Vienna University of Economics and Business

Disruptions in the business landscape have made resilience an increasingly important concept for multinational firms. As resilience has gained momentum, firms try to enhance their resilience to mitigate the impact of future disruptions and foster performance stability. However, empirical studies on the long-term effects of resilience on firms are rare. This study explores the relationship between resilience and innovation performance. We draw on the resilience literature and the ambidexterity view to suggest hypotheses that link higher resilience to lower innovation performance. Specifically, we argue that higher resilience puts an emphasis on exploitative capabilities and reduces explorative capabilities necessary for innovation. Using a sample of S&P 500 firms, we find support for our main hypothesis. More resilient firms had a lower innovation performance in the long run. This provides evidence for the inherent tension between short-term resilience and long-term success. Our results suggest that resilience is not the ultimate solution, but rather that firms need to carefully balance the resilience measures implemented to ensure long-term success. Our results also show that this effect is even more pronounced for more internationalized firms.

ABSTRACT How employees respond to organizational change is critical to the success of change initiatives. Scholars from diverse research communities have developed a wide range of theories to explain why some employees support while others resist organizational change. However, making sense of this theoretical diversity remains a key challenge for researchers aiming to advance both organizational change theory and practice across research communities. To address this challenge, we reviewed 215 empirical articles published between 1998 and 2024 in leading business administration journals. Through this review, we identified eight overarching theoretical themes that structure existing explanations: (1) motivational–attitudinal, (2) relational, (3) sensemaking and discourse, (4) work environment, (5) emotion, (6) morality and justice, (7) individual characteristics, and (8) learning. These themes provide a pluralistic map of the field's theoretical landscape, making visible the distinct perspectives, assumptions, and explanatory logics that characterize different research communities. Building on this framework, we outline how scholars can use our framework to advance research on change responses in five key areas: (a) refine theorizing within themes, (b) integrate across themes, (c) leverage new styles of theorizing, (d) extend theorizing on temporality, and (e) advance context‐sensitive theorizing. Finally, we elaborate on how the eight themes can be used in practice by change managers. In sum, this paper offers a foundation for pluralistic integration and advancing theory on employee responses to change across research communities.

Accelerating climate-tech innovation in the formative phase is crucial to meeting climate goals. However, effective green industrial policies require an understanding of when and where to target policy interventions within the value chain. We conceptualize nascent value chains for climate-tech as product clusters and explore innovation patterns within and across them. We analyze 14 climate-tech sectors using early-stage private investments in over 3,600 North American firms (2006-2021). In terms of product clusters, only 15% of firms develop end products, while 59% provide components, manufacturing, or optimization products, and 26% develop services. Investment evolution reveals three patterns of innovation: maturing innovation (e.g., energy efficiency), ongoing innovation (e.g., energy storage), and emerging innovation (e.g., agriculture). This characterization of nascent value chains offers an analytical basis for green industrial policy, identifying critical structural segments for intervention and illustrating how different value chain positions can create varied opportunities and pathways for regional benefit.

The high growth rate in digital businesses has heightened issues around the world about the environmental footprint of information technology (IT) infrastructure, especially as the data centres, cloud services, and high-performance computing workloads are becoming a large contributor to increasing energy use and carbon emissions. Although the topic of sustainability has gained increased regulatory and corporate attention, there remains no set, data-based approaches to measure the ecological performance of the organizations with regard to the digital operations. The paper presents a holistic analytical model that combines sustainable IT infrastructure indicators, cloud resource optimization policy, and corporate sustainability key performance indicators (KPIs). Based on a mixed-methodology, which integrates empirical data related to industry benchmarking, cloud provider sustainability reporting, and already existing environmental reporting criteria, the framework provides a systematic approach to connect micro-level IT energy telemetry (including power usage effectiveness (PUE), server utilization rates, virtualization efficiency, and carbon intensity of workloads) to macro-level sustainability results, including reductions in greenhouse gas (GHG) emissions, integration of renewable energy, energy cost reductions, and improvement in ESG performance. Quantitative modeling based on real world data demonstrates how green data analytics can be used to aid in carbon-conscious scheduling, predict IT energy demand and optimizing allocation of cloud resources. The outcomes indicate that the incorporation of IT operating data with high-end analytics can improve greatly the level of transparency, the measurement precision, and the environmental responsibility of digital enterprises. The originality of the study is the cross-layered mapping of technical IT metrics on organizational sustainability KPIs, which can be replicated to achieve the net-zero digital transformation. The suggested framework offers practical information to businesses, policymakers, and cloud service providers interested in realizing sustainability goals in fast-changing digital realms.

Biodiversity loss is part of the triple planetary crises and is strongly driven by global patterns of consumption, production, and international trade. Environmentally extended multi-regional input–output (EE-MRIO) models are widely used to trace environmental pressures along global supply chains, yet most biodiversity footprint studies are limited to historical snapshots or marginal changes after shocks. Forward-looking assessments that capture long-term structural transformations in socioeconomic development, technology, and trade remain scarce.In this study, we develop a framework for constructing prospective EE-MRIOs by systematically integrating scenario outputs from the integrated assessment model IMAGE into the global MRIO database EXIOBASE. The economic scenario information includes projections of macroeconomic development, industrial production, energy and crop system transitions, prices, and trade patterns. The environmental stressors include greenhouse gas emissions and land use, two main drivers of biodiversity loss. Prospective EE-MRIOs are generated for a business-as-usual baseline (SSP2) and three policy-oriented scenarios of reduced demand, protected areas, and climate mitigation.As an initial application, we compare CO2 emission footprints in 2019 with projections in 2035 under SSP2. The results show that rapidly growing regions such as rest of Asia, India, and rest of Africa experience strong emissions increases due to fast economic growth combined with relatively slow decarbonization, whereas emissions decline in regions such as the USA, Western Europe, and China as a result of stronger decarbonization and slower population-driven GDP growth. This highlights a growing divergence in regional emission trajectories driven by unequal structural transformation.Building on this baseline, the prospective EE-MRIOs enable a consistent forward-looking analysis of the responsibility allocation of biodiversity loss along global supply chains, the evolution of consumption-based drivers of biodiversity loss under different development pathways, and the assessment of synergies, trade-offs, and leakage effects under climate mitigation, conservation, and demand-side strategies. By providing a structurally consistent representation of future global supply chains, this work offers a new quantitative basis for evaluating long-term biodiversity risks and supporting long-term policy design.

