=Paper=
{{Paper
|id=Vol-2786/Paper6
|storemode=property
|title=Theory Building with Big Data-Driven Research – An Editorial Perspective - Abstract
|pdfUrl=https://ceur-ws.org/Vol-2786/Paper6.pdf
|volume=Vol-2786
|authors=Arpan Kumar Kar
|dblpUrl=https://dblp.org/rec/conf/isic2/Kar21
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==Theory Building with Big Data-Driven Research – An Editorial Perspective - Abstract==
https://ceur-ws.org/Vol-2786/Paper6.pdf
44
Theory Building with Big Data-Driven Research – An Editorial
Perspective
Arpan Kumar Kar
Department of Management Studies, IIT Delhi
Abstract: Data availability and easier access to various computational methodologies, are transforming the
Information Systems (IS) discipline. As more and more platforms get integrated with social media and other
platforms, the generation of big data is facilitated (Kar, 2014; Kar, 2015, Grover et al., 2017). This big data may
be generated due to interaction of users with the platform, interaction among users, interaction among
components during workflows in massive enterprise systems, and interaction among multiple organizations like
government, firms, and individuals (Singh et al., 2017; Gupta et al., 2018; Grover and Kar, 2017). This big data
may be analyzed using machine learning and artificial intelligence to generate insights for decision making
(Chakraborty and Kar, 2016, 2017; Kar, 2016).
These studies on data science often use big data which may incorporate structured and unstructured
data, from various digital platforms like social media, emails, IoT devices, mobile applications,
telecommunication devices, and smart applications. The research objectives that these studies address may
attempt to use methods from computational science to derive knowledge encoded into this big data. Interestingly,
the theoretical contributions are sometimes questioned in these studies. There is a need to ground back studies on
data science to information systems discipline so that the findings can enable a better understanding of the
interaction, usage and impacts of individuals, organizations, society, and polity with technological artefacts.
Multiple stakeholders may also be used in such analysis as multiple stakeholders often facilitate collective
knowledge documentation (Kar and Pani, 2014; Grover et al., 2019a; Grover et al., 2019b). We provide direction
in our opinion article to fulfill this grand objective to redefine research directions within data science research.
In this journey towards knowledge creation, first, the researchers need to develop the research
questions. Subsequently, based on the research questions, the data collection strategy and sampling strategy has
to be formulated. This addresses concerns surrounding veracity within big data research. Care should be taken in
such research to demonstrate the reliability and validity of measures. The methodology for theory building, being
big data-driven and therefore inductive, it is important to iteratively revisit the literature for developing a
theoretical model. Such a theoretical model should have ample opportunities for objective model specification.
The inputs to these models would be determined from big data analytics methods which may mine unstructured
and structured data for computing attributes for the model validation. Statistical validation is very important in
these studies beyond outputs derived from data visualization approaches. Researchers should also take ample
care to minimize the trade-off between internal validity and external validity in these big data-driven studies.
1. Short Biography
Prof. Arpan Kar is Associate Professor in Technology Delhi, India. His research interests are
Information Systems, in DMS, Indian Institute of in the domain of data science, digital
______________________________ transformation, internet ecosystems, social media,
ISIC’21:International Semantic Intelligence Conference, February and ICT-based public policy. He has authored over
25–27, 2021, New Delhi, India 150 peer-reviewed articles and edited 7 research
✉ : arpan_kar@yahoo.co.in(Arpan Kumar Kar) ______________________________
Copyright © 2021 for this paper by the authors. Use permitted
For more details on recent works see
under Creative Commons License Attribution 4.0 International (CC BY 4.0).
https://arpankar.com
CEUR Workshop Proceedings (CEUR-WS.org)
45
monographs with few thousands of citations. He is joining IIT Delhi, he has worked for IIM Rohtak,
the Editor in Chief of IJIM Data Insights, published IBM Research, and Cognizant. He has also handled
by Elsevier, the companion journal of International over 30 research, advocacy and training projects
Journal of Information Management. He further from national and international firms and
actively supports reputed knowledge dissemination governments. He has held multiple administrative
platforms like Int. Journal of Electronic positions as chair of corporate relations, academic
Government Research, Information Systems program improvement, doctoral colloquium,
Frontiers, Advances in Theory and Practice of strategic growth, faculty recruitment, IT
Emerging Markets, Global Journal of Flexible automation, and so on. He has received numerous
Systems Management, Int. Journal of Information recognitions for his research contributions from
Management, ICIS, PACIS, ECIS, and IFIP reputed organizations like IFIP, TCS, PMI, AIMS,
conferences as associate editor and on the editorial IIT Delhi, BK Birla (BimTech), and IIM Rohtak.
