Doc. dr. Sara Atanasova
1. Empowerment theory in contexts of digital health and digital health care
Empowerment is a concept that includes many definitions, understandings and thus dilemmas. The study of empowerment is because of its ambiguity, contextual and temporal dependence, a process of constant search for unexplored areas that allows us to explore its many dimensions.
In the contexts of health and healthcare, the concept of empowerment and its research is not only important because it potentially leads individuals to improved health outcomes, but it represents one of the key mechanisms of (social) change. To achieve empowerment goals, individuals unite in groups and empowerment presents an important condition of individuals’ and groups’ collective action,
especially of those that want to achieve a (social) change and improvement in the quality of their lives. Within this research topic student would focus on a chosen context of digital health and healthcare, e.g. (online) patient-physician relationship, use of self-tracking devices, specific (online) health behavior (search of health-related online information, vaccine hesitancy), context of (public) health care institutions and organizations, telemedicine and its related services, groups of patients with a specific chronic disease, caregivers, etc.
/27With the help of empowerment theory and the integration of different theoretical approaches, the research would include formation of a theoretical framework / model and its empirical research with the help of selected qualitative / quantitative methods or an integrated methodological approach.
Doc. dr. Marjan Cugmas
1. Generative artificial intelligence as a source of social support (with emphasis on methodological and statistical part, e.g., measuring, network analysis)
Izr. prof. dr. Vesna Dolničar
1. Factors influencing the use and outcomes of e-care and e-health services
Doc. dr. Darja Grošelj
1. Digital inequalities & use of generative AI - work within the digital inequalities framework to examine how motivation, access and skills shape the adoption, use and outcomes of generative AI. The thesis will also explore the implications of generative AI use for the reproduction, reduction, or transformation of digital inequalities.
2. Use of generative AI in everyday life – examine how people use generative AI in everyday life, focusing on their perceptions, motivations, and everyday practices. It may explore how users understand the value, usefulness, risks, and limitations of generative AI tools in different daily contexts.
3. Employee perceptions and experiences of generative AI in Slovenian SMEs – set in an organizational context and focuses on how employees in Slovenian small and medium-sized enterprises perceive and experience generative AI. It will examine the factors and processes involved in integrating AI tools into work processes in Slovenian businesses.
Doc. dr. Luka Kronegger
1. Analysis of social networks - the role of social mechanisms in the development of network structures, modelling of the dynamics of the actions of individuals in the network, ...
2. Analysis of unstructured data (web capture systems, computerized text analysis)
3. Issues related to digital sovereignty
Izr. prof. dr. Katja Lozar Manfreda
1. Quality of big data in social science research
In social science research, where empirical work relies heavily on survey data, we face the problem of increasing costs of survey research due to respondents' unwillingness to participate in surveys and decreasing opportunities for effective face-to-face outreach to potential respondents, such as visits or phone calls.
On the other hand, there is increasing interest in Big Data, which comes from administrative sources, online transactions, various sensors and tracking devices, and social media. There are ideas that this data could replace traditional survey data, as it can tell us something about people's behaviour, feelings, attitudes and intentions. However, while there are rigorous standards for the quality of survey data so that such data can be considered suitable for scientific research, there is a lack of quality standards for Big Data. The topic of this dissertation is the exploration of quality standards (indicators) for different types of Big Data to be used in social science research, using the quality standards for survey data as a starting framework to be further developed.
2. Use of gen AI in survey research
Exploration of the use of gen AI in various phases of a survey project. Comparison with “traditional” approaches to survey data collection.
3. Use of gen AI in teaching statistics
Having AI models, why anyone would still want to learn methods of statistical analysis? The dissertation project could explore the usage gen AI in statistical analysis, when it can replace “traditional” statistical approaches, how to motivate students to study statistics, what teaching approaches are needed.
4. Use of gen AI in social science research
Exploration of the use of gen AI in various phases of a social science research project. Comparison with “traditional” approaches to social science research.
5. Methodology of web surveys
Study of different approaches to evaluate and increase data quality in survey data collection.
Prof. dr. Gregor Petrič
1. Generative artificial intelligence as social infrastructure: knowledge, autonomy, and relationships in everyday life
2. Governance of digital social systems: moderation, group dynamics, communication quality, and effects on individual and collective action
3. Cybersecurity and resilience in digital society: developing a socio-technical approach in managing information security and human risks for high organizational resilience
Izr. prof. dr. Damjan Škulj
1. Data mining in social sciences
Doc. dr. Marina Trkman
1. The impact of privacy concerns related to a novel technology on the technology’s adoption success. The candidate will choose a technology of his/her interest to investigate factors that impact its adoption success. Specifically, he/she will focus on the impact of privacy concerns generated by the specific technology and
propose context-specific factors impacting it.
Prof. dr. Vasja Vehovar
1. Web survey methodology
Web survey methodology is a general umbrella term for methodological issues related to the web survey process. A comprehensive overview of the field is provided in the monograph Callegaro, Lozar-Manfreda, Vehovar (2015). Web Survey Methodology (Sage), which is publicly available. All of the topics discussed there can, in principle, also be potential dissertation topics, especially the following:
2. Data analytic topics: Analysis and use of paradata, either the respondent's digital traces created during the response process (e.g., timestamps, navigation patterns, device characteristics) or the researcher's digital traces created during the questionnaire development process.
3. Survey methodology topics: Response quality measurement, diagnosis, and use. This is related to various response quality indicators (e.g., nonresponse, satisficing, speeding, etc.). The factors that affect these quality indicators can be studied, as well as the use and impact of the response quality indicators.
4. Statistical topic: Evaluating probability and non-probability web survey panels. This is addressing the dilemma of whether web panels can replace traditional surveys, and in particular, whether non-probability web panels can replace probability surveys.
Prof. dr. Aleš Žiberna
1. Network analysis, especially blockmodeling – mainly focused on methodologi, that is evaluation and/or development of methods, but also advanced use of methods.
2. Evaluation of different way of measuring collaboration among researchers based on bibliographic data
Back to list of notificationsPublished: 11. May 2026 | Category: 2026/27