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New-institutional explanations for energy transition in Iran: investigating the interplays between institutions, actors, and technology
No full textThis paper explores the limited success of energy system transitions in Iran by analysing the interactions between renewable energy institutions, organisations, and individuals. While most literature focuses on successful sustainability transitions, little attention is given to failures, particularly in fossil-rich developing nations like Iran, which face unique socio-economic and political challenges in diversifying energy sources. Using a grounded theory approach, this study draws on qualitative interviews with public organisations, renewable energy investors, and policy document analysis. Findings reveal key barriers, including weakened credibility of renewable energy policies, lack of sustainability recognition among policymakers, and the absence of adaptive business models promoting autonomy and flexibility. Additionally, Iran’s energy security crisis is examined as both a risk to political and social stability and a potential catalyst for shifting perspectives toward sustainability. The study highlights the urgent need for governance reforms, inclusive policy frameworks, and a deeper understanding of the societal aspects of energy transitions.TU Berlin, Open-Access-Mittel – 202
Comparison of urban overland flow dynamics on sealed and partially permeable surfaces across a European transect
No full textThis study has analysed the extent to which overland flow dynamics, simulated with sub-hourly rainfall data for two scenarios of fully sealed and partially permeable urban streets, varies for 10 European cities across the Cfb climate zone. The cities showed similar behaviour in terms of frequency, total, peak flow and seasonality, with the exception of the western end of the Cfb transect. Introducing SUDS such as the partially permeable scenario reduced medium and large events in all cities; however; they were more pronounced in the central and north-eastern cities of transect. Long dry spells followed by large, flashier runoff events are prominent in some cities, increasing surface-water issues from diffuse pollution due to prolonged pollutant accumulation. The effects of temporal scaling of sub-hourly and hourly rainfall data on overland flow and the usability of ERA rain data were assessed to enable a coherent urban overland flow analysis relevant for pollution transport.TU Berlin, Open-Access-Mittel – 2025EC/HE/101060922/Ensuring high water quality/WATERU
Evaluation of a sign language avatar on comprehensibility, user experience & acceptability
No full textThis paper presents an investigation into the impact of adding adjustment features to an existing sign language (SL) avatar on a Microsoft Hololens 2 device. Through a detailed analysis of interactions of expert German Sign Language (DGS) users with both adjustable and non-adjustable avatars in a specific use case, this study identifies the key factors influencing the comprehensibility, the user experience (UX), and the acceptability of such a system. Despite user preference for adjustable settings, no significant improvements in UX or comprehensibility were observed, which remained at low levels, amid missing SL elements (mouthings and facial expressions) and implementation issues (indistinct hand shapes, lack of feedback and menu positioning). Hedonic quality was rated higher than pragmatic quality, indicating that users found the system more emotionally or aesthetically pleasing than functionally useful. Stress levels were higher for the adjustable avatar, reflecting lower performance, greater effort and more frustration. Additionally, concerns were raised about whether the Hololens adjustment gestures are intuitive and easy to familiarise oneself with. While acceptability of the concept of adjustability was generally positive, it was strongly dependent on usability and animation quality. This study highlights that personalisation alone is insufficient, and that SL avatars must be comprehensible by default. Key recommendations include enhancing mouthing and facial animation, improving interaction interfaces, and applying participatory design.TU Berlin, Open-Access-Mittel – 2025EC/HE/101070631/Transforming language translations and interpretations/UTTE
Optimised integral foam structures through thermoplastic foam injection moulding of radiation-crosslinked recyclates
No full textIn der vorliegenden Arbeit wurde ein Verfahrenskonzept zur Herstellung von geschäumten Thermoplast-Anwendungen mit strahlenvernetzten Recyclatanteilen entwickelt und die dazu relevanten Parameter und Eigenschaften untersucht. Als Referenzen wurden Probeplatten aus Neuware, welche im unvernetzten und vernetzten Zustand vorlagen, im konventionellen Spritzgussverfahren hergestellt. Dazu wurde sowohl der Einfluss der Strahlenvernetzung als auch der Einfluss der strahlenvernetzten Recyclatpartikel auf den Prozess, auf die Struktur sowie auf ausgewählte Formteileigenschaften untersucht und bewertet.
