Environmental Decision Research Articles

The Application of GIS Technology in Environmental Management in Ghana

This article traces the evolution of Geographic Information Systems (GIS) from the early days of cartography into contemporary time, within the context of environmental decision-making. The quantitative revolution influenced the development of cartography. In the 1970s computerized mapping and GIS automated cartography. The format and management of spatial data got transformed in the 1980s with the development of geo-query in GIS. In the 21st century GIS included social perspectives in managing GIS data bases. The technology was introduced into higher educational institutions of Ghana in the 1990s. Government departments recognized it to be an important tool for resolving spatial problems. Its potential for consensus building is yet to be fully exploited because the technology was considered to be complex. This was solved by shifting from generating GIS codes, to graphical interface and spatial model applications. From its initial focus of mapping and spatial database management, GIS now concentrates on varied degrees of complexity in modeling. Uses to which the technology has been put in Ghana include mapping the spatial distribution of Buruli Ulcer; using Digital Elevation Model (DEM) in geospatial and hydrological modeling to delineate drainage and watershed to predict flood prone areas in parts of Accra; and, in groundwater projects to select well sites for drilling bore holes. It was concluded that continued application of GIS in Ghana and other parts of the world could result in rare and timeless solutions being found to spatial and environmental problems that confront humanity.

Estimation of flint hills tallgrass prairie productivity and fuel loads: a model-based synthesis and extrapolation of experimental data

ContextThe > 25,000 km2 Flint Hills ecoregion in eastern Kansas and northeastern Oklahoma, USA, is an economically and ecologically important area encompassing the largest remaining tallgrass prairie ecosystem in North America. Prescribed fires are used routinely to control invasive woody species and improve forage production for the beef-cattle industry. However, burning releases harmful pollutants that, at times, contribute to air quality problems for communities across a multi-state area.ObjectivesEstablish a modeling framework for synthesizing long-term ecological data in support of Flint Hills tallgrass prairie management goals for identifying how much, where, and when rangeland burning can be conducted to maximize ecological and economic benefits while minimizing regional air quality impacts.MethodsWe used EPA’s VELMA ecohydrology model to synthesize long-term experimental data at the 35 km2 Konza Prairie Biological Station (KPBS) describing the effects of climate, fire, grazing, topography, and soil moisture and nutrient dynamics on tallgrass prairie productivity and fuel loads; and to spatially extrapolate that synthesis to estimate grassland productivity and fuel loads across the nearly 1000 times larger Flint Hills ecoregion to support prescribed burning smoke trajectory modeling using the State of Kansas implementation of the U.S. Forest Service BlueSky framework.ResultsVELMA provided a performance-tested synthesis of KPBS data from field observations and experiments, thereby establishing a tool for regionally simulating the combined effects of climate, fire, grazing, topography, soil moisture, and nutrients on tallgrass prairie productivity and fuel loads. VELMA’s extrapolation of that synthesis allowed difficult-to-quantify fuel loads to be mapped across the Flint Hills to support environmental decision making, such as forecasting when, where, and how prescribed burning will have the least impact on downwind population centers.ConclusionsOur regional spatial and temporal extrapolation of VELMA’s KPBS data synthesis posits that the effects of integrated ecohydrological processes operate similarly across tallgrass prairie spatial scales. Based on multi-scale performance tests of the VELMA-BlueSky toolset, our multi-institution team is confident that it can assist stakeholders and decision makers in realistically exploring tallgrass prairie management options for balancing air quality, tallgrass prairie sustainability, and associated economic benefits for the Flint Hills ecoregion and downwind communities.

Adaptive Handover Management in High-Mobility Networks for Smart Cities

The seamless handover of mobile devices is critical for maximizing the potential of smart city applications, which demand uninterrupted connectivity, ultra-low latency, and performance in diverse environments. Fifth-generation (5G) and beyond-5G networks offer advancements in massive connectivity and ultra-low latency by leveraging advanced technologies like millimeter wave, massive machine-type communication, non-orthogonal multiple access, and beam forming. However, challenges persist in ensuring smooth handovers in dense deployments, especially in higher frequency bands and with increased user mobility. This paper presents an adaptive handover management scheme that utilizes reinforcement learning to optimize handover decisions in dynamic environments. The system selects the best target cell from the available neighbor cell list by predicting key performance indicators, such as reference signal received power and the signal–interference–noise ratio, while considering the fixed time-to-trigger and hysteresis margin values. It dynamically adjusts handover thresholds by incorporating an offset based on real-time network conditions and user mobility patterns. This adaptive approach minimizes handover failures and the ping-pong effect. Compared to the baseline LIM2 model, the proposed system demonstrates a 15% improvement in handover success rate, a 3% improvement in user throughput, and an approximately 6 sec reduction in the latency at 200 km/h speed in high-mobility scenarios.

