Utilizing big data analytics for information systems research: challenges, promises and guidelines

Abstract

This essay discusses the use of big data analytics (BDA) as a strategy of enquiry for advancing information systems (IS) research. In broad terms, we understand BDA as the statistical modelling of large, diverse, and dynamic data sets of usergenerated content and digital traces. BDA, as a new paradigm for utilising big data sources and advanced analytics, has already found its way into some social science disciplines. Sociology and economics are two examples that have successfully harnessed BDA for scientific enquiry. Often, BDA draws on methodologies and tools that are unfamiliar for some IS researchers (e.g., predictive modelling, natural language processing). Following the phases of a typical research process, this article is set out to dissect BDA's challenges and promises for IS research, and illustrates them by means of an exemplary study about predicting the helpfulness of 1.3 million online customer reviews. In order to assist IS researchers in planning, executing, and interpreting their own studies, and evaluating the studies of others, we propose an initial set of guidelines for conducting rigorous BDA studies in IS.

Digital Service Innovation Enabled by Big Data Analytics - A Review and the Way Forward

“…Yet, we argue for two ways the domains of DSI and BDA can benefit one another, along the process of innovation. The first way is for DSI to make use of BDA as a method for scientific enquiry [57]. When services are provisioned through a collaborative network, the innovation process becomes more challenging, both to understand and to conduct.…”

“…Note that such studies describe business models that nurture service innovation, unlike service-specific business models mentioned in the previous section. Second, BDA enables the development of data science methods as emerging modes of scientific enquiry [57]. For example, BDA methods can be used to model service networks, support decision-making at various phases along the service innovation process and enhance an organization's environmental scanning and competitive intelligence [19,20] [23].…”

Theory-driven or process-driven prediction? Epistemological challenges of big data analytics

“…Predictive studies include inductive discovery of relationships among variables in a given dataset, whereby the discovery is driven by techniques and algorithms, without testable a priori hypotheses about causal relationships to be explicitly formulated (e.g. [9]). …”

“…Twitter is certainly not the largest pool of users, and some of the accounts are used by bots, not humans [9]. Furthermore, many companies are using it as a way to boost sales, analyzing tweets "only" is indeed biased.…”

“…Recently [9] have provided guidelines for employing BDA in IS research. They conclude that "reflecting on the guidelines, we can observe that each phase of the research process requires a revised set of actions and abilities" (p. 11) and advocate a skill set change for IS researchers with stronger emphasis on developing skills for data preparation and the deployment of analytical tools and cross-instrumental evaluation criteria.…”