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The dissertation consists of three scientific papers and a synopsis. The synopsis addresses the relevance of the dissertation and lists the key factors for the sustainability transition in the electricity system as a common denominator of the three papers.
The relevance of the dissertation results, on the one hand, from the urgency of the sustainability transition in the electricity system and an insufficient transition willingness of the eastern European Member States. On the other hand, the Multi-Level-Perspective as one of the most important scientific frameworks to grasp transitions does not provide a sufficient explanation of its mechanisms. Moreover, Demand Response aggregators as new enterprises on the European electricity market and potential reform initiators are still under researched. The following key factors for the sustainability transition of the electricity system have been identified: supply security concerns, Europeanisation, policy making and the dominance of short-term oriented economic evaluation.
Paper 1
The underlying factors in the uptake of electricity demand response: The case of Poland
Author: Katarzyna Ewa Rollert
Published: Utilities Policy, Volume 54, October 2018, 11-21, https://doi.org/10.1016/j.jup.2018.07.002
Demand response (DR) is considered crucial for a more reliable, sustainable, and efficient electricity system. Nevertheless, DR’s potential still remains largely untapped in Europe. This study sheds light on the roots of this problem in the context of Poland. It suggests that unfavorable regulation is symptomatic of the real, underlying barriers. In Poland, these barriers are coal dependence and political influence on energy enterprises. As main drivers, supply security concerns, EU regulatory pressure, and a positive cost-benefit profile of DR in comparison to alternatives, are revealed. A conceptual model of DR uptake in electricity systems is proposed.
Paper 2
Gaining legitimacy for sustainability transition: A social mechanisms approach
Authors: Ursula Weisenfeld and Katarzyna Ewa Rollert
Review and re-submit: Environmental Innovation and Societal Transitions / August 2021
Applying a social mechanisms approach to the Multi-Level Perspective, we conceptualize mechanisms of socio-technical transitions and of gaining legitimacy for transitions as co-evolutionary drivers and outcomes. Situational, action-formational, and transformational mechanisms that operate as drivers of change in a socio-technical transition require corresponding framing and framing contests to achieve legitimacy for that transition. We illustrate our conceptual insight with the case of the coal dependent Polish electricity system.
Paper 3
Demand response aggregators as institutional entrepreneurs in the European electricity market
Author: Katarzyna Ewa Rollert
Under review: Journal of Cleaner Production / August 2021
Demand Response (DR) aggregators act as intermediaries between electricity customers and network operators to tap the potential of the demand-based flexibility. This qualitative study reveals DR aggregators as institutional entrepreneurs that struggle to reform the still largely supply-oriented European electricity market. Unfavourable regulation, low value of flexibility, resource constraints, complexity, and customer acquisition are the key challenges DR aggregators face. To overcome them they apply a combination of strategies: lobbying, market education, technological proficiency, and upscaling the business. The study highlights DR aggregation as an architectural innovation that alters the interplay between key actors of the electricity system and provides policy recommendations including the necessity to assess the real value of DR in comparison to other flexibility sources by taking all externalities into account, a technology-neutral approach to market design and the need for simplification of DR programmes, and common standards to reduce complexity and uncertainty for DR providers.
Motivation: Maximizing the value from data has become a key challenge for companies as it helps improve operations and decision making, enhances products and services, and, ultimately, leads to new business models. The latter two have been investigated by scholars as part of an emerging research field on data-driven business model innovation. While enterprise architecture (EA) management and modeling have proven their value for IT-related projects, the support of enterprise architecture for data-driven business models (DDBMs) is a rather new and unexplored field. We argue that the current understanding of the intersection of data-driven business model innovation and enterprise architecture is incomplete because of five challenges that have not been addressed in existing research: (1) lack of knowledge of how companies design and realize data-driven business models from a process perspective, (2) lack of knowledge on the implementation phase of data-driven business models, (3) lack of knowledge on the potential support enterprise architecture modeling and management can provide to data-driven business model endeavors, (4) lack of knowledge on how enterprise architecture modeling and management support data-driven business model design and realization in practice, (5) lack of knowledge on how to deploy data-driven business models. We address these challenges by examining how enterprise architecture modeling and management can benefit data-driven business model innovation.
