the IS domains,
qualitative-interpretative researchers seek to find new methods
suitable for the wider, interdisciplinary scope of IS research.
This is done via finding or developing analytical new grounds and
looking at digital IT differently (e.g. via sociomaterial or
affordances perspectives), seeking additional foundations in other
fields (e.g., Zuboff's (2015) political economy informed analysis
of surveillance capitalism and "Big Tech") or via the modification
of methods to suit digital environments (e.g., virtual
ethnographies or computational grounded theory). Other forms of
qualitative research such as meta-synthesis, qualitative
comparative analysis and discourse analysis also seem promising
and relevant but are seldomly used in IS research. Digital IT
(e.g., social media, digital platforms, AI/ML) has increasing and
substantial impacts on society (e.g., mental health, trust in
science, misinformation and polarization, monopolization and
industry disruption). Hence, ethical, critical, value-based and
political-economy questions are increasingly and necessarily asked
about digital IT. Similarly, today's pace of innovation may also
warrant novel approaches to engaged scholarship and design
science. For instance, researchers are calling for methods that
can accumulate and update evidence more effectively (Lacity et al.
2021) and better ensure the generalizability of prescriptive
knowledge (Brendel et al. 2021; Iivari et al. 2021).
This special issue invites contributions that propose, introduce,
debate or critique novel quantitative, qualitative or
design-oriented methods research, with a focus on the "how" (i.e.,
origins, processes, steps of the method) and the "why" (e.g.,
"rigour vs relevance" and "it is new, but is it better" questions)
these approaches should be used in IS research. The goal of the
special issue is to provide a space for introducing and discussing
innovative "next-generation" methods for studying IT-related
phenomena, relative to the IS research field.
Papers may focus on, but are not restricted to, the following
themes:
* Critical reflection of research methods used in IS.
* Introducing new qualitative, quantitative or design research
methods.
* Transferability of non-IS research methods such as from other
fields or praxis.
* Novel combinations of research methods such as
cross-disciplinary or mixed-method approaches.
* Innovative approaches to analyse big data.
* Approaches dealing with special types of unstructured data.
* Problems of null hypothesis testing via big data and AI/ML.
* AI, ML and explainable AI for knowledge creation and theory
generation.
* Transparent, robust and replicable research designs and
methodologies.
* Transferability and generalizability of insights generated with
specific methods.
* Approaches to evaluating and communicating practical relevance
of findings.
* Critical assessment of novel approaches on practical,
conceptual, ethical or philosophical grounds.
* Philosophy of science-based proposals for or critiques of
methods.
* Culture and values-driven proposals for or critiques of methods.
* Methods to study the past, the present and/or the future.
Submission Guide:
* Journal of Information Technology special issue papers will go
through no more than two full rounds of peer review.
* Submissions to the Journal of Information Technology special
issue should follow the regular rules for research paper
submissions, selecting the special issue as the submission type
and its corresponding special issue editor as suggested Senior
Editor to handle the submission.
* Submission system:
https://mc.manuscriptcentral.com/jin
* Submission system FAQ:
https://clarivate.com/webofsciencegroup/support/scholarone-manuscripts/faqs-help
* JIT homepage:
https://journals.sagepub.com/home/jin
* JIT submission guidelines:
https://journals.sagepub.com/author-instructions/JIN
Submission Timetable:
* Abstract submissions: November 20, 2022. (optional; authors are
invited to submit extended abstracts of papers for early
reactions)
* Special issue workshop at ICIS 2022. (online participation
possible; based on submitted abstracts, authors have the
opportunity to present and discuss their paper ideas. For more
information see:
https://anu.au1.qualtrics.com/jfe/form/SV_0NZsFU8VQhuYj6m)
* First-round submissions: April 15, 2023.
* First-round decisions: July 15, 2023.
* Second-round submissions: December 15, 2023.
* Second-round decisions: February 15, 2024. (papers are either
acceptable with minor changes or rejected at this stage)
* Final versions due: May 15, 2024. (final decisions and online
publication soon afterwards)
References:
Baskerville, R. L., and Wood-Harper, A. T. 1996. "A Critical
Perspective on Action Research as a Method for Information Systems
Research", Journal of Information Technology (11:3), pp. 235-246.
