Big Data in Information Systems: A Review
Hrishitva Patel *
University of Texas, San Antonio, USA.
*Author to whom correspondence should be addressed.
Abstract
The emergence of big data has brought about a significant transformation in the domain of Information Systems, presenting academics and companies with unparalleled prospects and complexities. This abstract examines the potential risks and benefits associated with conducting research in a dynamic and fast growing field. The field of Information Systems is characterized by the significant potential of big data research to bring about transformative effects on various sectors and societies. However, this promising development also gives rise to apprehensions surrounding issues of privacy, ethics, and data security. The potential benefits of big data research are many and varied. First and foremost, this technology offers the potential to extract practical and applicable knowledge from extensive and varied collections of data. This, in turn, facilitates decision-making based on data, fosters innovation, and enhances effectiveness across multiple industries. Furthermore, it enables the progression of cutting-edge technologies, such as machine learning and artificial intelligence, which possess the capacity to propel substantial improvements in the field of Information Systems. In conclusion, the utilization of big data research has the potential to augment our comprehension of intricate phenomena, facilitate predictive analytics, and stimulate the advancement of tailored services, consequently amplifying user experiences. Nevertheless, the potential risks associated with conducting big data research are equally substantial. The rapid expansion of data gathering and analysis has given rise to apprehensions regarding the protection of data privacy, security, and ownership. Academic researchers are confronted with the task of effectively addressing ethical quandaries pertaining to the acquisition and utilization of sensitive personal data. Furthermore, it is imperative to carefully contemplate the significant concern around algorithmic bias and discrimination in the context of data-driven decision-making. Furthermore, the considerable quantity and intricate nature of data provide obstacles in relation to the quality of data, the administration of data, and the ability to scale.
Keywords: Big data, information system, security, data security
How to Cite
Downloads
References
Bahrami M, Shokouhyar S. The role of big data analytics capabilities in bolstering supply chain resilience and firm performance: A dynamic capability view. Information Technology & People, ahead-of i>-print(ahead-of-print); 2021. DOI:https://doi.org/10.1108/ITP-01-2021-0048.
Cadden T, Dennehy D, Mantymaki M, Treacy R. Understanding the influential and mediating role of cultural enablers of AI integration to supply chain. International Journal of Production Research, 2021;1–29. DOI:https://doi.org/10.1080/00207543.2021.1946614.
Frederico GF, Kumar V, Garza-Reyes JA, Kumar A, Agrawal R. Impact of I4.0 technologies and their interoperability on performance: Future pathways for supply chain resilience post-COVID-19. The International Journal of Logistics Management, ahead-of i>-print(ahead-of-print); 2021. DOI:https://doi.org/10.1108/IJLM-03-2021-0181.
Hiebl MRW. Sample Selection in Systematic Literature Reviews of Management Research. Organizational Research Methods. 2021;10944281 20986851. DOI:https://doi.org/10.1177/1094428120986851.
Abrahamson E, Bartner LR, Do bandwagon diffusions roll? How far do they go? And when do they roll backwards?. When: A Computer Simulation. Academy of Management Proceedings, 1990;1990(1):155–159. DOI:https://doi.org/10.5465/ambpp.1990.4978478.
Amankwah-Amoah J, Khan Z, Wood G. COVID-19 and business failures: The paradoxes of experience, scale, and scope for theory and practice. European Management Journal, Article in Press. 2020;1–6. Scopus. DOI:https://doi.org/10.1016/j.emj.2020.09.002.
Bag S, Dhamija P, Luthra S, Huisingh, D. How big data analytics can help manufacturing companies strengthen supply chain resilience in the context of the COVID-19 pandemic. The International Journal of Logistics Management, ahead-of i>-print(ahead-of-print); 2021. DOI:https://doi.org/10.1108/IJLM-02-2021-0095.
Hosseini S, Ivanov D. A New Resilience Measure for Supply Networks With the Ripple Effect Considerations: A Bayesian Network Approach.Annals of Operation Research; 2019. Available:https://aquila.usm.edu/fac_pubs/16506.
Khan SAR, Yu Z, Umar M, Lopes de Sousa Jabbour, AB, Mor RS. Tackling post-pandemic challenges with digital technologies: An empirical study. Journal of Enterprise Information Management, ahead-of i>-print(ahead-of-print); 2021. DOI:https://doi.org/10.1108/JEIM-01-2021-0040.
