Uncertainty In Values Research Articles

Multi-Channel Phase-Compensated Active Disturbance Rejection Control with an Improved Backstepping Strategy for Electro-Optical Tracking Systems

A multi-channel phase-compensated active disturbance rejection control (MPADRC) incorporating an improved backstepping strategy is proposed in this paper to handle the phase lag in the extended state observer (ESO) and the residual uncertainty in the system. Firstly, a multi-channel phase-compensated ESO (MPESO) is constructed by adding phase-advanced networks to all output channels of the ESO, which allows disturbances and system states to be compensated and feedback in a more timely manner, respectively. Then, to estimate and offset the residual uncertainty in the system, an improved backstepping control method is employed and a Lyapunov function is designed to verify the convergence of the error between the estimated and actual values of the residual uncertainty. After that, the improved backstepping control is combined with MPADRC, and comparisons with the conventional linear active disturbance rejection control (LADRC) are conducted for a range of cases. Finally, on an inertial stabilization platform in the electro-optical tracking system (ETS), simulation and experimental results verified the effectiveness of the proposed method.

Experimental Analysis of the Improvement of Heat Transfer in Tube Heat Exchangers via Passive Flow Generators Using Wire Coil Inserts

Abstract Due to their high performance and low-cost demands, internally treated tube heat exchanger surfaces are one of the passive heat transfer enhancements that have caught the industry's attention. At bulk temperatures of 30 °C, an experiment for the insertion of 1 mm and 0.5 mm wire coils with a constant pitch length of 8 mm was carried out in this study. The results on the improvement of heat transfer, including the velocity profile, Nusselt number, friction factor, and thermal enhancement efficiency, were significant. Based on a lower surface temperature recorded beyond the uncertainty value, the results demonstrated an improvement in heat transfer for smaller diameters of wire coil inserts. Interestingly, this improvement is concentrated at low Reynolds numbers, indicating that there may be a point at which an increase in wire thickness does not necessarily result in an equivalent improvement in heat transfer. For both wire thicknesses, a Nusselt number increase of up to 5 times was observed. The friction factor penalty, however, varies depending on the wire thickness, with a higher magnitude (3.2-fold increase) obtained for 1mm as opposed to a 1.8-fold increase for the lower counterpart. This distinction results in the 0.5 mm coil insert gaining better overall performance with an average of 2.2 for the thermal performance ratio, further solidifying the advantage of this technique for enhancing heat transfer in conduits.

Optical functionality simulation through traceable characterization of optical components

The production of high-value components containing functional micro- and submicron-scale features requires the implementation of high-quality in-process inspection technologies to guarantee zero-defect manufacturing, boosting the transition to digital manufacturing. In the present study, the impact of the measurement uncertainty assigned to surface micro- and submicron-structures on the optical performance of a one-dimensional diffraction grating was determined. Thus, 3D confocal microscopy was selected as an inspection technology, introducing measuring data and uncertainty values on an optical simulation model to evaluate the influence of those magnitudes on the irradiance profile of a light beam at a target plane after passing through the diffraction grating. To achieve this goal, the calibrated areal standards have been used in addition to good practice guides for instrument calibration combined with optical modeling to simulate the functional behavior of the optical device. Results proved that changes of hundreds of nanometers in the lateral dimensions of the grating profile lead to drastic deviations in the irradiance profile and, thus, deviation in the optical performance. Hence a change in the period and the structure width of the step grating to the limits of the calculated uncertainty (Nominal Period ± ux,y) supposes a change of down to a 50 % of decrease in the maximum peak of the irradiance profile detected. Thanks to these results, it is possible to define a range in the grating device's performance depending on the dimensional surface characterization and its uncertainty. Moreover, an in-situ measurement approach has been designed for further product quality control.

Pricing Indefinitely Lived Assets: Experimental Evidence

We study indefinitely lived assets in experimental markets and find that the traded prices of these assets are on average about 40% of the risk-neutral fundamental value. Neither uncertainty about the value of total dividend payments nor horizon uncertainty about the duration of trade can account for this low traded price. An Epstein-Zin recursive preference specification that models the dynamic realization of dividend payments, combined with either probability weighting or subjects’ heterogeneous risk attitudes, can rationalize the low traded prices observed in our indefinitely lived asset market. This paper was accepted by Yan Chen, behavioral economics and decision analysis. Funding: This work was supported by Concordia University and the Bank of Canada. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2021.03059 .

High-field downfield MR spectroscopic imaging in the human brain.

To investigate the feasibility of downfield MR spectroscopic imaging (DF-MRSI) in the human brain at 7T. A 7T DF-MRSI pulse sequence was implemented based on the previously described methodology at 3T, with 3D phase-encoding, spectral-spatial excitation, and frequency selective refocusing. Data were pre-processed followed by analysis using the "LCModel" software package, and metabolite maps created from the LCModel results. Total scan time, including brain MRI and a water-reference MRSI, was 24 min. The sequence was tested in 10 normal volunteers. Estimated metabolite levels and uncertainty values (Cramer Rao lower bounds, CRLBs) for nine downfield peaks were compared between seven different brain regions, anterior cingulate cortex (ACC), centrum semiovale (CSO), corpus callosum (CC), cerebellar vermis (CV), dorsolateral prefrontal cortex (DLPFC), posterior cingulate cortex (PCC), and thalamus (Thal). DF peaks were relatively uniformly distributed throughout the brain, with only a small number of peaks showing any significant regional variations. Most DF peaks had average CRLB<25% in most brain regions. Average SNR values were higher for the brain regions ACC and DLPFC (˜7 ± 0.95, mean ± SD) while in a range of 3.4-6.0 for other brain regions. Average linewidth (FWHM) values were greater than 35 Hz in the ACC, CV, and Thal, and 22 Hz in CC, CSO, DLPFC, and PCC. High-field DF-MRSI is able to spatially map exchangeable protons in the human brain at high resolution and with near whole-brain coverage in acceptable scan times, and in the future may be used to study metabolism of brain tumors or other neuropathological disorders.