Behfar, K., and Okhuysen, G. A. 2018. "Perspective-Discovery
within Validation Logic: Deliberately Surfacing, Complementing,
and Substituting Abductive Reasoning in Hypothetico-Deductive
Inquiry", Organization Science (29:2), pp. 323-340.
Brendel, A. B., Lembcke, T.-B., Muntermann, J., and Kolbe, L. M.
2021. "Toward Replication Study Types for Design Science
Research", Journal of Information Technology (36:3), pp. 198-215.
Burton-Jones, A., Butler, B. S., Scott, S. V., and (Xin), S. 2021.
"Next-Generation Information Systems Theorizing: A Call to
Action", MIS Quarterly (45:1), pp. 301-314.
George, G., Osinga, E. C., Lavie, D., and Scott, B. A. 2016. "Big
Data and Data Science Methods for Management Research", Academy of
Management Journal (59:5), pp. 1493-1507.
Gerow, J. E., Grover, V., Roberts, N., and Thatcher, J. B. 2011.
"The Diffusion of Second-Generation Statistical Techniques in
Information Systems Research from 1990-2008", Journal of
Information Technology Theory and Application (11:4), p. 2.
Gunning, D., Stefik, M., Choi, J., Miller, T., Stumpf, S., and
Yang, G.-Z. 2019. "Explainable Artificial Intelligence", Science
Robotics (4:37), eaay7120.
Hevner, A., March, S. T., Park, J., and Ram, S. 2004. "Design
Science Research in Information Systems", MIS quarterly (28:1),
pp. 75-105.
Iivari, J., Rotvit Perlt Hansen, M., and Haj-Bolouri, A. 2021. "A
Proposal for Minimum Reusability Evaluation of Design Principles",
European Journal of Information Systems (30:3), pp. 286-303.
Johnson, S. L., Gray, P., and Sarker, S. 2019. "Revisiting Is
Research Practice in the Era of Big Data", Information and
organization (29:1), pp. 41-56.
Kerr, N. L. 1998. "Harking: Hypothesizing after the Results Are
Known", Personality and Social Psychology Review (2:3), pp.
196-217.
Kitchin, R. 2014. "Big Data, New Epistemologies and Paradigm
Shifts", Big Data & Society (1:1), p. 2053951714528481.
Lacity, M., Willcocks, L., and Gozman, D. 2021. "Influencing
Information Systems Practice: The Action Principles Approach
Applied to Robotic Process and Cognitive Automation", Journal of
Information Technology (36:3), pp. 216-240.
Miranda, S., Berente, N., Seidel, S., Safadi, H., and
Burton-Jones, A. 2022. "Editor's Comments: Computationally
Intensive Theory Construction: A Primer for Authors and
Reviewers", MIS Quarterly (46), pp. iii-xviii.
Pratt, M. G., Kaplan, S., and Whittington, R. 2019. "Editorial
Essay: The Tumult over Transparency: Decoupling Transparency from
Replication in Establishing Trustworthy Qualitative Research",
Administrative Science Quarterly (65:1), pp. 1-19.
Sætre, A. S., and Van de Ven, A. 2021. "Generating Theory by
Abduction", Academy of Management Review (46:4), pp. 684-701.
Shmueli, G., and Koppius, O. R. 2011. "Predictive Analytics in
Information Systems Research", MIS Quarterly (35:3), pp. 553-572.
Shrestha, Y. R., He, V. F., Puranam, P., and von Krogh, G. 2020.
"Algorithm Supported Induction for Building Theory: How Can We Use
Prediction Models to Theorize?", Organization Science (32:3), pp.
856-880.
Smith, G. 2020. "Data Mining Fool's Gold", Journal of Information
Technology (35:3), pp. 182-194.
Zuboff, S. 2015. "Big Other: Surveillance Capitalism and the
Prospects of an Information Civilization", Journal of Information
Technology (30:1), pp. 75-89.
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