Research Approach: Addressing the challenges mentioned above, the mixed-method approach of this thesis draws on a systematic literature review, qualitative empirical research as well as the design science research paradigm. We conducted a systematic literature search on data-driven business models and enterprise architecture. Considering the novelty of data-driven business models for academia and practice, we conducted explorative qualitative research to explain “why” and “how” companies embark on realizing data-driven business models. Throughout these studies, the primary data source was semi-structured interviews. In order to provide an artifact for DDBM innovation, we developed a theory for design and action. The data-driven business model innovation artifact was inductively developed in two design iterations based on the design science paradigm and the design science research framework.
Contribution: This thesis provides several contributions to theory and practice. We identified a clear gap in previous research efforts and derived 42 data-driven business model-related EA concerns. In order to address the identified literature gap, we provide empirical evidence for data-driven business model innovation. Four pathways of data-driven business model design and realization were identified. Along these pathways, an overview of EA application areas was derived from the empirical and theoretical findings. With the aim of supporting practitioners in data-driven business model innovation, this thesis was concerned with the development of a reference model. The reference model for data-driven business model innovation provides a broad view and applies enterprise architecture, where appropriate. This thesis provides five recommendations for practitioners realizing data-driven business models that address the demand for support in data-driven business model innovation.
Limitations: Several limitations must be considered. We acknowledge the threat to validity based on the fact that the thesis was written over the span of two years. As DDBMs are an emerging phenomenon in the literature, our thoughts on the underlying concepts have also evolved. Our ideas evolved to include a wider range of literature, different terminology, and a broader empirical foundation. We have gathered and analyzed the extended literature on EA and DDBM interconnectivity. However, the selection of keywords restricts the set of results. The data stem from a limited number of organizations and industries; thus, our conceptual developments need further testing to ensure generalizability.
Future Research: This thesis suggests several fruitful research avenues. Complementing the current concepts with additional data and quantitative research methods could address the existing threats to validity. A deeper understanding of data-driven business model innovation pathways, in the light of the detailed methods per pathway, would enhance the knowledge on this topic. Future research could focus on conducting additional design cycles for the data-driven business model innovation reference model. It would be interesting to enrich the findings of this thesis with quantitative data on correlations in data-driven business model innovation and enterprise architecture support. Furthermore, investigating a single case study and exploring new application fields of enterprise architecture in the data-driven business model innovation context would benefit research and practice would benefit.
This dissertation presents an analysis of the relations to self and technology that emerge from and in the use of self-tracking technologies. The ethnographical study, combined with the Grounded Theory approach and a media analysis, demonstrates the complex intertwining or duality of control and care towards oneself that emerge or become possible in and through the application of ST technologies. ST devices assist in strengthening one's health and well-being in a playful way, building and maintaining a positive self-feeling, self-image and agency, and discovering unknown abilities and potentials within oneself. The ST technologies used provide orientation through complexity-reducing visualizations, highlighting patterns, and trend progression. They challenge through self-overload, dissatisfaction when not achieving goals, self-deception and distraction, narcissism and even loss of control - internally through compulsion to control as well as externally through loss of data otection and exploitation of private data by third parties, as well as handing over responsibility (in the form of decisions) to technology (algorithms) instead of self-responsibility. These two seemingly opposed yet concurrently existing self-relations reflect the dynamic between today's demands for self-responsibility (in health and performance terms) and the need for self-care and guidance for the many relevant, sometimes daily, decisions. They balance possibly existing tensions and ambiguities between the modes of self-relations that at first glance seem to be opposed and yet ultimately are jointly oriented towards the same goal, namely to master one's life (life maintenance) and to be in balance. The self-relations described in this thesis are supported, reinforced, or enabled by ST technology (and practice). Three different roles that ST technology can take in self-care and self-control were elaborated: technology as a means, a counterpart, and a promise. In relation to technology, another dialectic is visible, which shows the apparent contrast between its conception as a tool and means to achieve something and the approach to technology as an intimate counterpart (partner, nanny, coach) and a promise of salvation. The relationship with technology seems to intensify in and through the ST experience and takes on or is assigned a partner-like role by the users. Finally, the results indicate that the concept of (self-)optimization, contrary to its etymological meaning of a logic of increase, can also be understood differently, namely balancing. In this context, optimization does not necessarily mean the fastest, the highest, the strongest, but something that is achievable and satisfactory for the self - within the framework of the given and the desired. At the same time, the optimization understood as harmonizing and balancing in self-tracking becomes a lifelong task that, in principle, can never be completed because with the addition of new vital areas in life and throughout a lifetime also the individually understood and conceived balance often shifts.
