Research indicates that the fashion industry can be made more environmentally friendly by introducing a closed-loop economy – also known as a circular economy – and implementing sustainable business models in areas such as clothing resale, rental, repair, and remake. Secondhand clothing is the sector to which research attributes the greatest potential for success in introducing new sustainable business models. This study examines the secondhand subscription box and eleven potential determinants from the secondhand fashion consumption literature. It complements a constraint-based approach (Necessary Condition Analysis – NCA) with an effect-based approach (regression analysis) to identify the necessary-but-not-sufficient conditions and the key (regression) factors shaping purchase intention. Using a sample of 215 German participants, we found that most determinants are necessity-based bottlenecks. They must be present and reach a certain threshold. Some are also key factors that drive purchase intention. The relevance of the determinants varies across sub-groups. This has implications for both academia and practice in designing secondhand subscription boxes. However, the study reveals that the determinants are context-dependent and no general principle can be transferred to other circular economy or sustainable consumption contexts.
Open-circuit voltage (OCV) hysteresis affects state estimation in lithium iron phosphate (LFP) graphite cells. This work presents a review and benchmark of empirical open-circuit voltage (OCV) hysteresis models within a framework that treats OCV hysteresis holistically. The Plett Zero-State and One-State model, the Huria model, the Roscher model, and a Modified Preisach model adapted to the proposed framework are considered. Validation uses a real-world profile derived from fleet data (>75,000 vehicles, >20 million charging events) and augmented with OCV hysteresis validation points. All models are evaluated on a commercially available 2.6Ah lithium iron phosphate (LFP) graphite cell for comparability. The Roscher model achieves the best performance, with an overall mean absolute error (MAE) of 1.585mV and a mean absolute percentage error (MAPE) of 4.7% for the OCV hysteresis validation points, while also showing the highest robustness against drift. The Modified Preisach, Plett One-State, and Huria model achieve comparable accuracy but exhibit higher sensitivity to drift. Error decomposition indicates that polarization errors dominate under load, whereas hysteresis modeling becomes critical during rest. Sensitivity analysis demonstrates that reducing parameter interpolation decreases drift and measurement effort without compromising accuracy. The results provide guidance for robust hysteresis modeling in automotive battery management systems (BMSs).
During the energy crisis in 2022, electricity prices in Germany soared to unprecedented levels. To explore the drivers of the high electricity prices, we develop an electricity dispatch model that simulates hourly equilibrium prices under the assumption of perfect competition. We then extend this model to account for firms exercising market power. By comparing the outcomes of the perfect competition and Cournot competition models with actual market data, we demonstrate that market power may have contributed to higher prices during the crisis, elevating them beyond what rising input costs alone would justify.
Disruptive digital technologies, such as Artificial Intelligence (AI) and big data technologies, have profoundly reshaped businesses, impacting intra-firm processes, operations, and business models. Inter-firm collaboration serves as a fertile ground for disruptive digital technologies, while also fostering their refinement and implementation. Building on fragmented insights from diverse fields, our study conducts a systematic literature review to examine the bidirectional relationship between disruptive digital technologies and inter-firm collaboration. It is based on an analysis of 67 empirical studies published between 2010 and 2025 and lays the foundation for the ‘collaboration rewiring model’. This model synthesizes prior research across three stages of the collaboration process: (1) the initiation phase, in which disruptive digital technologies act as catalysts for collaboration, (2) the collaboration phase, where they transform collaboration processes, governance mechanisms, and relational dynamics, and (3) the outcome phase in which they reshape the outcomes of collaboration. Our collaboration rewiring model puts forward that disruptive digital technologies have distinct, phase-specific and interrelated influences on inter-firm collaboration. Taken together, the collaboration rewiring model offers a structured account of how disruptive digital technologies influence inter-firm collaboration and points to promising avenues for future research.
Tungsten oxide (WO3) thin films find applications in a variety of fields, including chromism, gas sensing, and photoelectrochemistry. In the context of each application, the necessity arises for the identification of specific material properties. In this study, we propose a sol-gel methodology that facilitates the modulation of material properties through the precise manipulation of synthesis parameters. The calcination temperature, template concentration, and number of dip coating steps were found to have a significant impact on the crystallinity, (002) facet exposure, surface area, band gap, and absorption efficiency of the mesoporous WO3 thin films. In the context of photoelectrochemical water oxidation, it has been determined that the crystallinity, band gap, and light absorption of the material are positively correlated with enhanced bulk processes. Photocurrent values of up to 3.75 mA cm-2 at 1.23 V vs RHE under AM 1.5 G illumination were achieved, while external quantum yields of 58% were also obtained. The systematic correlation between synthesis parameters and material properties, as outlined in this study, offers a straightforward and efficient method for adjusting WO3 thin film properties to meet specific application requirements.
