In digital knowledge work, flow promises not just productivity; it offers a pathway to well-being. Yet despite decades of flow research in HCI, we know little about how to design digital interventions that support it. In this work, we foreground lived interventions - everyday practices workers already use to foster flow - to uncover overlooked opportunities and chart new directions for digital intervention design. Specifically, we report findings from two studies: (1) a reflexive thematic analysis of open-ended survey responses (n = 160), surfacing 38 lived interventions across four categories: environment, organization, task shaping, and personal readiness; and (2) a quantitative online survey (n = 121) that validates this repertoire, identifies which interventions are broadly endorsed versus polarizing, and elicits visions of technological support. We contribute empirical insights into how digital workers cultivate flow, situate these lived interventions within existing literature, and derive design opportunities for future digital flow interventions.

Residential district heating demand response offers significant potential for enhancing system flexibility and supporting decarbonization, yet widespread implementation remains limited. While technical feasibility has been demonstrated in numerous studies, a systematic understanding of how different demand response approaches shape utility-customer interactions and implementation requirements remains fragmented. Through a systematic review of 26 studies, we analyze utility-customer interactions across five key dimensions: goals, thermal comfort, intelligence mechanisms, coordination strategies, and incentive structures and identify three distinct interaction patterns that emerge from different coordination approaches: direct utility control, dynamic price signals, and static price incentives. Our synthesis of quantitative evidence from field studies and simulations reveals characteristic performance outcomes and implementation requirements for each pattern. Direct utility control demonstrates reliable peak management capabilities of 5%–35% maximum power reduction, but requires substantial infrastructure investment and faces unresolved challenges in incentive design. Dynamic price signals enable customer systems to respond to variable production costs, with reported outcomes ranging from 4%–65% peak load reduction and 1%–53% cost savings depending on price elasticity and coordination approach, though purely decentralized implementations face risks of synchronized responses that can amplify rather than reduce peaks. Static price incentives achieve moderate, sustained peak reductions (10%–20%) with minimal infrastructure requirements but lack short-term flexibility. This systematic analysis provides utilities with evidence-based understanding of the operational implications, performance characteristics, and implementation challenges associated with each coordination approach.

Multi-agent deep reinforcement learning offers the possibility to analyze strategic bidding behavior of market participants in agent-based electricity market simulations. However, most implementations simplify the European day-ahead market by assuming hourly bidding instead of submitting $\mathbf{2 4}$-hour bids simultaneously, limiting the realism of learned strategies. This work investigates actor design choices to enable multi-step bidding in day-ahead markets, focusing on neural network architectures and action decoding. Feedforward neural networks, recurrent neural networks, and multi-head self-attention architectures, as well as three action decoding formulations (direct price, marginal cost factor, and stochastic marginal cost factor), are evaluated within an agent-based electricity market simulation framework across stylized test systems and a country-wide German scenario. Results show limited benefits from more complex actor architectures and increased computational overhead. While multi-step bidding is successfully learned in a single-agent setting, it does not translate to settings with multiple agents. This underlines remaining challenges for realistic multi-step bidding in electricity market simulations with multi-agent deep reinforcement learning.

Greenwashing poses a significant challenge to the fight against climate change by undermining trust in corporate sustainability claims. This study introduced the greenwashing tendency score (GTS), an automatable method designed to detect greenwashing tendencies in corporate sustainability reports. By leveraging textual sentiment and alignment analysis techniques in conjunction with environmental, social, and governance ratings, the GTS quantifies discrepancies between communicated and actual sustainability performance. We applied our methodology to 36 German stock index companies during the years from 2020 to 2022. Our key findings reveal substantial variations in greenwashing tendencies among these companies, emphasizing the need for more transparent and reliable sustainability reporting. The GTS emerged as a scalable, reproducible, and objective tool that can aid, for example, investors, regulators, and Non-government organizations in identifying greenwashing practices. This research contributed to the sustainable finance literature by introducing a neutral and open measure to assess firms' greenwashing tendency, summarizing implications for policymaking and regulatory authorities and discussing its potential for long-term accountability and integrity in corporate sustainability communications. Policy Significance Statement As sustainability becomes more and more important for consumers, investors, and policymakers, corporations face increased incentives to exaggerate their sustainability efforts through greenwashing. Such practices threaten transparency and accountability, potentially misleading different stakeholders and undermining policy objectives. To address this, we introduce the greenwashing tendency score (GTS), an objective and scalable measure that evaluates the authenticity of corporate sustainability communications using natural language processing (NLP) techniques. Policymakers and regulators can utilize the GTS to continuously monitor greenwashing trends, measure the effectiveness of sustainability regulations, and detect emerging anomalies across, for example, companies, industries, or geographical regions. The implementation of the GTS supports informed policymaking, enhances corporate accountability, and promotes genuine sustainability practices by ensuring sustainability claims align with actual environmental and social impact.