Assessment of forest functionality and the effectiveness of forest management and certification
(2021)
Forest ecosystems are complex systems that develop inherent structures and processes relevant for their functioning and the provisioning of ecosystem services that contribute to human wellbeing. So far, forest management focused on timber production while other services were less rewarded. With increasing climate change impacts, especially regulating ecosystem services such as microclimate regulation are ever more relevant to maintain forest functions and services. A key question is how forest management supports or undermines the ecosystems’ capacity to maintain those functions and services. Forest management implies silvicultural interventions such as thinning and timber harvesting and ranges from single tree extraction to large clearcuts as well as forest reserves without active forest operations and shape the character of forest ecosystems (e.g. natural versus planted forests). Artificial plantings, monocultures and management for economic timber production simplify forest structures and impair ecosystem resilience, resistance and the existence of forests but also the services essential for the prosperity and health of humanity. Efforts to reduce the negative impacts and attempts to safeguard forest functions are manifold and include compulsory national and international guidelines and regulations for forest management, conventions, but also voluntary mechanisms such as certification systems.
The main objective of this thesis was the development of a concept to assess the functionality of forests and to evaluate the effectiveness of forest ecosystem management including certification. An ecosystem-based and participatory methodology, named ECOSEFFECT, was developed. The method comprises a theoretical and an empirical plausibility analysis. It was applied to the Russian National FSC Standard in the Arkhangelsk Region of the Russian Federation – where boreal forests are exploited to meet Europe's demand for timber. In addition, the influence of forestry interventions on temperature regulation in Scots pine and European beech forests in Germany was assessed during two extreme hot and dry years in 2018 and 2019.
Microclimate regulation is a suitable proxy for forest functionality and can be applied easily to evaluate the effectiveness of forest management in safeguarding regulating forest functions relevant under climate change. Microclimate represents the most decisive factor differentiating clearcuts and primary forests. Thus, the assessment of forest microclimate regulation serves as convenient tool to illustrate forest functionality. In the boreal and temperate forests studied in the frame of this thesis, timber harvesting reduced the capacity to self-regulate forests’ microclimate and thus impair a crucial part of ecosystem functionality. Changes in structural forest characteristics influenced by forest management and silviculture significantly affect microclimatic conditions and therefore forest ecosystems’ vulnerability to climate change. Canopy coverage and the number of cut trees were most relevant for cooling maximum summer temperature in pine and beech forests in northern Germany. Maximum temperature measured at ground level increased by 0.21 – 0.34 K when 100 trees were cut. Opening the forest canopy by 10 % caused an increase of maximum temperature at ground-level by 0.53 K (including pine and beech stands). Relative temperature cooling capacity decreased with increasing wood harvest activities and dropped below average values when more than 656 trees per hectare (in 2018; and 867 trees in 2019) were felled. In pine stands with a canopy cover below 82 % the relative temperature buffering capacity was lower than the average. Mean maximum temperature measured at ground-level and in 1.3 m was highest in a pine-dominated sample plots with relatively low stand volume (177 m3 ha-1) and 9 K lower in a sample plot with relatively high stock volumes of F. sylvatica (> 565 m3 ha-1). During the hottest day in 2019, the difference in temperature peaks was more than 13 K for pine-dominated sample plots with relatively dense (72 %) and low (46 %) canopy cover.