Addressing the Uncertainties in the Environmental Analysis, Modeling, Source and Risk Assessment of Emerging Contaminants

Emerging contaminants (ECs) have become a growing source of worry for environmental researchers and stakeholders in recent decades. Compared with conventional pollutants, ECs can pose environmental risks even at a trace level. The analysis of ECs is typically significantly more challenging than that of conventional pollutants because of their trace amounts and diverse chemical structures. For sound environmental management, it is necessary to perform a comprehensive study of these pollutants. Global concern has increasingly grown over the occurrence, fate, environmental modeling, and risk assessment of such contaminants. Due to the dearth of knowledge in this area, various unc3ertainties inevitably exist in the investigation of ECs. Environmental problems cannot be precisely understood due to the ubiquitous uncertainties in environmental research. Uncertainties and their sources have been reviewed in this study, including spatial and temporal variability, uncertainty in sample collection and analysis, uncertainty in environmental modeling, uncertainty in risk assessment, and uncertainty in source characterization. Some suggestions to reduce uncertainties are summarized. An awareness of uncertainty is necessary for us to have a more accurate understanding and contribute to sound environmental decision-making and management. In addition, more work remains to be performed to reveal the uncertainties in the analysis and risk assessment of ECs.

Do We Have Sufficient Evidence to Derive Innovative Approaches to Assessing Unmet Need, Delivering Education on Bladder and Bowel Continence Health, and Providing a Better Environment for Joint Decision-Making? ICI-RS 2024.

Improved continence outcomes are reliant on identification of unmet need, education delivery, and shared decision-making. The evidence base on which to derive innovative approaches in these areas was unclear. A debate held at the International Consultation on Incontinence-Research Society meeting, held in Bristol in June 2024, considered ways to improve research requirements to advance these areas. Artificial intelligence solutions and digital approaches to healthcare are emerging at pace and offer possibilities to improve these three key areas but this must be driven by person-centered approaches. Care must be taken to avoid increasing inequality through digital exclusion and language barriers. Research questions are highlighted to derive innovation in these three key areas.

Distinct computational mechanisms of uncertainty processing explain opposing exploratory behaviors in anxiety and apathy.

Decision-making in uncertain environments can lead to varied outcomes, and how we process those outcomes may depend on our emotional state. Understanding how individuals interpret the sources of uncertainty is crucial for understanding adaptive behavior and mental well-being. Uncertainty can be broadly categorized into two components: volatility and stochasticity. Volatility describes how quickly conditions change. Stochasticity, on the other hand, refers to outcome randomness. We investigated how anxiety and apathy influenced people's perceptions of uncertainty, and how uncertainty perception shaped explore-exploit decisions. Participants (N = 1001, non-clinical sample) completed a restless three-armed bandit task that was analyzed using both latent state and process models. Anxious individuals perceived uncertainty as resulting more from volatility, leading to increased exploration and learning rates, especially after reward omission. Conversely, apathetic individuals viewed uncertainty as more stochastic, resulting in decreased exploration and learning rates. The perceived volatility-to-stochasticity ratio mediated the anxiety-exploration relationship post-adverse outcomes. Dimensionality reduction showed exploration and uncertainty estimation to be distinct but related latent factors shaping a manifold of adaptive behavior that is modulated by anxiety and apathy. These findings reveal distinct computational mechanisms for how anxiety and apathy influence decision-making, providing a framework for understanding cognitive and affective processes in neuropsychiatric disorders.

IMPLICATIONS OF THEORY OF PARTICIPATION FOR UNDERSTANDING THE CHALLENGES AND PROCESSES ASSOCIATED WITH DECARBONIZING THE UK BY 2050

Climate change remains one of the most urgent global challenges, with severe consequences including droughts, extreme weather events, and displacement. The transition to a low-carbon economy is widely recognized as essential for mitigating these effects. This paper explores the United Kingdom’s pathway toward decarbonization, emphasizing the importance of public participation through democratic innovations like mini publics. The theory of participation involves engaging the public in policy formation and environmental decision-making. By examining mini publics such as citizens' assemblies, this study highlights their role in enhancing public engagement and fostering acceptance of climate policies. The benefits of mini public include diverse viewpoints, improved cooperation between experts and citizens, and greater adherence to regulations. However, limitations exist, including challenges in achieving true representativeness, potential manipulation of expert testimonies, and the risk of recommendations being ignored by policymakers. Balancing the strengths and weaknesses of participatory approaches is vital to driving effective and inclusive decarbonization efforts in the UK. KEYWORDS: Decarbonization, Mini Public, Theory of Participation, Public Participation, Climate Policies.

