|Call for Papers|
International Journal of Information Management
Theory Building in IS with Big Data-Driven Research
The availability of and access to big data has changed, as digital transformation initiatives are increasingly maturing globally, assisted by the growth of computational capabilities (Grover et al., 2020). Whilst data availability and access used to be a major challenge for information systems (IS) research, the current abundance of big data has now resolved this considerably. The theoretical building blocks of IS research come mainly from management theory, organization theory, behavioral theory, computer science theories, and systems theory (Barki, Rivard and Talbot, 1993). Apart from the core computer science theories, the other related theories enable IS researchers to explain how users interact with technology artifacts within individual, organizational, social, and political contexts and the impact of such interaction. Theory building, however, seems to have been disrupted by the current trends in big data-driven research, whereby the essence of contributing to theory is increasingly seen to be lacking at all levels of analysis. Concurrently, big data-driven research may inspire contributions towards design science and action research, whereby innovative solutions may also be created which help to define ideas, capabilities, practices, and innovative products or services through big data analysis (Angelopoulos et al., 2020; Hevner et al., 2004).
While big data-driven studies are increasingly gaining popularity within IS research, they rarely introspect why a phenomenon is better explained by a theory and limit the analysis to what is happening by merely mining relevant data. Many such studies try to collect data and showcase applications of data science and visualization of unstructured, large volumes of data by demonstrating sentiment analysis, text mining, networks, and communities, without significant contribution to the theoretical context within which the problem is situated (Grover, 2020). Such studies do not attempt to explain why a particular phenomenon is witnessed and the data descriptions rarely contributes towards theory building. Thus, such studies have a weak connection with the relevant theories and IT artefacts, paying greater attention to data collection and analysis (Grover et al., 2020). Furthermore, since the data collection is often dated, such studies lose timeliness and do not attempt to explain causality (Grover, 2020).
This special issue intends to facilitate theory-focused research, based on the analysis of big data as outlined in the directions provided in the opinion paper of the editors (Kar and Dwivedi, 2020). We specifically seek for theory building attempts in addressing grand individual, organizational, social or political problems. In particular, studies should demonstrate ample representative elements of big data such as high volume, velocity, variety, variability, veracity, visualization, and leading to value in understanding the phenomenon under examination. Authors need to explain why a phenomenon is happening rather that what is happening. The connection with IT artefacts must be significantly strong, while studies on emerging technologies are also of interest. Units of analysis could be individuals, groups or organizations. Of particular interest are theories explaining the nature of interaction among entities from more than a single unit type. Further, studies which mine more than a single type of unstructured data, and multi-methodology studies are also of interest, provided they contribute to theory building. Data could potentially be extracted from platforms like social media, communication networks, text-based data, images, multi-media, online communities, application-based data, sensor-based data, location-based data, data from smart-phones, smart devices and wearables. Other sources would also be welcome as long as the studies contribute theoretically. For submissions to this special issue, prospective authors are encouraged to revisit recent examples of such endeavors in the IS literature (Oh et al., 2015; Grover et al., 2019a; Grover et al., 2019b; Kar, 2020; Georgiadou et al., 2020; Rao et al., 2020). Research topics may include, but are not limited to:
- How can we explain user interactions, consumer experiences, and impacts for emerging business models like digital services, location-based services or platform economy?
- How can big data-driven research be used to explain digital service or emerging technology adoption, usage, and impact behavior based on mining user generated content (UGC)?
- How can UGC be mined to explain user behavior in socio-political contexts like opinion polarization, acculturation or communal changes?
- How can user engagement or disengagement in digital platforms or technologies like wearables be measured and explained based on big data analytics?
- How can we explain phenomena surrounding digital service usage, user migration and experiences based on network data (e.g. telecommunication services)?
- How can we develop typology of users or organizations based on UGC from online communities, social media and digital platforms?
- How can we explain relationships between organizations and other stakeholders based on UGC from digital platforms and online marketplaces and their impacts on engagement or disengagement?