The Russian FSC standard has the potential to improve forest management and ecological outcomes, but there are shortcomings in the precision of targeting actual problems and ecological commitment. In theory, FSC would transform forest management practices and induce positive changes and effective outcomes by addressing 75 % of the identified contributing factors including highly relevant factors and threats including large-scale (temporary) tree cover loss, which contributes to reducing about half of the identified stresses in the ecosystem. It is theoretically plausible that FSC prevents logging in high conservation value forests and intact forest landscapes, reduces the size and number of clearcuts, and prevents hydrological changes in the landscape. However, the standard was not sufficiently explicit and compulsory to generate a strong and positive influence on the identified problems and their drivers. Moreover, spatial data revealed, that the typical regular clearcut patterns of conventional timber harvesting continue to progress into the FSC-certified boreal forests, also if declared as ‘Intact Forest Landscape’. This results in the need to verify the assumptions and postulates on the ground as it remains unclear and questionable if functions and services of boreal forests are maintained when FSC-certified clearcutting continues. On the clearcuts, maximum temperature exceeded 36 °C and stayed below 30 °C in the closed primary forest. The number of days with temperatures above 25 °C at least doubled on clearcuts. Temperature cooling capacity was reduced by up to 14 % and temperature buffering capacity up to 60 %. The main reason why FSC-certified clearcuts do not differ from conventional clearcuts is that about 97 % of trees within equally large clearcut sites of up to 50 ha were removed. The spatial design of clearcuts, their size and the intensity of clearing as well as the density of skidding trails for timber extraction was not positively influenced by FSC-certification. Annual tree cover loss was lowest in non-certified areas. This means, that FSC may even contribute to an increased biomass removal within the clearcuts, which compromises the ecosystems’ capacity to recover and maintain ecological functions and services. The analysis of satellite-based data on tree cover loss showed that clearcutting causes secondary dieback in the surrounding of the cleared area. FSC-certification does not prevent the various negative impacts of clearcutting and thus fails to safeguard ecosystem functions. The postulated success in reducing identified environmental threats and stresses, e. g. through a smaller size of clearcuts, could not be verified on site. The empirical assessment does not support the hypothesis of effective improvements in the ecosystem. In practice, FSC-certification did not contribute to change clearcutting practices sufficiently to effectively improve the ecological performance. Sustainability standards that are unable to translate principles into effective outcomes fail in meeting the intended objectives of safeguarding ecosystem functioning. Clearcuts that carry sustainability labels are ecologically problematic and ineffective for the intended purpose of ecological sustainability.
The overexploitation of provisioning services, i.e. timber extraction, diminishes the ecosystems’ capacity to maintain other services of global significance. It also impairs ecosystem functions relevant to cope with and adapt to other stresses and disturbances that are rapidly increasing under climate change.
Forest management under climate change needs to apply precautionary principles and reduce further ecological risks such as secondary dieback and deterioration of regulating services that are relevant for the functioning of forests. Forest managers have to avoid ecological disimprovements by applying strict ecological principles with effective outcomes in order to maintain functional forests that regulate their own microclimate also as a basis for sustainable economic benefits.
Mental health is an important factor in an individuals’ life - more than 300 million individuals suffered from depression in 2015. Online-based interventions have been developed for the treatment of various mental disorders. These types of interventions
have proven their efficacy and can lead to positive outcomes for suffering patients. During these interventions, a large amount of patient-specific data is gathered that can be utilized to increase treatment outcomes by informing decision-making processes of psychotherapists, experts in the field, and patients.