Greenwashing poses a significant challenge to the fight against climate change by undermining trust in corporate sustainability claims. This study introduces the Greenwashing Tendency Score (GTS), an automated method designed to detect greenwashing tendencies in corporate sustainability reports. By leveraging sentiment and alignment analysis techniques in conjunction with ESG ratings, the GTS quantifies discrepancies between reported and actual sustainability performance. We applied our methodology to 36 DAX40 companies over the years 2020 to 2022. Our key findings reveal substantial variations in greenwashing tendencies among these companies, emphasizing the need for more transparent and reliable sustainability reporting. The GTS emerged as a scalable, reproducible, and objective tool that can aid investors, regulators, NGOs, and companies in identifying greenwashing practices. This research not only contributes to the sustainable finance literature but also provides actionable insights for stakeholders committed to fostering accountability and integrity in corporate sustainability communications.

Heat is the largest single end-use sector, accounting for approximately one-third of final energy consumption in Germany. The European Union's targets for climate neutrality demand the decarbonization of the heating sector. This pilot study examines the relationship between heating billing methods and household heating behaviors in German residences. Through a comparative analysis of households with consumption-based versus non-consumption based billing systems, the conducted pilot study investigates differences in energy literacy, heating practices, and heating priorities. The descriptive findings from households reveal that residents with consumption based billing demonstrate higher self-reported knowledge of their heating and show stronger inclinations toward cost-saving and energyefficient behaviors. In contrast, households with nonconsumption based billing prioritize comfort oriented practices. These differences emphasize how billing methods influence heating behaviors and energy-efficiency awareness. Limited by sample size, this study provides valuable initial insights for developing targeted policies to promote sustainable heating practices and highlights the need for further research.

Decision support systems that evaluate user decisions have the potential to improve financial decision-making by alerting users to potentially disadvantageous choices. However, the feasibility of such systems, especially in complex decision-making scenarios, remains underexplored. This work in progress aims to investigate to what extend EEG-based decision support systems can be implemented using current technology. In a pilot study, we adapted the Iowa Gambling Task, a well-established decision-making paradigm, and collected 33-channel EEG data from three participants. As a proof of concept, we used a convolutional neural network (EEGNet) to classify positive and negative feedback, achieving subject-dependent binary classification accuracies ranging from 67 to 75

The increasing integration of renewable energy sources and the growing need for flexibility have made trading opportunities close to delivery increasingly important in European energy markets. This shift creates new profit opportunities for market participants, such as energy storage systems, which capitalise on temporal price differences. However, it also necessitates effective coordination across multiple markets-a significant challenge, particularly with continuous intraday trading. To address this, we propose an open-source, implementable framework for multi-market bidding under uncertainty designed to increase the profitability of energy storage systems through enhanced coordination. Specifically, we consider two spot markets: the day-ahead market and continuous intraday trading. By exploring varying degrees of market coordination with mixed integer linear optimisation, a rolling intrinsic algorithm and deep reinforcement learning, we discuss its ability to capture the benefits of multi-market participation. Already, a myopic multi-market approach that combines the day-ahead market and continuous intraday trading shows decreased risk at similar profit levels. The anticipation of rolling intraday profits, called coordinated multi-market bidding, remains a challenging task, but we present cases in which it starts to become beneficial. Most notably, we provide an open-source framework to further analyse the market dynamics.