- How can we model adverse impacts of disruptive technologies like artificial intelligence, blockchain, internet of things based on usage behavior or UGC?
- How can we explain user behavior and impacts based on data derived out of sensor-based data like wearables or other smart technologies used at home?
- How can we explain community driven behavior for information and misinformation propagation, cascade and changes to the ecosystem?
- How can we model determinants of information quality, misinformation or disinformation based on UGC, social networks, and user attributes?
- How can we model computationally derived attributed of images and videos to study consumer engagement and interaction processes, and outcomes?
- How can theories be developed to explain grand socio-political problems and challenges of like pandemic management, sustainable development goals, political harmony, etc?
- How can methods of NeuroIS and facial recognition be used to explain individual and group level behaviour like personality traits and socio-political behavioral inclination?
- How can multi-modal data analysis be used to create knowledge surrounding the process and impacts of use of emerging smart technologies?
Such explorations would need to ensure that theoretical contributions are well developed to contribute to IS research as highlighted in the editorial note developed in context of the special issue (Kar and Dwivedi, 2020). We welcome attempts to address domain, socio-political, structural or ontological, and epistemological questions through the development of management theories, organization theories, behavioural theories, and systems theories.
Manuscript submission: 30-Jul-2021
Notification of Review: 30-Sep-2021
Revision due: 30-Nov-2021
Notification of 2nd Review: 30-Jan-2022
2nd Revision [if needed] due: 31-Mar-2022
Notification of Final Acceptance: 30-Apr-2022
Expected Publication: Within 8 weeks of Acceptance by SI Editors
All submissions have to be prepared according to the Guide for Authors as published in the Journal’s website. Authors should select “SI: Theory building with big data”, from the “Choose Article Type” pull- down menu during the submission process. All contributions must not have been previously published or be under consideration for publication elsewhere.
Link for submission of manuscripts : https://www.evise.com/evise/jrnl/IJIM
A submission based on one or more papers that appeared elsewhere must bear major value-added extensions or updates (at least 50% of new material). Authors are requested to attach to the submitted paper their relevant, previously published articles and a summary document explaining the enhancements made in the journal’s version. All submitted papers will undergo a rigorous peer-review process that will consider programmatic relevance, scientific quality, significance, originality, style and clarity. The acceptance process will focus on papers that address original contributions in the form of theoretical, empirical and case research, leading to new perspectives on big data-driven theory building in information systems.
Arpan K. Kar is an Associate Professor in the Department of Management Studies, Indian Institute of Technology Delhi, India. His research interests are in the domain of data science, digital transformation, internet ecosystems, social media and ICT-based public policy. He has authored over 100 peer reviewed articles and edited 6 research monographs. He actively supports reputed knowledge dissemination platforms like International Journal of Information Management, International Journal of Electronic Government Research, Information Systems Frontiers, Advances in Theory and Practice of Emerging Markets, Global Journal of Flexible Systems Management, ICIS, PACIS, ECIS and IFIP conferences as associate/coordinating editor or on the editorial board. He has received reviewing excellence awards from multiple journals like I&M, GIQ, IJIM, LUP, JRCS and ESWA. Prior to joining IIT Delhi, he has worked for IIM Rohtak, IBM Research and Cognizant. He has also handled a lot of research, advocacy and training projects from national and international firms and governments. He has received numerous recognitions for his research contributions from reputed organizations like IFIP, TCS, PMI, AIMS, IIT Delhi, BK Birla (BimTech) and IIM Rohtak.
Spyros Angelopoulos is an Associate Professor at Durham University Business School, in the UK. His research focuses on the behavior of users on online platforms, the adaptation of organizations during digital transformation endeavors, and on security and privacy issues of users on online platforms. He was selected as 2019-2020 ‘Cloud Faculty Expert’ by Google for his commitment to leading transformation in cloud computing education and research. He is also editing a special issue in the Journal of Operations Management, on “Operations Management in the Age of Digital Transformation”. He has received best paper awards for his work on data driven research on online platforms.