Es zeigte sich, dass durch die Strahlenvernetzung von den untersuchten Probeplatten eine signifikante Verbesserung der Endteileigenschaften erreichbar ist. Trotz hoher Gewichtsreduktion konnten zum Beispiel bei den geschäumten Proben teilweise bessere Eigenschaften erreicht werden, als bei den kompakten Bauteilen. Durch eine zusätzliche Einbringung von strahlenvernetzten Recyclatpartikeln wurde allgemein eine Verbesserung insbesondere der mechanischen Eigenschaften und der Oberflächen-eigenschaften erreicht. Die Probekörper zeigten eine homogene und mikrozellulare Integralschaumstruktur, die u.a. auf den Nukleierungseffekt der zugefügten unaufschmelzbaren Recyclatpartikeln zurückzuführen ist. Verfahrens-technisch waren die Compounds ohne Probleme verarbeitbar, allerdings zeigte sich ein erhöhter Spritzdruckbedarf in Folge der Viskositätszunahme bei 50%-Recyclatanteil. Bei niedrigen Anteilen war der Spritzdruckanstieg geringer und er lag je nach Recyclatanteil sogar unter den Werten des Kompaktspritzgusses. Die Untersuchungen an geschäumten Probeplatten zeigten zudem, dass selbst bei hohen Recyclatanteilen von 50% keine weitere Reduzierung der Oberflächenqualität auftrat.
Zur Vorhersage der Untersuchungsergebnisse wurden modellhafte Ansätze des Leichtbaueffekts und der linearen Mischungsregel genutzt, welche als Ergebnisse valide Übereinstimmungen aufzeigten. Zudem wurde eine komplette Validierung und Optimierung der Prozessketten durchgeführt, sodass ein Endprodukt mit hohem Recyclatanteil bei höheren mechanischen und thermischen Eigenschaften hergestellt werden konnte. Zusätzlich wiesen die Bauteile eine hohe Oberflächenqualität bei gleichzeitig niedrigem Formteilgewicht verglichen mit einem unmodifiziertem Polypropylen auf. Eine Kostenanalyse zeigte ein hohes wirtschaftliches Potential und leistet somit einen wichtigen Beitrag zur Nachhaltigkeit durch Partikelrecycling.In the present work, a process concept for the production of foamed thermoplastic applications with radiation-crosslinked recyclates was developed and the relevant parameters and properties were investigated. As references, sample plates of virgin material, which were available in a non-crosslinked and crosslinked state, were produced using the conventional injection molding process. The influence of radiation crosslinking and the influence of the radiation-crosslinked recyclate particles on the process, on the structure and on selected molded part properties were investigated and evaluated.
It was shown that a significant improvement in the final part properties can be achieved through the radiation crosslinking of the tested test plates. Despite a high weight reduction, the foamed samples, for example, achieved better properties in some cases than the compact parts. An additional admixture of radiation-crosslinked recyclate particles generally improved the mechanical and surface properties in particular. The test specimens exhibited a homogeneous and microcellular foam structure, which is partly due to the nucleation effect of the added unmeltable recyclate particles, depending on the recyclate content. In terms of process technology, the compounds could be processed without any problems, except that an increased injection pressure occurred as a result of the increase in viscosity by 50% recycled content. At lower proportions, the increase in injection pressure was lower; depending on the proportion of recyclate, it was even below the values for compact injection molding part. The tests on the foamed test plates also showed that there was no further reduction in surface quality even with a high amount of recyclate.
Model approaches of the lightweight effect and the linear mixing rule were used to predict the test results, which showed good agreement. In addition, a complete validation and optimization of the process was carried out so that an end product with a high amount of recycled material with higher mechanical and thermal properties could be produced. In addition, the parts exhibited a high surface quality with a low part weight compared to an unmodified polypropylene. A cost analysis revealed high economic potential, thus making an important contribution to sustainability through particle recycling
a contribution to condition-based maintenance
No full textIn Kooperation mit dem Triebwerkshersteller Rolls-Royce Holdings plc. forscht die Technische Universität Berlin an Methoden zur Zustandsüberwachung und -vorhersage von Triebwerksbauteilen und insbesondere hydrodynamischen Gleitlagern. Diese Methoden sollen die Grundlage für eine zustandsorientierte und vorausschauende Instandhaltung bilden. Im Vergleich zur zeitbasierten Instandhaltung bieten sie ökonomische und ökologische Vorteile, indem sie eine optimierte Instandhaltung ermöglichen.