In order to ensure energy affordability, we propose a design-oriented behavioral research study with the aim of helping low-income tenants to develop an efficient energy behavior by increasing their energy self-efficacy. We propose to compare different digital interventions in field tests to understand, in an un filtered way, what helps low-income tenants to be able to reduce their energy costs. We thereby contribute towards understanding how the vulnerable group of low-income tenants with their limitations and needs regarding their energy consumption behavior can be effectively supported digitally. In addition, we con tribute initial measurement instruments for energy worries, energy literacy and energy self-efficacy to evaluate the effects of digital interventions.

Background and Relevance: Virtual and hybrid work redefine expectations for video meeting systems (VMS), which should enable seamless collaboration. Users of VMS face challenges such as reduced engagement, lack of trust, and inefficient coordination. Further, virtual teams in organizational settings are regularly confronted with social dilemmas, where personal goals conflict with collective objectives. In these situations an individual's Social Value Orientation (SVO) has shown to significantly influence decision-making and team behavior. Unlike face-to-face interactions, trust-building processes are less explicit in virtual environments. To address this, we propose two intervention elements to strengthen the teamwork-related Shared Mental Model (SMM) of virtual teams. Proposal and Methods: We introduce a graphical recall of personality by examining the relationship between the SVO Slider Measure and low-order constructs of the Portrait Values Questionnaire (PVQ) with 42 students. Additionally, we propose the Mental Contrasting for Collaboration Rules (MCCR) method, which applies a Socratic approach to foster a collaborative mindset via mental contrasting, tested in a preliminary think-aloud study with six researchers. Results and Discussion: Our findings identify six PVQ constructs (achievement, power-resources, face, universalism-concern, universalism-tolerance, and conformity-rules) that are suitable for enhancing transparency about trustworthiness, but also raise discussions about Schwartz's conceptual distinction into personal-focus and social-focus. Moreover, while MCCR was perceived as useful, further iterations are needed to improve comprehensibility. Future Work: We will refine the intervention elements and conduct a controlled laboratory experiment to assess their impact in social dilemmas (e.g., Weakest Link Game) and their effectiveness in improving team coordination across different team compositions.

Electricity markets are undergoing transformative changes driven by integrating renewable energy and emerging technologies, and evolving market conditions such as shifting demand patterns, regulatory reforms, and increased price volatility. To address the complexity of electricity markets and their interactions, we present ASSUME, an open-source agent-based simulation framework that incorporates multi-agent deep reinforcement learning for modeling adaptive market participants. ASSUME offers a modular architecture for representing generator and demand-side agents, bidding strategies, and diverse market configurations. ASSUME has been proven effective in multiple research studies, demonstrating its ability to analyze complex bids, demand-side flexibility, and other market scenarios. By incorporating adaptive strategies through deep reinforcement learning, ASSUME supports dynamic strategy exploration, enabling a deeper understanding of electricity market behaviors. With its flexible architecture, documentation, tutorials, and broad accessibility, ASSUME ensures usability across different user groups, minimizing technical overhead and freeing up human resources for deeper insights into operational, economic, and policy-related challenges in this critical sector.

Collective self-consumption (CSC) in multi-family buildings presents a promising strategy to expand access to photovoltaic (PV) electricity in urban settings and is actively supported by recent EU directives. However, national implementations remain highly diverse highlighting the need for a systematic understanding of CSC system designs. Through a structured literature review, 41 peer-reviewed studies were analyzed to identify and classify six recurring CSC archetypes based on four key design dimensions: participation scale, consumption model, production model, and point of installation. Four primary archetypes are discussed in detail, highlighting their implications for PV-sharing system design and stakeholder inclusion. This synthesis provides a framework for comparing CSC models across different policy environments, highlighting trade-offs between centralized coordination and individual autonomy, and informing policy design for inclusive renewable energy adoption.