Modgil S, Gupta S, Stekelorum R, Laguir I. AI technologies and their impact on supply chain resilience during COVID-19. International Journal of Physical Distribution & Logistics Management, ahead-ofi>-print(ahead-of-print); 2021a. DOI:https://doi.org/10.1108/IJPDLM-12-2020-0434.
Modgil S, Singh RK, Hannibal C. Artificial intelligence for supply chain resilience: Learning from Covid-19. The International Journal of Logistics Management, ahead-ofi>-print(ahead-of-print); 2021b. DOI:https://doi.org/10.1108/IJLM-02-2021b-0094.
Nayal K, Raut R, Priyadarshinee P, Narkhede BE, Kazancoglu Y, Narwane V. Exploring the role of artificial intelligence in managing agricultural supply chain risk to counter the impacts of the COVID-19 pandemic. The International Journal of Logistics Management, ahead-of i>-print(ahead-of-print); 2021. DOI:https://doi.org/10.1108/IJLM-2021;12-2020-0493.
Patyal V, Sarma P, Modgil S, Nag T, Dennehy D. Mapping the links between Industry 4.0, Circular Economy and Sustainability: A Systematic Literature Review. Journal of Enterprise Information Management; 2021.
Purvis L, Spall S, Naim M, Spiegler V. Developing a resilient supply chain strategy during ‘boom’ and ‘bust.’. Production Planning & Control, 2016;0–0. DOI:https://doi.org/10.1080/09537287.2016.1165306.
Ross JW, Beath CM, Quaadgras A. December 1). You May Not Need Big Data After All.Harvard Business Review; 2013. Available:https://hbr.org/2013/12/you-may-not-need-big-data-after-all.
Wenzel M, Stanske S, Liberman MB. Strategic responses to crisis. Strategic Management Journal, 2021;42(2). DOI:https://doi.org/10.1002/smj.3161.
Yen BPC, Zeng B. Modeling and Analysis of Supply Chain Risk System under the Influence of Partners’ Collaboration. 2011 44th Hawaii International Conference on System Sciences, 2011;1–10. DOI:https://doi.org/10.1109/HICSS.2011.311.
Abrahamson E. Managerial Fads and Fashions: The Diffusion and Rejection of Innovations. Academy of Management Review. 1991:16.
Ahmad MO et al. Kanban in software engineering: A systematic mapping study. Journal of Systems and Software. 2018: 137.
Azadegan A, et al., Supply chain involvement in business continuity management: Effects on reputational and operational damage containment from supply chain disruptions. Supply Chain Management: An International Journal. 2020;25.
Barlette Y, Baillette P, Big data analytics in turbulent contexts: Towards organizational change for enhanced agility. Production Planning & Control. 2022;33.
Baryannis G, et al., Supply chain risk management and artificial intelligence: State of the art and future research directions. International Journal of Production Research. 2019;57.
Belhadi A, et al., Manufacturing and service supply chain resilience to the COVID-19 outbreak: Lessons learned from the automobile and airline industries. Technological Forecasting and Social Change. 2021;163.
Belhadi A, et al., Building supply-chain resilience: An artificial intelligence-based technique and decision-making framework. International Journal of Production Research; 2021.
Belhadi A, et al., Artificial intelligence-driven innovation for enhancing supply chain resilience and performance under the effect of supply chain dynamism: An empirical investigation. Annals of Operations Research; 2021.
Brandon-Jones E et al. A Contingent Resource-Based Perspective of Supply Chain Resilience and Robustness. Journal of Supply Chain Management. 2014:50.
Cavalcante IM et al., A supervised machine learning approach to data-driven simulation of resilient supplier selection in digital manufacturing. International Journal of Information Management. 2019; 49.
Chen DQ, Preston DS, Swink M. How the Use of Big Data Analytics Affects Value Creation in Supply Chain Management. Journal of Management Information Systems. 2015;32.
Chowdhury MMH, Quaddus M, Supply chain readiness, response and recovery for resilience. Supply Chain Management: An International Journal. 2016;21.
Collins C, et al., Artificial intelligence in information systems research: A systematic literature review and research agenda. International Journal of Information Management. 2021;60.
Conboy K, et al., Using business analytics to enhance dynamic capabilities in operations research: A case analysis and research agenda. European Journal of Operational Research. 2020;281.
Constantiou I, Shollo A, Vendelø MT, Mobilizing intuitive judgement during organizational decision making: When business intelligence is not the only thing that matters. Decision Support Systems, 2019;121.