board. He has been a guest editor for journals like Prior to joining IIT Delhi, he has worked in IIM
Industrial Marketing Management, International Rohtak, Cognizant Business Consulting, and IBM
Journal of Information Management, Information India Research Laboratory.
Systems Frontiers, Australasian Journal of
Information Systems, and Journal of Advances in
Management Research. He has received reviewing
excellence awards from multiple journals like I&M,
GIQ, IJIM, LUP, JRCS, JOCS, and ESWA. Prior to
2. References
Enterprise Information Management. 32(5),
[1]Chakraborty, A., & Kar, A. K. (2016). A review 735-757.
of bio-inspired computing methods and potential [7]Grover, P., Kar, A. K., Janssen, M., &
applications. In Proceedings of the international Ilavarasan, P. V. (2019). Perceived usefulness,
conference on the signal, networks, computing, ease of use, and user acceptance of blockchain
and systems (pp. 155-161). Springer, New technology for digital transactions–insights from
Delhi. user-generated content on Twitter. Enterprise
[2]Chakraborty, A., & Kar, A. K. (2017). Swarm Information Systems, 13(6), 771-800.
intelligence: A review of algorithms. In [8]Grover, P., Kar, A.K. & Dwivedi, Y.K. (2020).
Nature-Inspired Computing and Optimization Understanding Artificial Intelligence Adoption
(pp. 475-494). Springer, Cham. in Operations Management – Insights from the
[3]Grover, P. & Kar, A.K. (2020). User review of academic literature and social media
Engagement for Mobile Payment Service discussions. Annals of Operations
Providers – Introducing the Social Media Research. https://doi.org/10.1007/s10479-020-0
Engagement model. Journal of Retailing and 3683-9
Consumer [9]Gupta, S., Kar, A. K., Baabdullah, A., &
[4]Grover, P., & Kar, A. K. (2017). Big data Al-Khowaiter, W. A. (2018). Big data with
analytics: A review of theoretical contributions cognitive computing: A review for the future.
and tools used in literature. Global Journal of International Journal of Information
Flexible Systems Management, 18(3), 203-229. Management, 42, 78-89.
[5]Grover, P., Kar, A. K., & Ilavarasan, P. V. [10]Kar, A. K. (2014). A decision support system
(2017). Understanding the nature of social for website selection for internet-based
media usage by mobile wallets service advertising and promotions. In Emerging Trends
providers–an exploration through the SPIN in Computing and Communication (pp.
framework. Procedia computer science, 122, 453-457). Springer, New Delhi.
292-299. [11]Kar, A. K. (2015). Integrating websites with
[6]Grover, P., Kar, A. K., & Janssen, M. (2019). social media–An approach for group decision
Diffusion of blockchain technology. Journal of
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support. Journal of Decision Systems, 24(3), [15]Kar, A.K. (2020). What affects Usage
339-353. Satisfaction in Mobile Payments? Modeling
[12]Kar, A. K. (2016). Bio-inspired computing–a User Generated Content to develop the “Digital
review of algorithms and scope of applications. Service Usage Satisfaction Model”. Information
Expert Systems with Applications, 59, 20-32. Systems Frontiers. DOI:
[13]Kar, A. K., & Pani, A. K. (2014). How can a 10.1007/s10796-020-10045-0
group of procurement experts select suppliers? [16]Singh, H., Kar, A. K., & Ilavarasan, P. V.
An approach for group decision support. Journal (2017, March). Performance assessment of
of Enterprise Information Management, 27(4), e-government projects: a multi-construct,
337-357. multi-stakeholder perspective. In Proceedings of
[14]Kar, A.K. & Dwivedi, Y.K. (2020). Theory the 10th International Conference on Theory and
building with big data-driven research – Moving Practice of Electronic Governance (pp. 558-559)
away from the “What” towards the “Why”.
International Journal of Information
Management.102205.