Der aktuelle Stand der Forschung zeigt einen zunehmenden Einsatz datengestützter Methoden des Machine Learnings, insbesondere des Deep Learnings, zur Zustandsüberwachung. Eine wesentliche Herausforderung ist die Generierung geeigneter Datensätze zum Training und zur Evaluation dieser Methoden. Bei Gleitlagern ist es beispielsweise nur unter großem Aufwand möglich, den Verschleißzustand während eines Versuchs direkt zu bestimmen. Zudem besteht Forschungsbedarf an genaueren und robusteren Methoden zur Verschleißüberwachung und -vorhersage unter Berücksichtigung externer Effekte.
Diese Arbeit untersucht den Einsatz von Methoden des Deep Learnings zur Zustandsüberwachung von Maschinenteilen am Beispiel hydrodynamischer Gleitlager. Der Schwerpunkt liegt auf der Körperschallanalyse zur Schätzung und Vorhersage des Verschleißvolumens. Zudem werden Anforderungen für eine praktische Anwendung abgeleitet.
Zur Generierung eines Datensatzes für das Training und die Evaluation von Methoden des Deep Learning wurden ein Gleitlagerprüfstand und Verfahren zur Verschleißvolumenschätzung entwickelt. Der temperaturgeregelte Gleitlagerprüfstand ermöglicht eine gezielte Einstellung eines Betriebspunkts sowie eine Aufzeichnung von Körperschallsignalen und Betriebsgrößen während eines Versuchs. Das Verfahren zur Verschleißvolumenschätzung basiert auf der Erfassung von Rundheitsprofilen vor und nach einem Versuch. Durch Differenzbildung der Rundheitsprofile kann das Verschleißvolumen geschätzt werden. Eine Unsicherheitsanalyse zeigt die Grenzen und das Potenzial des entwickelten Verfahrens auf, was eine Grundlage für zukünftige Verbesserungen bildet.
Mit dem entwickelten Datensatz wurde der Einsatz von Methoden des Machine Learning und Deep Learning zur Verschleißvolumenschätzung evaluiert. Genauer wurden der Einsatz von linearer Regression, Random Forest Regressor, mehrlagigen Perzeptronen und Gated Recurrent Units, einer Struktur eines rekurrenten neuronalen Netzes, untersucht. Die Ergebnisse zeigen, dass eine genaue Schätzung des Verschleißvolumens mit den verwendeten Methoden möglich ist. Dabei ist ein zentrales Ergebnis, dass sich die Methoden des Deep Learning bei geeigneter Datenlage besser für den praktischen Einsatz eignen, da sie auch mit Zeitreihen trainiert werden können. Somit ist es möglich, Daten zum Training zu nutzen, bei denen sich der Betriebspunkt über die Zeit verändert.
Für die Vorhersage des Verschleißvolumens wurden Methoden des Deep Learning eingesetzt und evaluiert. Die Verfügbarkeit geeigneter historischer Daten ist dabei entscheidend für die Genauigkeit der Vorhersagen. Es wurde eine Struktur eines neuronalen Netzes verwendet, welches die Vorhersage in mehreren Stufen generiert. Dabei wird zunächst der aktuelle Verschleißzustand ermittelt, auf dessen Basis die eigentliche Vorhersage des Verschleißvolumens durchgeführt wird. Die untersuchten Methoden ermöglichen eine präzise Vorhersage an einem Gleitlager unter Verwendung historischer Daten und einer Trajektorie des zukünftigen Betriebs.
Die entwickelten Methoden können die Grundlage einer vorausschauenden Instandhaltung bilden. Insbesondere in der Luftfahrtindustrie, wo hydrodynamische Gleitlager eine wichtige Rolle spielen, könnte der Einsatz dieser Technologien zu einer Erhöhung der Betriebssicherheit und zu einer Optimierung der Instandhaltung führen.In cooperation with the engine manufacturer Rolls-Royce Holdings plc., the Technical University of Berlin is researching methods for the condition monitoring and prediction of engine components, with a particular focus on hydrodynamic plain bearings. These methods are intended to form the basis for condition-based and predictive maintenance. Compared to time-based maintenance, they offer economic and ecological advantages by enabling optimized maintenance.