The escalating demand for minerals and metals due to digitalization, infrastructure growth, and the renewable energy transition is driving an ever-increasing expansion of mining activities. To support the effective consideration of biodiversity commitments of governments and to ensure that the implications for biodiversity are included in any planning, there is an urgent need for decision-relevant, comprehensive spatial information. Here, we present a framework that integrates global mining land-use data with a spatially explicit biodiversity assessment. The assessment approach addresses both local and global biodiversity consequences of mining-driven land use and pinpoints specific species and locations most strongly affected. Using multiregional input-output analysis, we trace these mining-related biodiversity footprints along global supply chains. Our results show that biodiversity loss impacts associated with global mining land use are nearly twice as high as previously estimated. Hotspots in Indonesia, New Caledonia, Australia, Brazil, and Peru account for 57% of global mining-related biodiversity impacts. Coal, precious metals, nickel, iron, and copper extraction together contribute 82% of the total impacts. Due to international trade, 77% of mining-related biodiversity footprints occur outside the countries of final consumption. Demands from China, Europe, Japan, and the USA, primarily in construction, services, machinery, and electronics, account for 58% of mining-related biodiversity footprints. Together, these findings provide a global baseline of current mining-related biodiversity pressures, against which future mining development and biodiversity outcomes can be evaluated.Building on our current baseline assessment, we link future mining land-use trajectories with species-level biodiversity outcomes under alternative pathways representing different exploration intensities. We combine exploration activity maps with historical land-use data to infer future spatial patterns of mineral extraction. For over 80,000 terrestrial vertebrates and tree species, we quantify changes in habitat-suitable ranges and attribute pixel-level habitat gains or losses to projected mining land conversion. The resulting regionalized biodiversity response will enable spatially explicit projections of species extinction risk under alternative mining land-use scenarios. In combination, these findings can provide a continuous picture of how mining, from current operations to future expansion, affects biodiversity, offering key insights for reconciling mineral supply planning with global conservation and sustainability goals.

The growing integration of intermittent renewables, especially solar PV, challenges grid stability and limits the operational flexibility of conventional hydropower. Beyond hydrological variability, hydropower is constrained by mandatory ecological flow releases, which restrict its ability to reduce generation during high solar output or ramp up during peak demand due to water scarcity. This paper introduces Ecological Flow Energy Storage (EFES), a cost-effective, environmentally sound solution to enhance grid flexibility. EFES involves building small reservoirs downstream of existing hydropower plants to temporarily store ecological flows. This decouples ecological requirements from electricity generation, enabling hydropower plants to halt generation during solar-rich periods without breaching environmental mandates. Stored water can later be released through upstream turbines during low solar availability or peak demand, effectively turning a regulatory constraint into a strategic energy storage asset. EFES supports better integration of renewables, optimizes hydropower dispatch, and enables economic arbitrage, all while preserving downstream ecological services. In Brazil, the estimated EFES potential is 14.3 GW, with a storage cost that can be ten times lower than battery storage. This paper explores the technical feasibility, economic viability, and environmental implications of EFES, highlighting its promise as a scalable, low-cost energy storage solution for modern power systems. • A proposal for increasing the flexibility of hydropower for both ecological and energy services • Downstream micro-reservoirs can decouple hydropower generation from ecological flow. • Enabling hydropeaking to reduce solar curtailment • In Brazil, Ecological Flow Energy Storage can shave ∼14.3 GW peak. • A scalable, low-cost path to integrate VRE via hydropower flexibility

• Exploration of various datasets and models used for information disorder detection • Creation of domain-specific mixed datasets named MENA with a focus on European news • Insights from an in-depth cross-data and cross-model comparative analysis • RoBERTa and the Longformer model show best performance in detection experiments • Context matters and domain-specific datasets contribute to the robustness of models Information disorders, such as dis-, mis-, and malinformation, can lead to societal and/or economic harm. They are rapidly spread, extensively consumed on the web, and represent a threat to democracy. AI-based detection models can identify information disorders to some extent. However, major issues are the dynamics of news characteristics and concept drift. The generalization ability of a model is an important requirement and refers to its robustness when applied on unseen data. The aim of this work is to better understand the state of the art regarding information disorder detection approaches by conducting a reproducibility study and a cross-data and cross-model comparative analysis that leads to: (i) insights with respect to the effectiveness of binary information disorder classification; (ii) performance results on seen and unseen data; and (iii) new mixed European datasets named MENA. We conduct an evaluation of a fine-tuned BERT-based model applied on European data, which has received limited attention to date. The best performing models in our experiments are the RoBERTa and the Longformer models. The evaluation gives insights about potential biases of datasets that can be used to improve a model’s generalization ability. We also show that using domain-specific datasets for fine-tuning contributes to the robustness of models. Finally, we provide takeaways concerning reproducibility and stress the need for more transparent AI-based detection techniques.