Regional green economies and Bitcoin's electricity consumption: Paving the way for global sustainability.

In the context of global sustainability goals, it is imperative to examine the interplay between emerging financial technologies and environmental objectives, which in turn offers insights into the implications of such technologies on sustainable investments and environmental policy decisions. Accordingly, this study investigates the influence of Bitcoin's energy consumption (BEC), as a representative of the cryptocurrency market, on the regional green economy indices of the United States, Europe, and Asia. Utilizing both unfiltered and filtered data, we conduct empirical analyses from both static and dynamic perspectives to account for the causal relationships that emerged during critical market developments, employing novel algorithms including forward recursive, rolling windows, and recursive rolling. The results reveal that energy-related developments play a dominant role in shaping the causal link between BEC and the green economy performance. More specifically, events such as the COVID-19 pandemic, the Russia-Ukraine war, and China's ban on cryptocurrency mining appear to significantly drive casual effects. After filtering out the impacts of global equity markets and information technology developments, we observe even more pronounced impacts on these causal relationships. Our findings highlight the need for coordinated global actions to address the energy consumption of the cryptocurrency market, facilitating the transition to global sustainability.

Emergency Airway Management: A Systematic Review on the Effectiveness of Cognitive Aids in Improving Outcomes and Provider Performance

Background/Objectives: Emergency airway management is a critical skill for healthcare professionals, particularly in life-threatening situations like “cannot intubate, cannot oxygenate” (CICO) scenarios. Errors and delays in airway management can lead to adverse outcomes, including hypoxia and death. Cognitive aids, such as checklists and algorithms, have been proposed as tools to improve decision-making, procedural competency, and non-technical skills in these high-stakes environments. This systematic review aims to evaluate the effectiveness of cognitive aids in enhancing emergency airway management skills among health professionals and trainees. Methods: A systematic search of MEDLINE, Embase, CINAHL, Cochrane Library, Scopus, Web of Science, and ClinicalTrials.gov was conducted from February to March 2024. Studies examining the use of cognitive aids, such as the Vortex method, the ASA difficult airway algorithm, and visual airway aids, in emergency airway scenarios were included. Outcomes assessed included decision-making speed, procedural success rates, and non-technical skills. Data were extracted using standardized protocols, and the quality of included studies was appraised. Results: Five studies met inclusion criteria, encompassing randomized controlled trials, controlled studies, and mixed-methods research. Cognitive aids improved decision-making times (reduced by 44.6 s), increased procedural success rates, and enhanced non-technical skills such as teamwork and crisis management. Participants reported reduced anxiety and improved confidence levels (self-efficacy scores increased by 1.9 points). The Vortex method and visual cognitive aids demonstrated particular effectiveness in simulated scenarios. Conclusions: Cognitive aids significantly enhance emergency airway management skills, improving performance, reducing errors, and increasing provider confidence. Integrating cognitive aids into training programs has the potential to improve patient safety and outcomes. Further research is needed to validate these findings in clinical settings and optimize cognitive aid design and implementation.

The Role of Religion as an Informal Institution in Shaping Corporate Environmental Responsibility: A SWOT Analysis of Publicly Listed Companies in Religiously Diverse Regions

This study examines the influence of religion on corporate environmental decision-making, particularly focusing on publicly listed companies in regions with varying religious contexts. The core issue addressed is how religion, as an informal system, can affect corporate behaviour in terms of environmental responsibility, especially in contexts where formal institutions or regulations are insufficient. To address this issue, the SWOT analysis method is employed. The problem is discussed from four perspectives: strengths, weaknesses, opportunities and threats. The findings indicate a positive correlation between the intensity of regional religious influence and corporate environmental responsibility. Companies in regions with a strong religious presence are more likely to disclose environmental information, invest in sustainable practices and reduce pollution, particularly in high-pollution industries. Additionally, religion was found to foster higher corporate social responsibility (CSR) through its ability to shape managers ethical values. Based on these findings, the study suggests that companies should integrate religious or cultural values into their CSR strategies to enhance environmental management. Governments, on the other hand, could collaborate with religious organisations to supplement formal environmental policies, particularly in areas where regulatory enforcement is weak, thereby improving corporate adherence to environmental responsibility.