The current state of research shows an increasing use of data-driven methods of machine learning, particularly deep learning, for condition monitoring. A major challenge is the generation of suitable datasets for training and evaluating these methods.
In the case of journal bearings, for example, it is only possible to determine the state of wear directly during an experiment with great effort.
In addition, there is a need for research into more accurate and robust methods for wear monitoring and prediction, taking into account external effects.
This work investigates the use of deep learning methods for monitoring machine components condition, using hydrodynamic plain bearings as an example. The focus is on structure-borne sound analysis for estimating and predicting wear volume. Furthermore, requirements for practical implementation are derived.
To generate a dataset for training and evaluating deep learning methods, a plain bearing test rig and a method for estimating wear volume were developed. The temperature-controlled plain bearing test rig allows for precise adjustment of an operating point and recording of structure-borne sound signals and operational parameters during an experiment. The wear volume estimation method is based on the capture of roundness profiles before and after an experiment. By calculating the difference between the roundness profiles, the wear volume can be estimated. An uncertainty analysis reveals the limitations and potential of the developed method, providing a foundation for future improvements.
Using the developed dataset, the application of machine learning and deep learning methods was evaluated to estimate the volume of wear. Specifically, the use of linear regression, random forest regressor, multilayer perceptrons, and gated recurrent units (a type of recurrent neural network) was investigated. The results show that an accurate estimate of wear volume is possible with the methods employed. A key finding is that deep learning methods are better suited for practical use when appropriate data are available, as they can also be trained with time series data. This makes it possible to use training data collected under dynamic operating conditions.
To predict wear volume, deep learning methods were used and evaluated. The availability of suitable historical data is crucial for the accuracy of predictions. A neural network structure was used to generate the prediction in multiple stages. First, the current wear state is determined, which forms the basis for the actual prediction of the wear volume. The methods studied enable precise wear volume prediction for a plain bearing using historical data and a trajectory of future operations.
The developed methods can serve as a foundation for predictive maintenance. In particular in the aviation industry, where hydrodynamic plain bearings play a crucial role, the application of these technologies could increase operational safety and optimize maintenance
Interpretierbarkeitsbasierte Modelverbesserung mit dem Reveal2Revise Lebenszyklus
No full textMachine learning (ML) models are increasingly deployed in various safety-critical applications, e.g., as assistants in medical diagnostics for early melanoma detection. Despite their impressive predictive capabilities, these models often exploit spurious correlations present in the training data, as they are trained to detect any pattern in the data to solve the underlying task. For instance, ML models for medical imaging may base predictions on the existence of data artifacts, such as hospital tags, rather than clinically valid features, which can result in potentially dangerous mispredictions in high-stakes scenarios. The detection and correction of such unintended behavior pose significant challenges, as existing methods typically require extensive annotations or significant human supervision, which are often impractical to obtain in real-world applications. This thesis addresses these challenges by introducing the Reveal2Revise framework, an iterative and interpretability-driven model debugging life cycle designed to minimize the reliance on human intervention. The framework consists of four key steps: bias revealing, bias modeling, model revision, and evaluation. We further identify and address shortcomings in existing methods utilized for the first three steps. Specifically, we review and compare interpretability-driven approaches for detecting spurious model behavior and introduce semi-automated methods for bias annotation to reduce manual effort. Furthermore, we develop robust pattern-based concept activation vectors for precise bias modeling and propose a novel bias mitigation approach that penalizes spurious model behavior in the latent space. Our contributions enhance the reliability and trustworthiness of ML models, ultimately facilitating their responsible deployment in safety-critical applications.Modelle des Maschinellen Lernens (ML) werden zunehmend in sicherheitskritischen Anwendungen eingesetzt, beispielsweise zur Assistenz in der medizinischen Diagnostik zur Früherkennung von Melanomen. Trotz ihrer beeindruckenden Vorhersagefähigkeiten nutzen diese Modelle häufig zweifelhafte, nicht-kausale Korrelationen in den Trainingsdaten, da sie trainiert werden jegliche Muster in den Daten zur Lösung der vorliegenden Aufbabe zu erkennen. Zum Beispiel können ML Modelle in der