Management Information Systems

Economic and social systems are complex structures emerging from the interactions of a large number of agents, and outcomes are often not directly predictable from individual behaviors.

This research area tests “what-if” questions using agent-based modeling, simulation, and scenario analysis. Models are developed in R and Python environments and are integrated with machine learning techniques to be transformed into both explanatory and predictive systems.

This research area examines the effectiveness of household waste management systems and the transition process toward a zero-waste approach.

The study analyzes the political, infrastructural, social, and behavioral factors required for consumers to reduce waste generation and adopt behaviors such as reuse and composting. It also investigates the integration of circular economy principles into waste management systems.

Behavioral finance examines financial decision-making processes beyond the assumptions of classical economics by incorporating individuals’ psychological, cognitive, and emotional biases. This research area analyzes investor behavior, cognitive biases (such as overconfidence, herd behavior, and loss aversion), risk perception, and irrational elements in decision-making processes.

It investigates how price formation in financial markets is shaped not only by rational expectations but also by behavioral factors. In this context, market anomalies, investor sentiment, and behavioral modeling approaches are studied to better understand and predict financial decision-making processes.

Digital payment technologies, electronic money systems, and FinTech ecosystems comprehensively address the digitalization of financial services. This research area examines the development of digital payment infrastructures, electronic money applications, and the adoption processes of financial technologies.

In addition, the effects of digitalization on economic performance, money circulation velocity, and financial system efficiency are analyzed. In this context, the transformation of financial systems is addressed at both institutional and macroeconomic levels.

Entrepreneurship is one of the key drivers of economic growth, employment, and innovation. This research area includes entrepreneurship ecosystems, technology entrepreneurship, corporate entrepreneurship, and business model innovation.

Incubation centers, accelerator programs, technoparks, and university-based entrepreneurship structures are evaluated within this scope. The growth processes of startups and ecosystem performance are analyzed using data analytics and network approaches.

This research area focuses on revenue management and pricing optimization strategies aimed at improving profitability and efficiency.

Dynamic pricing models, demand forecasting, and consumer behavior analysis are used to determine optimal pricing strategies. In addition, advanced analytics, machine learning, and artificial intelligence tools are employed to develop decision systems that adapt to resource allocation and market fluctuations.

Special emphasis is placed on real-time data integration and the development of pricing frameworks aligned with business objectives.

This research develops decision-making frameworks based on statistical and probabilistic principles and examines decision-making processes under uncertainty.

Machine learning techniques are used alongside probability distribution parameter estimation, financial volatility analysis, operational analytics, and consumer preference modeling methods. The aim is to model uncertainty structures affecting individual and organizational decisions and to provide a theoretical foundation for AI-based decision systems.

In the digital age, relationships between individuals, businesses, and institutions are as important as the resources they possess. This research area examines the structure, information flows, and collaboration mechanisms of social, economic, and technological networks.

Network data is derived from organizational communication records, collaboration platforms, and digital interactions and is transformed into measurable network indicators. The evolution of networks over time is analyzed using dynamic modeling approaches.

This research area examines the integration of business intelligence (BI) and data analytics to enhance informed decision-making processes in organizational contexts.

Core BI principles, data management strategies, and effective database querying practices are evaluated within social science applications. In addition, the impact of machine learning and artificial intelligence on business operations and data-driven marketing strategies is analyzed.

A key aspect of this research is the integration of advanced analytics into Enterprise Resource Planning (ERP) systems to ensure seamless information flow across business processes.

Sustainable finance refers to an approach in which financial decisions are made by considering environmental, social, and governance (ESG) factors.

This field aims at long-term value creation that incorporates environmental responsibility, social justice, and ethical governance alongside economic gains. Sustainable finance analysis evaluates financial decisions of firms and investors within this framework, jointly addressing risk management and opportunity analysis.

This research area examines the relationship between sustainable consumption habits and consumers’ disposal behaviors (reuse, donation, recycling, or disposal).

The product life cycle and the consumer-product relationship are considered key determinants of sustainable behavior. In addition, the effects of economic, socio-cultural, historical environments, personal values, and household routines on post-consumption behaviors are analyzed.

In today’s rapidly changing technological environment, firms’ ability to gain competitive advantage largely depends on their capacity to generate and manage innovation. This research area includes innovation strategies, R&D management, technology transfer, open innovation, technology adoption, and the development of firms’ innovation capabilities.

Firm entry into new technological domains, collaboration networks, and data analytics methods are examined. The aim is to understand how organizations evaluate technological opportunities and build sustainable competitive advantage.

Big data analytics, economic and managerial data analysis, forecasting models, and causality analysis constitute the core components of this research area

Research focuses on supporting decision-making processes through econometric modeling, time series analysis, and causality tests. In addition, business intelligence (BI) systems, data management strategies, and effective database querying are examined as part of organizational decision-support infrastructures.

The impact of machine learning and artificial intelligence on business processes and data-driven strategies is also included in this field.

This research area focuses on applications of artificial intelligence and machine learning techniques in business and social sciences.

Core topics include consumer behavior modeling, financial forecasting, operational analytics, and decision support systems. The integration of these advanced analytics methods with Enterprise Resource Planning (ERP) systems and the development of data-driven business processes are also included in the scope.

Investment analysis involves systematic evaluation processes aimed at identifying the best investment opportunities in financial markets and minimizing risk.

In this process, firms’ financial conditions, industry trends, and economic indicators are analyzed to forecast future value changes. Investors make informed portfolio decisions across stocks, bonds, commodities, and other financial instruments.

Fundamental and technical analysis methods are used together to assess market opportunities and risks.

Today, organizations face both digitalization and sustainability pressures simultaneously. The integration of these two processes is referred to as the “twin transition” approach.

This research area examines green innovation, sustainable business models, circular economy, and carbon reduction strategies. It also analyzes the contribution of digital technologies to sustainability goals and their impact on environmental performance.