Assessment of N-related microbial processes in the soil of the Polesie National Park and adjacent areas, including reclaimed land.

Soil microorganisms are essential for maintaining ecosystem functionality, particularly through their role in the nitrogen (N) biogeochemical cycle. Thus, they also contribute to greenhouse gas emissions from soils. Microorganisms are sensitive indicators of soil health, as they respond rapidly to disturbances caused by factors like unsustainable agricultural practices or industrial activities, such as mining. These bioindicators are also useful for monitoring the effectiveness of environmental remediation treatments, such as soil reclamation. The study aimed to assess microbial activity related to the N cycle in soils across various habitats (peatland, grassland, forest, and field) under conservation management within the Polesie National Park (PNP), as well as in comparable conventionally managed habitats. Additionally, the effectiveness of soil reclamation was assessed using waste from the mining industry, specifically gangue. Significant differences were observed in the assessed activities, both between habitats and their locations, i.e., within the PNP and outside of it. It was found that microbial activity was lower in soils from the PNP area. This was likely a result of more intensive fertilizer application on soils not subject to the restrictions imposed by park regulations. It should be noted that urease activity, which can contribute to the adverse phenomenon of nitrogen loss from soils when elevated, similarly to nitrous oxide (N2O) emission, was lower in the soils from the park than in those located outside of it. Therefore, designating an area as a national park protects the soil environment from human pressures associated with intensive agriculture, thereby helping to maintain its homeostasis. Reclamation using gangue proved ineffective in restoring microbial activity in degraded soil to the level of cultivated soil outside the park. The assessed activities in the reclaimed soil were lower than the target levels. The results provided valuable information useful for environmental management decisions in terms of soil and air protection, as well as the use of waste for reclamation.

Spatiotemporally derived agricultural field delineations for species effects assessments and environmental decision support.

Rural landscapes are strongly defined by the spatial distribution of agricultural fields. GIS layers that capture this information have much utility in many decision support contexts, particularly with regards to the intersection of agricultural pesticide use and endangered species habitat. The United States Department of Agriculture's Cropland Data Layer (CDL) is a georeferenced, annual resource that often serves a crucial role in pesticide risk-related decision support applications. However, CDL agriculture timeseries data are not mapped to explicit field boundaries, contributing to increased uncertainty regarding differentiated crop type spatial homogeneity and geographic extent, inherently adding complexity to multi-temporal crop monitoring and analyses efforts. We describe the development and testing of an approach for field delineation based on timeseries information from the 2008-2021 CDL at spatial scales relevant for endangered species risk assessment. We validate and test the approach against quantitative crop information and contextualize the outputs as part of a case study reconstructing past agricultural pesticide exposures to non-target species to demonstrate the utility of the method for ecological risk assessment decision support. The approach resulted in delineated field unit boundaries that effectively incorporated the unmodified CDL crop type generalized spatial distribution patterns; derived metrics closely corresponded with reported crop metrics for landscapes with proportionally significant agriculture use. When modified to reflect areas of mixed/small crop acreages, the method can provide a useful framework for large-scale field delineation of the CDL, which can complement ongoing environmental risk assessment and conservation efforts in agricultural landscapes.

Bridging causal explanation and predictive modeling: the role of PLS-SEM

Partial Least Squares Structural Equation Modeling has gained considerable attention across diverse academic fields, including business, social sciences, marketing, and management. A key challenge in utilizing PLS-SEM is balancing explanatory and predictive power when selecting the most suitable model from competing alternatives. This paper explores the effectiveness of various quality criteria for evaluating causal-predictive models, with a focus on resistance to change in Vietnamese SMEs. The study emphasizes the importance of both in-sample and out-of-sample predictions, using metrics such as R², BIC, AIC, Q², RMSE, MAE, and CVPAT. The findings reveal that traditional criteria like R² may not be sufficient for identifying the best model, while PLSpredict, CVPAT, BIC, and AIC offer superior performance in determining the optimal balance between explanatory and predictive capabilities. These insights provide practical implications for researchers and practitioners, highlighting the need to tailor model selection to specific objectives, such as theory development or real-world forecasting. For practitioners, the study underscores the benefits of leveraging simpler, more generalizable models for robust decision-making in dynamic or resource-constrained environments.