medizinischen Bildverarbeitung Vorhersagen auf der Grundlage von Datenartefakten wie Krankenhauskennzeichen treffen, anstatt auf klinisch validen Merkmalen. Dies kann in risikobehafteten Szenarien zu potenziell gefährlichen Fehlvorhersagen führen. Die Erkennung und Korrektur solcher unerwünschten Verhaltensweisen stellen erhebliche Herausforderungen dar, da bestehende Methoden typischerweise umfangreiche Annotationen oder signifikante menschliche Aufsicht erfordern, was in der Praxis oft unmöglich ist. Diese Dissertation adressiert diese Herausforderungen, indem sie das Reveal2Revise Framework einführt, einen iterativen und auf Erklärbarkeitsmethoden basierenden Lebenszyklus zur Modellkorrektur, der darauf abzielt, die Abhängigkeit von menschlicher Aufsicht zu minimieren. Das Framework beinhaltet vier Schritte: Bias-Erkennung, Bias-Modellierung, Modellrevision und Evaluation. Darüber hinaus identifizieren und beheben wir Schwächen in Methoden für die ersten drei Schritte. Dabei überprüfen und vergleichen wir erklärbarkeitsbasierte Ansätze zur Erkennung fehlerhaften Modellverhaltens und führen semi-automatisierte Methoden zur Bias-Annotation ein, um die Annotationskosten erheblich zu reduzieren. Zudem entwickeln wir robuste Konzeptaktivierungsvektoren für eine präzise Bias-Modellierung und schlagen einen neuartigen Ansatz zur Modellrevision vor, der fehlerhaftes Modellverhalten in fortgeschrittenen Netzwerkrepräsentationen bestraft und somit das Verhalten der ML-Modelle der Erwartung von Experten anpasst. Unsere Beiträge steigern die Zuverlässigkeit und Vertrauenswürdigkeit von ML-Modellen und ermöglichen dadurch deren verantwortungsvollen Einsatz in sicherheitskritischen Anwendungen
Landslide-triggered tsunamis – a review
No full textRisk mitigation for landslide-triggered tsunamis (LTT) is impeded by high uncertainty regarding the location of triggering landslides and the expected wave heights. Hence, this review aims to comprehensively analyze the spatial distribution, landslide characteristics, generated wave heights, and impact on humans of 317 LTT published as a catalog in a data repository (Dohmen et al. 2025). A classification system for LTT is established based on the preparatory and triggering factors of the landslides: (1) earthquakes, (2) volcanic activity, (3) paraglacial conditions, (4) precipitation, (5) anthropogenic activities, and (6) unknown causes. LTT triggered by earthquakes and volcanic activity are the most frequent classes and account for the highest fatalities and greatest economic damage. The highest waves are generated in enclosed marine environments and inland waters, often caused by anthropogenic activities such as reservoir operations. To mitigate risks from LTT, it is essential to know the exact location of the triggering landslide before failure, which is not the case for most events. As data availability is limited for landslide characteristics—especially in submarine environments—there is a need for high-resolution bathymetric data to map and investigate tsunamigenic submarine landslides and link them to expected tsunami heights and potential impacts on coastal populations. Offshore landslide susceptibility mapping is therefore recommended as a promising approach for identifying potential LTT failure locations.TU Berlin, Open-Access-Mittel – 2025BMFTR, 03G0906E, Projekt CLIENT II - TsunamiRisk: Durch Vulkane und Hangrutschungen induzierte Tsunami
Investigating the effect of prior exposure and fidelity on quality and realism perception of VR digital twins
No full textThis study explores how prior exposure to physical objects influences the quality and realism perception of Digital Twins (DT) with varying levels of fidelity in Virtual Reality (VR). In a mixed experimental design, 24 participants were divided into two equal groups: an exposure group, in which members were shown physical objects before inspecting and rating their replicas in VR, and a control group without prior knowledge. Three objects were presented, each under four fidelity conditions with varying texture resolution and geometric detail. Participants rated perceived quality and realism through in-VR self-reports. Statistical analysis revealed that texture resolution significantly affected realism and quality perception, whereas geometric detail only influenced quality ratings. Investigating the between-factor, no significant effect of exposure on quality and realism perception was found. These findings raise important questions about the cognitive relationship between physical objects and their digital counterparts and how fidelity influences the perception of DTs in VR.TU Berlin, Open-Access-Mittel – 202
from smallholder constraints to collective actions with insights from the Highland communities
No full textPurpose