The articles included in this dissertation focus on the analysis of such data collected in digital psychological treatments by using machine learning approaches. This dissertation utilizes various machine learning methods such as Bayesian models, regularization techniques, or decision trees to predict different psychological factors, such as mood or self-esteem, dropout of patients, or treatment outcomes and costs. These models are evaluated using a variety of performance metrics, for example, receiver operating characteristics curve, root mean square error, or specialized performance metrics for Bayesian inference. These types of analyses can support decision- making for psychologists and patients, which can, in turn, lead to better recommendations and subsequently to increased outcomes for patients and simultaneously more insight about the interplay between psychological factors. The contribution of this interdisciplinary dissertation is manifold and can be classified at the intersection of Information Systems, health economics, and psychology. The analysis of user journey data has not yet been fully examined in the field of psychological research. A process for this endeavor is developed and a technical implementation is provided for the research community. The application of machine learning in this context is still in its infancy. Thus, another contribution is the exploration and application of machine learning techniques for the revelation of correlations between psychological factors or characteristics and treatment outcomes as well as their prediction. Additionally, economic factors are predicted to develop a process for treatment type recommendations. This approach can be utilized for finding the optimal treatment type for patients on an individual level considering predicted treatment outcomes and costs. By evaluating the predictive accuracy of multiple machine learning techniques based on various performance metrics, the importance of considering heterogeneity among patients’ behavior and affect is highlighted in some articles. Furthermore, the potential of machine learning-based decision support systems in clinical practice has been examined from a psychotherapists’ point of view.
This dissertation focused on the nature and role of organizational practices for the employment of older people and the extension of their working lives. The set of four articles is driven by the objective to further deepen our understanding of how organizations can facilitate ageing at work to the benefit of both, employees and employers. Findings are empirically based on qualitative expert interview data from Germany and the U.S. and several quantitative field studies among older employees in Germany. To bridge gaps in measurement of organizational practices related to aging at work, this dissertation proposes a new comprehensive, multifaceted, and thoroughly conceptualized measure of organizational practices related to aging at work, the Later Life Workplace Index (LLWI). Through the course of the four articles the LLWI is conceptually developed based on qualitative interview data, operationalized, validated based on multiple field studies among older workers, and applied in a multi-level study among older employees of 101 organizations. Results suggest that organizational practices are not uniform, but multifaceted in their presence within organizations and their effects for the employment of older workers. The LLWI distinguishes nine domains of practices including an age-friendly organizational climate, work design, individual development, and practices tailoring the retirement transition. Thus, it may lay the foundation for more granular organizational level research in the field. Further, this dissertation’s fourth article applies the LLWI and argues based on person-environment fit and socio-emotional selectivity theory that organizational practices address different individual needs and, thus, affect employment depending on employees’ individual characteristics. Results suggest that older employees’ retirement intentions are effected by individual development, transition-to-retirement, and continued employment practices depending on their health resources. Application of the new measure in practice to improve organizations’ response to the aging workforce and opportunities for future research based on the LLWI are discussed.
Detecting and assessing road damages for autonomous driving utilizing conventional vehicle sensors
(2021)
Environmental perception is one of the biggest challenges in autonomous driving to move inside complex traffic situations properly. Perceiving the road's condition is necessary to calculate the drivable space; in manual driving, this is realized by the human visual cortex. Enabling the vehicle to detect road conditions is a critical and complex task from many perspectives. The complexity lies on the one hand in the development of tools for detecting damage, ideally using sensors already installed in the vehicle, and on the other hand, in integrating detected damages into the autonomous driving task and thus into the subsystems of autonomous driving. High-Definition Feature Maps, for instance, should be prepared for mapping road damages, which includes online and in-vehicle implementation. Furthermore, the motion planning system should react based on the detected damages to increase driving comfort and safety actively. Road damage detection is essential, especially in areas with poor infrastructure, and should be integrated as early as possible to enable even less developed countries to reap the benefits of autonomous driving systems. Besides the application in autonomous driving, an up-to-date solution on assessing road conditions is likewise desirable for the infrastructure planning of municipalities and federal states to make optimal use of the limited resources available for maintaining infrastructure quality. Addressing the challenges mentioned above, the research approach of this work is pragmatic and problem-solving. In designing technical solutions for road damage detection, we conduct applied research methods in engineering, including modeling, prototyping, and field studies. We utilize design science research to integrate road damages in an end-to-end concept for autonomous driving while drawing on previous knowledge, the application domain requirements, and expert workshops. This thesis provides various contributions to theory and practice. We design two individual solutions to assess road conditions with existing vehicle sensor technology. The first solution is based on calculating the quarter-vehicle model utilizing the vehicle level sensor and an acceleration sensor. The novel model-based calculation measures the road elevation under the tires, enabling common vehicles to assess road conditions with standard hardware. The second solution utilizes images from front-facing vehicle cameras to detect road damages with deep neural