Biphasic models improved S‐metolachlor dissipation endpoint predictions

AbstractAccurate predictions of herbicide dissipation endpoints are essential for making environmental management decisions, estimating the duration of weed control, and deciding crop replant and rotation options. The performance of the single first‐order (SFO), first‐order multiple‐compartment (FOMC), bi‐exponential (BEXP), first‐order double‐exponential decay (FODED), and first‐order two‐compartment (FOTC) was evaluated to describe S‐metolachlor dissipation. Results showed that the BEXP and FOTC were not appropriate. The FODED was ranked as the top model with the lowest Akaike's information criterion (AICc) value among the other three candidate models and was followed by the FOMC model. The SFO displayed the largest root mean square error (11.43) and mean absolute error (9.16) values, and the smallest Nash–Sutcliffe efficiency value (0.88) within the set of three appropriate candidate models. The SFO predicted a time it takes the herbicide to dissipate to one‐eighth of its original concentration, 21 and 26 days shorter than that predicted by the FOMC and FODED, respectively. The SFO also predicted a time for S‐metolachlor to dissipate to one‐tenth of its original concentration, 33 days shorter than that predicted by both FOMC and FODED. Therefore, even though the SFO is effective at predicting half‐life, caution might be required when making inferences about DT90 or the time required for the dissipation of 90% of the initial concentration of pesticides in the soil using the SFO.

Social conditions of smallholder dairy farmers influence their environmental decisions

The objective of this study is to characterize the social conditions for making agricultural decisions. Particular attention is paid to sustainable agricultural practices in dairy farming. The theoretical framework has been developed around two major explicatory perspectives: sustainable development and quality of life. In the study, we assume that the regional dimension of problems related to the sustainable development of the dairy sector requires solutions adequate to the needs of the regions to create sustainable milk production systems. We put forward a thesis on the impact of objective living conditions of farmers on the readiness to introduce changes in the farm and to apply sustainable agricultural practices. The study has been carried out using the survey method, conducted in a face-to-face interview with fifty farmers engaged in milk production. This study contributes to revealing the role of social sustainability criteria for adopting environmentally friendly farming practices. The novelty of the approach is the use of in-depth sociological analysis in research on the sustainable development of agriculture in small farms (1–5 cows). The results have revealed a significant positive relationship between the assessment of farmers’ well-being and the willingness to adopt sustainable agricultural practices. The results also suggest that the analysis of social determinants of agricultural decisions is essential for the implementation of the assumptions of sustainable agriculture. Here we demonstrate that the criteria of subjective well-being can be considered important factors in a positive attitude towards sustainable development and openness to changes in agricultural production.

Choosing a Competitive Advantage Under the Constraints and Possibilities of the External Environment: An Empirical Study in the Retail Trade Sector in Elbasan, Albania

The effects of macro and micro environmental on the decisions of choosing the operations strategy in the manufacturing industry are part of a number of scientific researches. In addition to this industry, the service industry, mainly in recent years, has attracted the attention of researchers, including the way it develops operations strategies. Based on the experiences of the manufacturing sector, in this paper we will try to analyze the relationships between external environmental factors and decisions on the choice of operations strategy. The conducted study highlights important relations between these variables. Thus, technology, as one of the most dynamic macro-environmental factors, through its elements such as retail computer systems or even innovations, dictates the choice of operations strategy. While other elements, such as labor supply does not appear to have the same impact as technology on choosing the right strategy of operations in the retail sector in our country. In order to prove the hypothesis raised in this study we used the factor and the path analysis. The primary data collected through the questionnaire built specifically for this study were processed with the SPSS program.