Scholars emphasize contextualizing entrepreneurship to gain deeper insights into entrepreneurial dynamics in various contexts (i.e., regions, sectors, and periods). The agriculture sector presents an interesting context for investigating entrepreneurship as it functions within particular socio-economic and environmental conditions. This sector holds immense potential to significantly impact the global GDP and employment, as well as contribute to the achievement of several SDGs. It is particularly crucial for the impoverished developing world, where the majority of the agricultural population resides in rural areas and is primarily associated with agriculture for their livelihoods. Despite its significance, the sector remains underdeveloped due to numerous challenges. As a result, the sector’s potential for employment generation, poverty alleviation, and food security remains unrealized. Agricultural entrepreneurship has the capacity to transform this scenario by introducing innovative solutions. It has become an important catalyst for rural development, poverty alleviation, and food security, particularly in developing countries. Therefore, it is imperative to understand the dynamics of the sector to help foster agricultural entrepreneurship. However, the mainstream entrepreneurship literature has overlooked the agriculture sector, and existing research has primarily focused on developed economies and urban settings. Conversely, there is limited empirical evidence that addresses the distinctive dynamics of agricultural entrepreneurship in developing countries, particularly focusing on marginalized smallholders in challenging rural contexts, such as the highland Himalayan region. This gap in the literature hinders the formulation of policies and programs that can effectively support agri-entrepreneurship within these regions. Therefore, heeding calls for contextualizing entrepreneurship, this thesis aims to investigate the dynamics of agricultural entrepreneurship, with a particular focus on smallholder constraints and determinants in developing countries and how these factors manifest in challenging rural environments such as the highland Himalayan context, as well as how community-based organizations such as the Village Agricultural Cooperatives (VACs) are instrumental in fostering agri-entrepreneurship thus improving livelihoods of marginalized rural communities. Using these interconnected studies, the thesis seeks to guide policymakers, development practitioners, and aspiring agricultural entrepreneurs to address challenges and foster agri-entrepreneurship to drive sustainable development, improve food security, and create economic opportunities for rural communities.
Literature Analysis
This thesis builds upon a growing body of literature on agricultural/ rural entrepreneurship, with a particular focus on constrained (rural/agricultural) entrepreneurship, cognitive/ behavioral aspects, and institutional and social capital dimensions. Despite growing interest among scholars in contextualizing entrepreneurship, the mainstream entrepreneurship literature has neglected the agriculture sector and rural contexts, particularly in the developing world. The smallholders, who represent the majority of the agricultural population, are often overlooked by entrepreneurship researchers and policymakers, especially in challenging rural contexts such as the unique highland Himalayan context. By contextualizing entrepreneurship studies, this thesis uses the sectoral and regional context approach. It focuses on the agriculture sector in developing countries with a particular focus on under-represented smallholders in the scarcely explored highland Himalayan context. Theories such as the entrepreneurial ecosystem, institutional theory, collective action, and social capital theory guide this research. The current body of literature lacks a comprehensive, agriculture-specific synthesis of constraints to smallholder entrepreneurship, particularly in developing countries. The extant literature on smallholders’ constraints is often scattered and fragmented, mainly focusing on individual barriers rather than providing a thorough comprehension of the multifaceted constraints to agri-entrepreneurship. Therefore, the first study conducts a systematic review of peer-reviewed literature to identify, categorize, and prioritize the constraints to agri-entrepreneurship, to provide a comprehensive overview of constraints faced by smallholders in their entrepreneurial endeavors in developing countries. This global perspective provides a theoretical foundation and identifies gaps in understanding region-specific challenges and effective enabling mechanisms. Moreover, the body of research on entrepreneurial behavior indicates that there is a lack of studies that concentrate on the agriculture sector and are mostly inclined towards sector-neutral, generic entrepreneurial behavior. The extant scholarship has paid little attention to early-stage entrepreneurial activity within the agriculture sector, particularly in the context of developing countries. Therefore, using the latest GEM dataset, the second study quantitatively analyzes the influence of cognitive and sociocultural factors on the propensity to participate in agri-entrepreneurship activities in developing countries. Furthermore, the rural settings, particularly the highland regions in developing countries such as the Himalayan region of Pakistan, are scarcely explored in the (agricultural) entrepreneurship literature. Therefore, the third study explores context-specific factors constraining marginalized smallholders from engaging in agri-entrepreneurship in the highland Himalayan context, whereas the fourth study analyses the role of community-based organizations such as VACs as an enabling mechanism to address marginalized smallholder constraints and foster agri-entrepreneurship through collective action in the under-explored highland Himalayan context.
