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PurposeThis study develops a structured framework for supplier risk assessment that supports the empirical assessment of supplier-level resilience capabilities. The framework integrates specific and overarching risk dimensions, factors and indicators, thereby enabling companies to proactively identify and manage potential disruptions. It addresses existing research gaps by consolidating fragmented perspectives into a coherent structure that enables empirical investigation and operationalization.Design/methodology/approachA node-level approach combines a structured literature review with expert workshops to derive operationalizable supplier-specific indicators that facilitate empirical investigation and cross-sector validation. The literature review identified essential risk dimensions, associated factors, and operational indicators, each linked to potential information sources. These were validated and weighted through expert workshops, forming the basis of a measurement approach intended as an assessment instrument for future application and testing.FindingsThe resulting framework comprises environmental, financial, social and operational risk dimensions, each associated with structured risk factors and indicators, providing a foundation for assessing supplier-specific vulnerabilities. By explicitly assigning information sources, such as certifications, indices, and internal records, the framework facilitates data acquisition. The expert workshops corroborated the relevance of a flexible model that allows for industry-specific adaptation while remaining amenable to model building.Originality/valueThis novel framework provides an integrated, multi-layered structure that translates resilience concepts into testable components for supplier risk assessment. It bridges theoretical constructs and operational indicators, paving the way for systematic supplier risk assessment across industries.
Over the past decade, research on the mental health of humanitarian staff has gained momentum, prompting organizations to increasingly support and invest in workers' mental health and well-being. However, many of the existing initiatives-such as one-on-one talk therapy-are grounded in Euro-American traditions. These approaches may not align with the cultural frameworks or preferences of all staff, potentially leaving significant segments of the workforce underserved. Furthermore, challenges related to team cohesion are difficult to address within current staff care structures. Against this backdrop, arts-based initiatives (ABIs) may present a promising complementary approach but remain largely overlooked in humanitarian staff care practice and research. This qualitative study explores the viewpoints of eight in-house mental health professionals (MHPs) currently or previously working for humanitarian and development organisations-a key but hard-to-reach study population-on complementing existing staff care services with ABIs, such as music-making, visual arts, and dance. Data generated through semi-structured online and in-person interviews were analysed using thematic analysis to examine perspectives regarding potential benefits, design considerations, and anticipated challenges related to implementing ABIs in humanitarian workplaces. Three themes reflecting MHPs' perspectives emerged from the analysis. Overall, MHPs supported introducing ABIs as complementary services. They emphasized what they perceived as the unique potential of these initiatives to enhance well-being, bridge cultural divides, and foster a sense of community, ultimately contributing to a more positive work environment. However, MHPs also underscored the importance of tailoring ABIs to the specific contexts in which they are implemented, requiring carefully designed interventions to prevent unintended negative outcomes. Amongst others, they identified accessibility and buy-in from senior management as key factors for a successful and sustainable rollout. Further research is needed to systematically explore staff preferences, participation factors, integration models, as well as, at a later stage, the effectiveness of ABIs for this occupational group.
Standard valuation methods, including discounted cash flow, the income approach standard IDW S 1 of the Institute of Public Auditors in Germany, and market multiples, compress milestone probabilities, continuation options, and risk shifts into opaque aggregate parameters; none provides a structured protocol for decomposing AI integration into auditable option-level assumptions. We propose an industry-agnostic taxonomy separating AI Integrators from AI Providers. AI Integrators are further classified by their Integration Depth Level, ranging from no integration to AI at the core of the product or process. A milestone-gated real-options overlay decomposes milestone state value into five components, and an Analytic Hierarchy Process-based Success Readiness Index derives per-option probabilities from structured pairwise comparisons for scenario analysis. Applied to an AI-native energy software-as-a-service firm, the framework yields a coherent valuation band traceable to identifiable option-level assumptions. Risk concentrates in later-stage continuation options, matching the structural prediction for AI Providers. The protocol applies across the firm lifecycle, including mergers and acquisitions due diligence. The case is a single-firm demonstration of protocol coherence, not empirical validation; multi-case testing against realised post-exit valuations is left to future research.
This study addresses the challenge of real-time detection of the weeds Colchicum autumnale and Rumex species on grassland sites, which is an inherently difficult problem because the predominantly green weed leaves provide little contrast to the similarly colored vegetation backgrounds. The resulting detector will be integrated into the SELBEWAG tool, a non-chemical, site-specific weed treatment device. We collected and annotated RGB video recordings from grassland sites in Southwest Germany and trained a quantized EfficientDet object detection model, which has been optimized for low latency on edge devices. The detection system achieved a mean average precision of 0.606 across both weed types (0.617 for Rumex and 0.595 for C. autumnale). With an optimal decision threshold, the model demonstrated precision values of 56.0
The importance of the diversification and the related diversification factor in business valuation is an area that still hasn't been studied in depth. In CAPM, due to the model assumptions, it is implied that the investor holds a perfectly diversified portfolio, which answers the question of diversification. The objective of this paper is to clarify the definition of diversification and how diversification effects can be incorporated into business valuation. The research contribution consists of showing methods to capture the risk situation of the valuation subject based on asset allocation and to derive the diversification effect from it. As a methodological approach, we chose the simulation-based business valuation, which proclaims for itself that it incorporates the most diversification effects and degree. The analysis shows that this method captures all non-hedged risks and thus performs a stand-alone valuation without diversification effects. If the diversification effect of a specific portfolio is to be included in the valuation, the valuer must move from the stand-alone approach to the portfolio level. In this case, the diversification effect cannot be equated with the correlation coefficient but must be calculated using various aggregation methods. Since the usual variance-covariance method excludes extreme risks, we propose the use of the Copula ap proach, which offers the potential for improving risk modeling and achieving more accurate results when calculating the diversification effect. Derived from our analysis, we can conclude that the modeling of different diversification effects in business valuation is possible, but this is associated with high data costs and high methodological competence of the valuer.