networks. Despite other research in this area, our algorithms are designed to be applicable on edge devices in autonomous vehicles with limited computational resources while still delivering cutting-edge performance. In addition, our analyses of deep learning tools and the introduction of new data into training provide valuable opportunities for researchers in other application areas to develop deep learning algorithms to optimize detection performance and runtime. Besides detecting road damages, we provide novel algorithms for classifying the severity of road damages to deliver additional information for improved motion planning. Alongside the technical solutions, we address the lack of an end-to-end solution for road damages in autonomous driving by providing a concept that starts from data generation and ends with servicing the vehicle motion planning. This includes solutions for detecting road damages, assessing their severity, aggregating the data in the vehicle and a cloud platform, and making the data available via that platform to other vehicles. Fundamental limitations in this dissertation are due to boundaries in modeling. Our pragmatic approach simplifies reality, which always distorts the degree of truth in the result. This affects the model building of the quarter-vehicle and deep learning. Further limitations occur in the end-to-end concept. This represents the integration of road damages in the autonomous driving task but does not detail the aggregation modules and interfaces of the subsystems. The completion of this work does not conclude the topic of road damage detection and assessment in autonomous driving. Research must continue to optimize the proposed solutions and test them on a widespread basis in the real world. Furthermore, the sensor fusion of different approaches is fascinating in order to combine the advantages of individual systems. Integrating the end-to-end concept into the ecosystem of an autonomous vehicle is another fascinating field, taking interfaces and cloud platforms into account.
This cumulative dissertation investigates food policy councils (FPCs) as potential levers for sustainability transformation. The four research papers included here on this recent phenomenon in Germany present new insights regarding the process of FPCs’ emergence (Emergence paper), the legal conditions which affect their establishment (Legal paper), the different roles of FPCs in policy-making processes (Roles paper) and FPCs’ potential to democratise the food system (Food democracy paper).
Drawing on and contextualizing the results of the four individual studies, the framework paper uses the leverage points concept originally developed by Meadows (1999) and adopted by Abson et al. (2016) as a lens to discuss FPCs’ potential as levers for sustainability transformation. This conceptual background includes three so-called realms of leverage, which are considered to be of particular importance in transformational, solution-oriented sustainability science: first, the change, stability and learning in institutions (re-structure), second, the interactions between people and nature (re-connect) and third, the ways in which knowledge is produced and used (re-think). Framing the findings of the four research papers in terms of these three realms, the framework paper shows that FPCs could serve as cross realm levers, i.e. as interventions that simultaneously address knowledge production, institutional reform and human-nature interactions.
This dissertation, entitled “How to Embed Sustainability in the Core of Higher Education Institutions: Drivers of, Barriers to, & Patterns behind the Implementation Processes of Sustainability Curricula – Insights from a Quantitative Meta-Study with Data from around the Globe,” addresses the question of how sustainability curricula1 can be implemented and established in higher education institutions2. This research question is based on the assumption that sustainable development requires new ways of thinking and acting in the world. Accordingly, universities – as hubs for knowledge generation, innovation, and education – provide a central leverage point for sustainably developing society at large. Therefore, the institutionalization of sustainability curricula is not only socially demanded, but also stipulated in numerous political statements from the international community (e.g., those of the UN and UNESCO) and operationalized via Sustainable Development Goal No. 4: “Quality Education”. Previous findings on how such implementation can be successful and what factors support or inhibit the process have come primarily through case studies of individual higher education institutions. These studies provide important insights but have been largely descriptive rather than analytical and leave open questions about the generalizability of their findings – for example, the extent to which other universities can be guided by the experiences of the respective higher education institutions. The present dissertation addresses this research gap. Through a meta-study (i.e., an analytical comparison of existing case studies), generalizable findings on the implementation processes of sustainability curricula are explored. In the first step, a case universe was collected in order to provide a database for deeper analyses. In two further analysis steps that built on the case universe from Step 1, certain factors that promote or inhibit the implementation of sustainability curricula (Step 2) and specific implementation patterns (Step 3) were examined. The following paragraphs provide greater details and an overview of the respective findings. In the first step, a database of peer-reviewed English-language case studies from around the globe that report on such processes was created. A total of 230 case studies were identified, 133 of which focus on the implementation processes of sustainability curricula.3 A bibliometric analysis of the 230 case studies revealed that this field of research is growing, although the discourse is primarily dominated by authors from North America, Europe, Oceania, and Asia, with South America and Africa being underrepresented. In addition, a citation analysis demonstrated that some universities incorporate findings from other countries whereas other universities act in isolation. This observation leaves open the question of the extent to which universities learn from one another in order to advance the implementation of sustainability curricula. In the second step of the analysis, the qualitative data of the collected case studies (sample of 133 case studies) were compared using the case survey method, which is a specific type of a meta-1 Sustainability curricula include courses, programs, and certificates from all fields of study that deal in some form with sustainability topics. For a more-detailed discussion of what education for sustainable development (ESD) entails, see Section Error! Reference source not found..
