Frontiers of Business Intelligence and Analytics

Three out of seven application areas of BI & A 3.0 are underrepresented according to the results of our literature review: Smart health, Security & public safety, and E-Government & politics. To extend the body of knowledge, we suggest focusing BI & A 3.0 research on these three application areas. Thereby, we are aware that the size of each area is not comparable. E-government research for example is generally underrepresented in IS research by having a look into publications, highly cited articles appear. Security & public safety is also a central topic in other scientific disciplines, such as "computer security, computational criminology, and terrorism informatics", which might be a reason for its current underrepresentation in IS research.

In the area of Smart health, researchers may investigate the effects of health simulations, based on fitness tracker apps. The pharma industry is increasingly investing in the development of digital supported therapies via apps or smart health devices but many obstacles exist regarding data reliability . Combined with the dimension human computer interaction, the question "How can health simulation systems based on tracked fitness data be developed to influence individual`s health behavior" needs to be answered (1). One challenge in this regard is the limited availability of efficient solutions to make use of all the data available by sensors in or close-to real-time. In the Security and public safety area, researchers may investigate the effects of smart home systems. Both, the effects on the probability of burglaries and the insurance industry as a whole need to be investigated. Therefore, we suggest the following research question: "How may smart home systems influence burglary probabilities and which effects does that have on the insurance industry". Furthermore, access to smart home data is strictly regulated by privacy laws, so that the question of "how to secure smart home systems regarding the misuse of data" might additionally be raised (2). Predicting burglaries and its economic effects is a research field, which needs further data science foundations. Prototypes that collect and analyze smart home data are necessary. Again, we suggest evaluating these kinds of prototypes by an action research approach.

The scandal of Cambridge Analytica and its influence on the Trump campaign in the U.S. elections in 2016 evidenced the importance of data analytics for E-Government and politics. In this application area, researchers may exemplarily investigate the influence of big data delivered by sensors, such as traffic, smart phone or smart home devices on the election behavior of citizens as well as ways to prevent such data from misuse. This leads to the question of how sensor data may be used to manipulate elections and which regulations are demanded for preventing such data misuse (3). Answering this question requires at least a theory building part, for which we suggest applying a grounded theory approach. We classify this research question as diagnostic, since it focuses on analyzing sensor-based data and its diagnostic power to explain election results.