Research Design
This thesis is structured into four interconnected studies. Article 1 uses the systematic literature review (SLR) methodology followed by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to synthesize global evidence on constraints to agri-entrepreneurship, particularly for smallholders in developing countries, identify themes, and research gaps. Using the GEM dataset (APS 2020), Article 2 quantitatively (using logit regression and correlation) analyzed the effects of cognitive and social factors on agri-entrepreneurship in developing countries. Article 3 employs a qualitative methodology, utilizing semi-structured interviews with key informants and thematic analysis using Gioia approach, to provide nuanced and contextualized insights into smallholder constraints in the highland Himalayan region. Article 4 uses a qualitative case-study approach to analyze how VACs mitigate smallholders’ constraints and stimulate entrepreneurship through semi-structured interviews with key informants, document reviews of VACs, and informal discussions with farmers in the highland Himalayan region, allowing for in-depth exploration of a potential solution. This mixed-method approach integrates global insights with in-depth regional analysis, creating triangulation and ensuring a thorough understanding of the constraints, determinants, and approaches to agri-entrepreneurship among smallholders in developing countries. The SLR provides a foundation, the qualitative study adds depth, and the case study illustrates strategies to mitigate those challenges, collectively giving a holistic understanding of the dynamics of agri-entrepreneurship in developing regions.
Results
This thesis contributes to advancing the understanding of agricultural entrepreneurship, particularly focusing on smallholders in developing countries. Article 1 synthesizes global evidence on smallholders’ constraints to agri-entrepreneurship in developing countries. This study systematically identified and classified smallholder constraints into various thematic categories, which include market-related challenges, financial constraints, human capital limitations, institutional factors, technological shortcomings, and infrastructural challenges. These constraints often intersect, exacerbating challenges and limiting entrepreneurial potential. The existing literature on smallholder constraints is usually scattered and fragmented; therefore, by categorizing and prioritizing, this study provides a comprehensive overview of smallholder constraints in the developing world. Article 2 investigates the factors (cognitive and social) that influence early-stage entrepreneurial activity in the agriculture sector in developing countries using two distinct models. Assessment of individual factors found self-efficacy as the sole significant predictor of agricultural entrepreneurship emphasizing the role of knowledge and skills in agricultural entrepreneurial decision-making, while when various elements of cognitive (opportunity perception, risk perception, and perceived self-efficacy) and social factors (role models, social status, media coverage, and desirable career choice) are combined, they significantly influence engagement in agricultural entrepreneurship, suggesting that both individual mindset and societal support systems are crucial in fostering agriculture entrepreneurship. Article 3 attempts to contextualize the constrained agri-entrepreneurship through an in-depth investigation focusing on the marginalized smallholders in the highland Himalayan context. Drawing on in-depth expert interviews and rigorous thematic analysis, this study revealed a deeply interconnected set of structural, sociocultural, spatial-ecological, and institutional constraints that shape the everyday realities of rural livelihoods in this geo-politically sensitive, socio-culturally conservative, and environmentally fragile context. The smallholder farmers in this region do not simply face a lack of access to resources; they contend with an entire ecosystem of exclusion, which includes market and value chain exclusion, sociocultural constraints, geographic and infrastructural limitations, climate-induced risks, institutional voids, political marginalization, and conflict exposure. Linking these factors to three overarching theoretical dimensions, such as socio-cultural-economic constraints, spatial-ecological constraints, and macro-structural constraints, the study offers both conceptual clarity and practical direction. Article 4 delves into the role of VACs as enablers of entrepreneurship among smallholders in the highland Himalayan region. The findings indicate that the VACs play a vital role in addressing smallholders’ constraints and stimulating agri-entrepreneurship. They facilitate smallholders through resource mobilization (access to finance, inputs, and local resources), capacity building (training and skill development, information and knowledge sharing), market integration (value addition, innovation, and market access), and social capital building (networking and collaborations, youth and women inclusion), thus fostering agri-entrepreneurship and contributing to the economic empowerment of marginalized rural communities.
