Research
We develop and apply advanced methods and have access to a huge volume of rich data, and are developing new applications, concepts and theories to analyze it.
At its core, Strategic Business Analytics utilizes quantitative, data-driven models and applications to generate novel insights that can be used to augment business processes and decisions. Through SBA, significant relationships between entities can be identified, existing business processes can be optimized or novel processes developed, and decision making can be improved by more accurate predictions.
The range of methodologies spans both diagnostic and forward-looking (predictive/prescriptive) analytics. The former applies descriptive and diagnostic models to gain a better understanding about customers, products, or organizational processes. The latter utilizes these insights to generate predictions and recommendations for future actions that minimize cost, maximize revenue, or in general optimize one or multiple objectives.
Our research expertise spans the entire range of Strategic Business Analytics. We use a variety of methods in our research projects, from regression models to identify the impact of attributes on a target variable of interest, to advanced Machine Learning and Big Data Analytics models to fully utilize the information in structured, semi-structured, and unstructured (e.g., text, image) data.
Researchers:
Areas of interest: Business Analytics, Fairness/bias/explainability/accountability in data-driven/algorithmic decision making, Big Data analytics, Two-sided Matching, Strategic manipulation in decision scenarios
Methods: Predictive/Prescriptive Analytics, Machine Learning, Exploratory Data Analysis, (Heuristic) Optimization, Simulation, Agent-based Modeling, Game Theory
Areas of Interest: Internet Research, Social Media, Online Communities, User Behaviour, Participation, Vulnerability, Dark Side of the Internet, Maladaptive Behaviour, Digital Divide, People Analytics, Computational Social Science, Social Computing, HCI
Methods: Text analysis, sentiment analysis, Computer Vision, data mining, applied AI, applied ML, network analysis, surveys, mixed-methods
Areas of interest: AI initiatives in Accounting, Finance Transformation, Predictive Financial Forecasting Systems, Information Overload and Deceptive Disclosure, Accountability and Transparency for Managerial Decision-making
Methods: Quantitative Methods, Field Study Research