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. 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, behavioural theory, computer science theories, and systems theory. Apart from the core computer science theories, the other related theories enable IS researchers to explain how users interact with technology artefacts 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.
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. 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. Furthermore, since the data collection is often dated, such studies lose timeliness and do not attempt to explain causality.
This special issue intends to facilitate theory-focused research, based on the analysis of big data as outlined in the directions provided in a recent opinion paper published in the journal. 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.
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. 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.