2 Higher education institutions (HEIs) include universities, universities of applied sciences, and other institutions that offer at least a bachelor’s degree.
3 A detailed explanation of the case sample and subsamples can be found in Section Error! Reference source not found..
analysis. The focus of the comparison lay on the drivers of and barriers to the processes of sustainability curriculum implementation at higher education institutions. Driving- and inhibiting factors have been thoroughly examined theoretically in the discourse on education for sustainable development (ESD), especially those pertaining to higher education institutions. However, no large body of data has yet been created to empirically test these hypotheses. The present meta-study found that the following factors lead to the deep-rooted and comprehensive establishment of sustainability curricula: strong leadership support; the establishment of sustainability curricula in the areas of education, research, campus operations, and outreach activities; formal participation of internal (including students) and external stakeholders; and engagement by sustainability champions (change agents), who are often the first to implement sustainability curricula and can face strong resistance. Other enabling factors include strategic planning, coordination, communication, having a vision, external political influence, the presence of a window of opportunity (e.g., an environmental disaster, a change in presidency), and the availability of interdisciplinary meeting spaces. On the other hand, the strongest cited barriers to the implantation of sustainability curricula were found to be the lack of interdisciplinary meeting spaces, the lack of a vision, the lack of incentives, the lack of resources, an overly full curriculum, and an unsupportive / overly bureaucratic organizational structure. The third step of the analysis also built on data from the 133 case studies and explored whether certain types or patterns of implementation processes occur. Through the analysis, six implementation patterns were identified that share similar driving- and inhibiting factors. The respective interplay between factors leads to various degrees of sustainability curriculum implementation in terms of how deeply rooted and comprehensive this implementation is. As discussed in greater detail below, in descending order of the level of achieved deep-rooted change, these patterns are (1) a collaborative paradigm shift, (2) bottom-up institutional change, (3) top-down institutional change, (4) the presence of many barriers that hinder institutional change, (5) externally driven initiatives, and (6) initiatives that are scattered due to a lack of coordination. Across all patterns, two phases could be identified: First, the impetus to implement ESD may be initiated not only by internal actors, but also by external ones. This initiation can take hold from the “bottom-up” (i.e., by students or faculty), from the “top-down” (i.e., at the presidential level), or in both directions simultaneously. The following key factors appear to be important in driving the initial implementation forward: a culture of open communication between all stakeholders in which feedback and reflection are welcome and even actively solicited, the development of a shared understanding and vision that further create a sense of ownership and long-term success, a high level of collaboration among all stakeholders, and existing initiatives that lead to knowledge sharing and other resources. In this regard, informal collaboration and cooperation can partially compensate for a lack of presidential-level support and/or a formal communication structure. Furthermore, developing a strategy with individual steps and shared responsibility leads to more-successful implementation of ESD at higher education institutions. The presented findings add a complementary empirical perspective to the discourse on the establishment of ESD at higher education institutions. First, the case studies that specifically address the implementation processes of sustainability curricula are reviewed and analyzed here for the first time as part of a research landscape. This research landscape reveals where research on such implementation processes has been or is being conducted. On this basis, both researchers and funders can reflect on the status quo and plan further research- or funding endeavors. Second, this dissertation offers the opportunity to compare a multitude of individual case studies and thus to develop new and generalizable insights into the implementation of sustainability curricula. The empirical analysis uses 133 case studies to identify key factors that promote or inhibit the implementation of sustainability curricula and to add a complementary perspective to the discourse, which has thus far been dominated by theoretical considerations and individual case studies. The analysis thereby offers a new perspective on generalizable influencing factors that appear to be important across different contexts. Thus far, specific patterns of implementation processes have been infrequently studied, and with few datasets. This dissertation analyzes the complex interplay between over 100 variables and provides one of the first research attempts at better understanding the processes that lead to the deep-rooted and comprehensive implementation of sustainability curricula. Internal and external practitioners of higher education institutions can find examples and evidence that can be useful in planning the next steps of their sustainability curriculum implementation. In the future, higher education institutions will play an even greater role in the journey toward sustainable development. This dissertation offers generalizable empirical findings on how universities can succeed in recognizing their own responsibility to that end and in realizing this transformation through the implementation of ESD.