H.R. Rao is the AT&T Distinguished Chair at the University of Texas at San Antonio College of Business. Prior to working at UTSA, Professor Rao was the SUNY Distinguished Service Professor at the University at Buffalo. He graduated from Krannert Graduate School of Management at Purdue University. Professor Rao was inducted into the UTSA Academy of Distinguished Researchers in 2019. He has authored or co-authored more than 200 technical papers, of which more than 125 are published in archival journals. Professor Rao was the inaugural recipient of The Bright Internet Award for his contributions to the information systems discipline by KMIS, the Korea Society of Management Information Systems. In 2018 Professor Rao was awarded the International Federation for Information Processing (IFIP) Outstanding Service Award for significant service contributions to the field of information systems and information systems security. In November 2016, Professor Rao received the prestigious Information Systems Society Distinguished Fellow Award (Class of 2016) for outstanding intellectual contributions to the information systems discipline. Rao’s work has received best paper and best paper runner up awards at ISR, AMCIS and ICIS. He is co-editor in chief of Information Systems Frontiers, advisory editor of Decision Support Systems, associate editor of ACM TMIS and senior editor at MIS Quarterly. Rao was ranked No. 3 in publication productivity internationally in a 2011 CAIS study.
Angelopoulos, S., Brown, M., McAuley, D., Merali, Y., Mortier, R. and Price, D. (2020). Stewardship of Personal Data on Social Networking Sites, International Journal of Information Management.
Barki, H., Rivard, S., & Talbot, J. (1993). A keyword classification scheme for IS research literature: an update. MIS Quarterly, 17(2), 209-226.
Georgiadou, E., Angelopoulos, S., & Drake, H. (2020). Big data analytics and international negotiations: Sentiment analysis of Brexit negotiating outcomes. International Journal of Information Management, 51, 102048.
Grover, P., Kar, A. K., & Ilavarasan, P. V. (2019b). Impact of corporate social responsibility on reputation—Insights from tweets on sustainable development goals by CEOs. International Journal of Information Management, 48, 39-52.
Grover, P., Kar, A. K., Dwivedi, Y. K., & Janssen, M. (2019a). Polarization and acculturation in US Election 2016 outcomes–Can twitter analytics predict changes in voting preferences. Technological Forecasting and Social Change, 145, 438-460.
Grover, V. (2020). Do We Need to Understand the World to Know It? Knowledge in a Big Data World. Journal of Global Information Technology Management, 23(1), 1-4.
Grover, V., Lindberg, A., Benbasat, I., & Lyytinen, K. (2020). The Perils and Promises of Big Data Research in Information Systems. Journal of the Association for Information Systems, 21(2), 268-291.
Hevner, A. R., et al. (2004). Design Science in Information Systems Research. MIS Quarterly, 28(1), 75-105.
Kar, A.K. & Dwivedi, Y.K. (2020). Theory building with big data-driven research – Moving away from the “What” towards the “Why”. International Journal of Information Management, 54, 102205. https://doi.org/10.1016/j.ijinfomgt.2020.102205
Kar, A.K. (2020). What Affects Usage Satisfaction in Mobile Payments? Modelling User Generated Content to Develop the “Digital Service Usage Satisfaction Model”. Information Systems Frontiers, https://doi.org/10.1007/s10796-020-10045-0.
Oh, O., Eom, C., & Rao, H. R. (2015). Role of social media in social change: An analysis of collective sense making during the 2011 Egypt revolution. Information Systems Research, 26(1), 210-223.
Rao, H. R., Vemprala, N., Akello, P., & Valecha, R. (2020). Retweets of officials’ alarming vs reassuring messages during the COVID-19 pandemic: Implications for crisis management. International Journal of Information Management, 102187. https://doi.org/10.1016/j.ijinfomgt.2020.102187