Big Data Research in Information Systems: Toward an Inclusive Research Agenda

By Ahmed Ali Abbasi, Suprateek Sarker, Roger H. L. Chiang. Journal of the AIS, 2016

730 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering, Information Systems and Management, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/c113b3f17d8f08bdaaa18ae88c24afd4d41cb543

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In Electrical and Electronic Engineering

In Information Systems and Management

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Citations in the corpus

Big data analytics capabilities: a systematic literature review and research agenda (2017, methodology): “For instance, a commonly acknowledged aspect of big data is its veracity (Akter et al. 2016a, b; Abbasi et al. 2016).”
What Do We Talk About When We Talk About Dashboards? (2019, background): “As the scope of dashboard use moves from merely reporting performance via proxy metrics to more in-depth problem solving [1,11], users also want more analytical support in their tools, particularly “smart” features such as automated analytics (pattern identification, prediction) and what-if simulati”
Debating big data: A literature review on realizing value from big data (2017, background): “There is some considerable acknowledgement in the literature of data being a potential key resource of organizations’ business models (e.g., Abbasi et al., 2016; Struijs et al., 2014).”
Assessing the impact of big data on firm innovation performance: Big data is not always better data (2020, methodology): “This approach has been used in many studies (e.g., Abbasi, Sarker, & Chiang, 2016; Akter et al., 2016; Ghasemaghaei & Calic, 2019a, 2019b; Sun, 2012).”
The Effect of Big Data and Analytics on Firm Performance: An Econometric Analysis Considering Industry Characteristics (2018, background): “But not only the data types and technologies have changed, but also the information value chain, that is, how these technologies are leveraged by managers to extract knowledge from data and support decision making [1].”
Big Data: Tutorial and guidelines on information and process fusion for analytics algorithms with MapReduce (2018, background): “There is a clear growing rate in the number of research studies [13,14], and the trend is not expected to change in the short future.”
Creating Strategic Business Value from Big Data Analytics: A Research Framework (2018, background): “It is estimated that reducing one daily mile driven per driver could save $30 million in a year for the company [1].”
ICT4D research: reflections on history and future agenda (2017, background): “Abbasi, Sarker, and Chiang (2016) argue that the volume, velocity, variety, and veracity of big data disrupt the traditional information value chain and thus open up a whole new set of agenda for IS research.”
Exploring the relationship between big data analytics capability and competitive performance: The mediating roles of dynamic and operational capabilities (2020, background): “…effects of BDACs, as structured adoption is seen as an enabler of the underlying processes that comprise a firm’s overall dynamic capability and can subsequently facilitate better evolutionary fitness by renewing operational capabilities and resulting in competitive performance gains [10,21,22].”
Big Data Analytics Capabilities and Innovation: The Mediating Role of Dynamic Capabilities and Moderating Effect of the Environment (2019, positive): “As such, the major contribution of big data analytics lies in the fact that it enables better informed decision-making, which is subject to less bias and based on empirical evidence (Abbasi, Sarker, & Chiang, 2016).”
Artificial Intelligence and Business Value: a Literature Review (2021, background): “…that form our proposed research agenda, and the corresponding directions described within these themes, also raise several important concerns regarding the extended information value chains of organizations and the related activities within these (Abbasi et al., 2016; Koutsoukis & Mitra, 2003).”