Collective self-consumption (CSC) in multi-family buildings presents a promising strategy to expand access to photovoltaic (PV) electricity in urban settings and is actively supported by recent EU directives. However, national implementations remain highly diverse highlighting the need for a systematic understanding of CSC system designs. Through a structured literature review, 41 peer-reviewed studies were analyzed to identify and classify six recurring CSC archetypes based on four key design dimensions: participation scale, consumption model, production model, and point of installation. Four primary archetypes are discussed in detail, highlighting their implications for PV-sharing system design and stakeholder inclusion. This synthesis provides a framework for comparing CSC models across different policy environments, highlighting trade-offs between centralized coordination and individual autonomy, and informing policy design for inclusive renewable energy adoption.

Amidst the rapidly evolving landscape of digital participation formats, navigating the field becomes a challenge for practitioners, policymakers, and researchers. This paper investigates how a taxonomy for digital involvement projects can be leveraged to capture design knowledge on participatory projects and make it accessible to relevant stakeholders. Employing a Design Science Research approach, we develop and assess an interactive web application, and preliminary design archetypes based on 46 project examples. Our research contributes to capturing the diversity of participatory project design, bridging theory and practice in the digital governance domain and beyond.

Background and Relevance: Virtual and hybrid work redefine expectations for video meeting systems (VMS), which should enable seamless collaboration. Users of VMS face challenges such as reduced engagement, lack of trust, and inefficient coordination. Further, virtual teams in organizational settings are regularly confronted with social dilemmas, where personal goals conflict with collective objectives. In these situations an individual’s Social Value Orientation (SVO) has shown to significantly influence decision-making and team behavior. Unlike face-to-face interactions, trust-building processes are less explicit in virtual environments. To address this, we propose two intervention elements to strengthen the teamwork-related Shared Mental Model (SMM) of virtual teams. Proposal and Methods: We introduce a graphical recall of personality by examining the relationship between the SVO Slider Measure and low-order constructs of the Portrait Values Questionnaire (PVQ) with 42 students. Additionally, we propose the Mental Contrasting for Collaboration Rules (MCCR) method, which applies a Socratic approach to foster a collaborative mindset via mental contrasting, tested in a preliminary think-aloud study with six researchers. Results and Discussion: Our findings identify six PVQ constructs (achievement, power-resources, face, universalism-concern, universalism-tolerance, and conformity-rules) that are suitable for enhancing transparency about trustworthiness, but also raise discussions about Schwartz’s conceptual distinction into personal-focus and social-focus. Moreover, while MCCR was perceived as useful, further iterations are needed to improve comprehensibility. Future Work: We will refine the intervention elements and conduct a controlled laboratory experiment to assess their impact in social dilemmas (e.g., Weakest Link Game) and their effectiveness in improving team coordination across different team compositions.

The impact of AI tools like ChatGPT on cognitive load in knowledge work is not yet fully understood in the evolving field of human-AI interaction. This study aims to explore the cognitive load dynamics arising from AI-assisted tasks, revealing their potential to streamline workflows, but also risking cognitive overload, potentially hindering task performance, learning, and enjoyment. Anchored in cognitive load theory, our research proposes an experiment that leverages wearable EEG technology to empirically investigate the cognitive load fluctuations experienced by individuals engaged in AI-assisted programming tasks. By dissecting the interplay between user engagement and AI assistance, this study seeks to uncover effective patterns of AI tool usage that mitigate cognitive overload. Thereby, we aim to contribute to cognitive load theory by detailing the interactive load dynamics inherent in AI-assisted work.

Dynamic tariff adoption is considered to be an important driver of demand response, enabling more sustainable and reliable power systems. However, first studies have shown that a high share of households subscribing to dynamic tariffs can lead to so-called "avalanche effects" on the distribution grid level, wherein load profiles align across households. Avalanche effects can create new demand peaks that necessitate costly grid reinforcement measures. Here, we analyze the impacts of policy options for grid charge and solar photovoltaic (PV) feed-in remuneration on grid reinforcement costs given increasing shares of dynamic tariff adoption. The analysis framework is open-source and uses empirical data from real households. We find that the widely proliferated regulatory scenario with volumetric grid charges and PV feed-in-tariffs leads to heavy reinforcement needs. We show that novel policy options, such as rotating or segmented grid charges, can alleviate grid reinforcement needs.