Implications
This thesis contributes to the ongoing discourse on agricultural/ rural entrepreneurship. By applying the sectoral and regional context approach, this thesis delves into the realm of agri-entrepreneurship within developing countries, with a special emphasis on smallholders, adding to the limited but growing literature in this domain. Moreover, this thesis provides nuanced and context-specific insights by focusing on underrepresented subsistence-oriented and marginalized smallholders in the scarcely explored highland Himalayan context. This thesis integrates ecosystem, institutional, resource-based, spatial embeddedness, cognitive, and social capital perspectives to provide a multidimensional and comprehensive understanding of the constraints, determinants, and approaches to smallholder agricultural entrepreneurship.
This thesis underscores that fostering agri-entrepreneurship among smallholders requires integrated, community-centered strategies that address both structural, sociocultural, and behavioral dimensions. Beyond improving access to finance and infrastructure development, interventions must deliberately strengthen social capital through farmers’ groups and cooperatives that reduce isolation and facilitate knowledge exchange and resource sharing. The cumulative insights highlight that hybrid, adaptive, and socio-culturally embedded interventions are necessary to enable smallholders to transition from subsistence farming to resilient, market-oriented agricultural entrepreneurship.Agrarisches Unternehmertum birgt erhebliches Potenzial für die Armutsbekämpfung, Ernährungssicherung und nachhaltige Entwicklung. Da die etablierte Entrepreneurship-Forschung den Agrarsektor, ländliche Gebiete und marginalisierte Kleinbauern – insbesondere in herausfordernden Gebirgsregionen – weitgehend vernachlässigt hat, untersucht diese Dissertation die Dynamik des Sektors in Entwicklungsländern. Die Arbeit reagiert auf die Forderung nach einer stärkeren Kontextualisierung der Forschung und konzentriert sich auf Beschränkungen für Kleinbauern, individuelle Determinanten sowie kollektive Ermöglichungsmechanismen auf Basis empirischer Erkenntnisse aus der Hochgebirgsregion des pakistanischen Himalayas. Die Dissertation verwendet ein Mixed-Methods-Design, bestehend aus vier miteinander verknüpften Studien. Zunächst synthetisiert eine systematische Literaturübersicht die globale Evidenz zu Barrieren für Kleinbauern und kategorisiert dabei marktbezogene, finanzielle, humankapitalbezogene, institutionelle, technologische und infrastrukturelle Hindernisse. Darauf folgt eine quantitative Analyse, die den Einfluss kognitiver und soziokultureller Faktoren auf frühphasige agrarunternehmerische Aktivitäten untersucht und die zentrale Rolle der Selbstwirksamkeit sowie sozialer Unterstützungsmechanismen hervorhebt. Eine anschließende qualitative Studie kontextualisiert das Unternehmertum in der Himalaya-Region und deckt ein komplexes Ökosystem aus sozioökonomischen, räumlich-ökologischen und makrostrukturellen Sachzwängen auf. Abschließend demonstriert eine qualitative Fallstudie, wie landwirtschaftliche Dorfgenossenschaften diese Beschränkungen durch kollektives Handeln abmildern, indem sie den Ressourcenzugang verbessern, Human- und Sozialkapital aufbauen und die Marktintegration fördern. Theoretisch trägt die Dissertation zur Forschung bei, indem sie Ökosystem-, Institutions-, Kognitions- und Sozialkapitalperspektiven integriert, um ein multidimensionales Verständnis von Agri-Entrepreneurship in marginalisierten Kontexten zu schaffen. Empirisch liefert sie wertvolle Belege aus einer geopolitisch sensiblen und ökologisch fragilen Region. Praktisch unterstreichen die Ergebnisse die Notwendigkeit integrierter, gemeinschaftszentrierter Interventionen, um Kleinbauern den Übergang von der Subsistenzwirtschaft zu resilientem, marktorientiertem Unternehmertum zu ermöglichen
Die Stadt als Labor. Wie Do-it-yourself-Kulturen Wege transdisziplinären Lernens eröffnen
No full textMakerspaces, Gemeinschaftsgärten und Repair-Cafés verknüpfen akademisches, praktisches und erfahrungsbasiertes Wissen und fördern so Nachhaltigkeit, Demokratie und Selbstwirksamkeit. Ihr Erfolgsrezept sind flexible Bildungsstrukturen. Forschung und Lehre finden in diesen Strukturen der Stadt einen idealen Erprobungsraum, um transdisziplinäres Lernen zu etablieren