Temporal trends and predictive modeling of air pollutants in Delhi: a comparative study of artificial intelligence models

Air pollution monitoring and modeling are the most important focus of climate and environment decision-making organizations. The development of new methods for air quality prediction is one of the best strategies for understanding weather contamination. In this research, different air quality parameters were forecasted, including Carbon Monoxide (CO), Nitrogen Monoxide (NO), Nitrogen Dioxide (NO2), Ozone (O3), Sulphur Dioxide (SO2), Fine Particles Matter (PM2.5), Coarse Particles Matter (PM10), and Ammonia (NH3). Hourly datasets were collected for air quality monitoring stations near Delhi, India, from November 25, 2020 to January 24, 2023. In this context, five intelligent models were developed, including Long Short-Term Memory (LSTM), Bidirectional Long-Short Term Memory (Bi-LSTM), Gated Recurrent Unit (GRU), Multilayer Perceptron (MLP), and Extreme Gradient Boosting (XGBoost). The modelling results revealed that Bi-LSTM model had the best predictability performance for forecasting CO with (R2 = 0.979), NO with (R2 = 0.961), NO2 with (R2 = 0.956), SO2 with (R2 = 0.955), PM10 with (R2 = 0.9751) and NH3 with (R2 = 0.971). Meanwhile, GRU and LSTM models performed better in forecasting O3 and PM2.5 with (R2 = 0.9624) and (R2 = 0.973), respectively. The current research provides illuminating visuals highlighting the potential of deep learning to comprehend air quality modeling, enabling improved environmental decisions.

Challenges in Transitioning from Industry 4.0 to Industry 5.0: A Multicriteria Analysis

In this study, we evaluate the transition from Industry 4.0 (I4.0) to Industry 5.0 (I5.0) using a multicriteria approach that integrates the Analytic Hierarchy Process (AHP) and Fuzzy Inference Systems (FISs). Key criteria identified through a comprehensive literature review include sustainability, human factors, resilience and organizational management, which are critical for the successful implementation of I5.0. AHP is utilized to rank these criteria and assign relative weights, reflecting their importance in the overall evaluation. FIS is employed to manage the inherent uncertainty and imprecision in decision-making within the context of I5.0. The results provide a holistic view of how companies navigate this transition, highlighting their strengths and weaknesses. This study offers valuable insights into the challenges and opportunities associated with adopting I5.0, emphasizing the utility of combining multicriteria approaches for strategic decision-making in dynamic industrial environments. These findings aim to support organizations in making informed decisions, fostering a more innovative, sustainable and human-centric industry. The combination of these methodologies provides a robust foundation for future research and practical applications in advancing I5.0.

The Influence of Network Position on Executives’ Preference for Imitation over Innovation: A Scenario-Based Experimental Approach

This study investigates how the different dimensions of a firm’s network position influence executives’ preference for imitation over innovation in new venture development, thus reflecting their preference for uncertainty. Through a scenario-based experimental approach involving 1024 responses from 64 senior executives, we examine how network centrality, structural equivalence, and structural autonomy shape these strategic preferences. The findings reveal that firms with higher network centrality tend to favor innovation over imitation more strongly. At the same time, those in structurally equivalent positions show a greater preference for imitation. Structural autonomy does not significantly affect these preferences. This study contributes to the literature by revealing how different network structures uniquely influence strategic decision-making in uncertain environments, offering valuable insights for scholars and practitioners. The scenario-based experimental method enhances the precision of the findings and provides actionable implications for real-world decision-making in venture development.

How Do They Decide? Performance Support Staff Perceptions of Decision-Making in Elite-Sport Environments.

Wilson, PJ, Roe, G, and Kiely, J. How do they decide? Performance support staff perceptions of decision-making in elite-sport environments. J Strength Cond Res XX(X): 000-000, 2024-Decision-making is a critical component of performance support staff responsibilities, yet research exploring staff perceptions relating to key facets of decision-making is lacking. This study aimed to explore perceptions of performance support staff relating to; the importance of decision-making in their role, the value of team decision-making, strategies to enhance decision-making skills, monitoring the process and outcome of decisions, and the obstacles to good decision-making within their professional contexts. A mixed-methods approached was used to explore perceptions of decision-making in 191 performance support staff (male staff = 179, female staff = 12) working in professional (n = 147) or international (n = 44) sport. Performance support staff reported decision-making as extremely important to their role; however, only 31% had received formal decision-making education and revealed underdeveloped decision-making capabilities within their organizations as a barrier to good decision-making. Most performance support staff conveyed that team decision-making is a valuable endeavor and augments decision outcomes; yet articulated dysfunctional team dynamics as an obstacle to good decision-making in their environments. Largely, performance support staff reported an absence of organizational strategy and workflow systems and processes for tracking and making decisions, respectively, whereas only 22% had a consistent reviewing and updating process. When making decisions, performance support staff faced several different cognitive biases and were challenged by the environmental pressures. The perspectives documented here suggest that performance support staff would likely benefit from undertaking evidence-based training and implementing scientifically informed strategies to enhance decision-making processes within their organizations.