Doctorate in Business and Socioeconomic Sciences
Sustainable Governance, Social Sustainability and the Well-being of Societies
Our research on sustainable governance has the intention to find out about optimum ways in which governments can contribute to sustainable development.
Resilient regions and cities: There is still demand of clarifying the role of different institutional actors in helping to initiate and develop transition processes towards sustainable development at the local and regional level. Research on regional transition processes which imply the ability to absorb external shocks and activate self-regulative processes could help to understand how certain regions could escape of their lock-ins. Concepts like transition management or evolutionary governance combined with the theory of resilience and path dependence could provide a basis for PhD research. This type of research will most likely concentrate on case study research which has to be understood as research strategy involving an empirical investigation where it is important to include multiple sources of evidence and therefore ideally follow a mixed methods approach. This could be a set of individual case studies which would help to identify certain types of phenomena under study, e.g. studies of particular public and/or private and third sector organizations and institutions or processes of change and adaptation. In such case a sequential explanatory research strategy would have to be adopted where in order to explain and interpret the quantitative data collected in the first phase. Possible PhD topics include an analysis and evaluation of different types of industry-related services (e.g. tourism, energy, etc.) in the context of the European and /or OECD rural development policy. Research in this area could possibly lead to regional development performance measurement.
Evaluating urban/regional climate governance: Environmental problems and issues, such as climate change, are inherently political in nature, which increases the need for legitimate and transparent democratic processes that allow societies and local communities to choose policies that they see as both equitable and effective. Around the world, cities are experimenting with new forms of governance that include collaboration and partnerships with civil society and business actors but what are the lessons learned and how can cities and regions learn from each other. PhD research could focus on the effectiveness of different types of governance structures and mechanisms and include a policy analysis and process and impact evaluation of existing policies. Another angle could be the identification of factors supporting/limiting participatory governance mechanisms which seems to be a key when it comes to climate governance. A comparative analysis of ‘successful’ cities and regions could help to fill a research gap. Such an evaluation research would help to assess the effects and effectiveness of certain policies which allows to apply qualitative, quantitative, and mixed methods depending on the type of evaluation (impact or process evaluation). If the interest focusses more on the regional climate governance performance and the combined regional development assessment than classical quantitative methods may need to be applied (factor analysis, regression analysis, logit models, structural equation models, etc.).
Multi-level and multi-actor dimensions of governance in the context of sustainability: How do mechanisms of one governance regime influence and/or overwhelm the impacts of another? Work could concentrate on the question of co-existence, interaction and co-evolution of different governance regimes where empirical investigations detected different types of interactions between the regimes and provide a good basis for future research. This should put more emphasis on the mechanisms through which one regime might influence another and how emerging governance regimes initiate and shape transition processes. From a methodological point of view a mixed methods approach would allow to intensify triangulation as well as supplementing more comparative empirical studies.
Tropical deforestation, indigenous peoples land rights and inequality: Tropical deforestation is an important contributor to climate change, through the release of carbon in the atmosphere. Today indigenous communities represent an important actor in the management of land resources. It is estimated that more than 8.5 billion ha of land are being managed by indigenous and local communities. Evidence suggests that the recognition of land rights to indigenous communities can slow down the process of deforestation. However, the process of formal recognition of land rights to indigenous communities is still difficult. This has also profound implications in terms of the rights of indigenous peoples to exist and reflect the existence of deep inequalities (compared to non-indigenous populations). Possible PhD topics would therefore look at both the process of recognition of indigenous communities rights to land and/or to its effects on deforestation in tropical regions.
Leading drivers of subjective well-being or ill-being: Subjective well-being assessment has become an integral part of official reporting about the progress of societies. Nevertheless, and in spite of a variety of survey data analyses, there is not too much known, still, about the decisive drivers of positive or negative mood or life perception. In order to reach the required depth of such an analysis, mixed methods have to be applied which combine quantitative and qualitative data. Research gaps can be closed by re-analysis of existing literature and data, and statistical evaluation of a couple of large-scale public data sets, and by collecting new data, including well-designed survey data as well as qualitative interviews. This may refer to an exploration of the most important influencing factors or to the careful analysis of a theoretically selected variable.
Well-being and citizen science: Including non-professionally academic citizens into the scientific progress, in particular regarding data collection, serves 2 purposes: the work which is done by the volunteers and the resulting database which exceeds the options of traditional structures, and the opening of science towards the major part of society which are not necessarily professional researchers. Regarding quality of life and well-being, the citizen science approach look particularly appealing at first sight, since larger-scale observation would be made possible by an increased number of observers. On the other hand, latent processes such as well-being are difficult to observe and need a well-reflected at least professionalized observation regime. To a lesser extent, this is also true for drivers of well-being. Innovative research could relate to new operationalizations of well-being components and drivers in order to make them appropriate for citizen science data collection, which would open new options for monitoring and controlling societal trends and, at the end of the day, designing interventions for improving states, conditions and circumstances within a society.
International educational system evaluations (such as PISA and PIAAC) and their methodology: Educational achievement, in particular competencies in the context of everyday life, has become an element of international reporting about societal progress, for example in OECD’s “Better Life Index” webpage (http://www.oecdbetterlifeindex.org/). It has become standard that data sets are publicly available and therefore provide a rich source of analysis options. However, the very complex non-standard methodology limits the range of potential researchers who are able to carry out professional data analyses as required, and already standard analyses require an in-depth consideration of methodological issues. Potential thesis topics refer to multivariate and trend analysis on the basis of the international data sets, in particular regarding well-being and diversity, but also to methodological considerations such as analytical options provided by the analytical framework, robustness, goodness-of-fit and efficiency of the models applies.