TIME for REFL-ACTION: Interpersonal competence development in projectbased sustainability courses
(2021)
This dissertation investigates interpersonal competence development in project-based sustainability courses. Visions of a sustainable, safe, and just future cannot be reached by one individual alone. Thus, future change agents need to be able to collaborate and engage with stakeholders, to approach the manifold crises, challenges, problems, and conflicts we are facing together, and to promote and push forward sustainability transitions and transformations. Therefore, this research investigates three project-based sustainability graduate courses by comparing and contrasting teaching and learning outcomes, processes, and environments. A comparative case study approach using a Grounded Theory-inspired research design which triangulates several qualitative methods and perspectives is applied to allow for generalizable insights. Thereby, this dissertation provides empirically-informed insights which are further discussed in relation to selected teaching and learning theories. This leads, first, to a discussion of practical implications within (and beyond) sustainability higher education; and second, provides a theoretical foundation for interpersonal competence development in project-based learning settings – so that educating future change agents can gain momentum.
Findings of this research show that embracing conflicts when they occur (i.e. before they provoke cascading effects in the form of further conflicts down-the-road) is an effective strategy to help further develop interpersonal competence. This requires a conflict-embracing attitude. Attitude, in general, seems to be key in interpersonal competence and competence development overall. Self-reflection, if not explicitly required by outside influences (such as instructors), arises naturally from a self-reflective attitude, and is shown to provide the basis for developing interpersonal competence. This research introduces the term ‘Refl-Action’ which stresses the importance of pairing ‘learning by doing’ (as is often the focus in project-based learning settings) with conscious moments of ‘reflecting about the doing’.
More specifically, the research presented here identified four learning processes for interpersonal competence development: receiving input, experiencing, reflecting, and experimenting. Based on the empirical data, when the four processes are purposefully combined, following a meaningful sequence attitudes, knowledge, and skills in collaborative teamwork and impactful stakeholder engagement, are fostered (two facets of interpersonal competence). Each of the four learning processes is set in motion through various interactions students engage in during project-based sustainability courses: student-student (labeled ‘peer’), student-instructor (labeled ‘deliberate’), student-stakeholder (labeled ‘professional’), and student-mentor (labeled ‘supportive’) interactions. When these interactions are made explicit subjects of inquiry – i.e. the (inter-)action is linked with (self-)reflection – different learning processes complement one another: Interpersonal competence facets (collaborative teamwork and impactful stakeholder engagement) and domains (attitudes, knowledge, skills) are fostered. While, overall, interactions, processes, and conflicts have been identified as supportive for interpersonal competence development, trust has emerged as another variable inviting further investigation.
The findings of this thesis can be useful not only to support more conscious course design and facilitation, but should also be taken into consideration in other project-based (sustainability) settings. Both, sustainability novices and experts are regularly required to engage in teams and with stakeholders. Applying a conflict-embracing and self-reflective attitude allows to actively deal with differences encountered where diverse people interact, and to move forward on sustainability problems and visions in collaboration.