Measurement uncertainty from sampling and its role in validation of measurement procedures

It is now widely accepted that the measurement process usually begins when the primary sample is taken. The uncertainty of measurement (MU) must therefore include contributions that arise from the primary sampling, and also from any physical preparation of the sample which often occurs before the sample reaches the laboratory. Guidance on how to estimate MU that includes that arising from sampling (UfS) has been widely applied to a wide range of application sectors (e.g. food, feed, water, sediment, soil, gases). Recent revision of ISO/IEC 17025:2017 (https://www.iso.org/standard/66912.html) has also recognised the inclusion of sampling within the measurement process. This recognition has implications for the validation of measurement procedures that include sampling (VaMPIS). The scope of method (or procedure) validation has therefore to be expanded and reassessed, in order to include all of these components. The uncertainty of the measurement value (MU) is a key parameter that encompasses the effects of all the other operating characteristics of the analytical procedure that is traditionally considered during its validation. It has the further advantage that it can also incorporate the uncertainty due to sampling and physical sample preparation, thus providing a single value of uncertainty that derives from the entire measurement procedure. The fitness for purpose (FnFP) of the whole measurement procedure, which is required for validation, can be judged by comparing the estimated MU (including UfS), against a Target MU, however that is set. A case study for the determination of nitrate in glasshouse lettuce shows how this VaMPIS approach can be applied to a whole measurement procedure. The experimental MU is estimated using the Duplicate Method and compared against a Target MU set using the Optimised Uncertainty (OU) method. The measurement procedure published in EU guidance is shown not to be fit for purpose (FFP). However, this approach identifies how that sampling procedure can be modified to achieve FnFP for the whole procedure, by increasing the number of sample increments per batch from 10 to 40.

Estimating accurate and precise strain in low-magnitude penetratively deformed rocks: Integrating forward strain modeling and natural data

Estimating grain-scale strain using commonly used strain methods from a low-magnitude penetratively deformed rock is typically fraught with uncertainties as the measured strain is influenced by the pre-deformed grain shapes and grain-scale strain partitioning. Therefore, developing kinematic and mechanical models of orogenic wedge evolution based on such strain data can be misleading without knowing how accurate and precise those data are. To determine accuracy we created five 2-D synthetic rock images and deformed them with known strain under pure shear, simple shear, and general shear. We generated 4160 strain ratio (Rs) and long-axis orientation (φ) data. To determine precision we calculated 2σ (95%) uncertainties in Rs and φ from 102 quartz-rich rocks in Sikkim and generated 977 uncertainty data. Results show that the shape matrix eigenvector and stereographic projection methods record the lowest root mean square error, mean absolute error, and 2σ uncertainty values suggesting they are the most accurate and precise, regardless of strain ratio and deformation types. However, the accuracy and precision of φ deteriorate at Rs < 1.5. For most accurate and precise results, using the above mentioned methods, we recommend a minimum of 100 and 75 markers for Rs < 1.5 and Rs > 1.5, respectively.

Values, challenges, and responses associated with high-priced potential cures: perspectives of diverse stakeholders in South Korea

BackgroundThe emergence of high-priced potential cures has sparked significant health policy discussions in South Korea, where the healthcare system is funded through a single-payer National Health Insurance model. We conducted focus group interviews (FGIs) and accompanying surveys with diverse stakeholders to comprehensively understand related issues and find better solutions to the challenges brought by these technologies.MethodsFrom October to November 2022, 11 FGIs were conducted with stakeholders from various sectors, including government payers, policy and clinical experts, civic and patient organisations, and the pharmaceutical industry, involving a total of 25 participants. These qualitative discussions were supplemented by online surveys to effectively capture and synthesise stakeholder perspectives.ResultsAffordability was identified as a critical concern by 84% of stakeholders, followed by clinical uncertainty (76%) and limited value for money (72%). Stakeholders expressed a preference for both financial-based controls and outcome-based pricing strategies to mitigate these challenges. Despite the support for outcome-based refunds, payers raised concerns about the feasibility of instalment payment models, whether linked to outcomes or not, due to the specific challenges of the Korean reimbursement system and the potential risk of ‘cumulative liabilities’ from ongoing payments for previously administered treatments. In addition, the FGIs highlighted the need for clear budgetary limits for drugs with high uncertainties, with mixed opinions on the creation of special silo funds (64.0% agreement). Less than half (48%) endorsed the use of external reference pricing, currently applied to such essential drugs in South Korea. A significant majority (84%), predominantly non-pharma stakeholders, advocated for addressing cost-effectiveness uncertainty through re-assessment once long-term clinical data become available.ConclusionsThis study uncovers a broad agreement among stakeholders on the need for more effective value assessment methodologies for high-priced potential cures, stressing the importance of more robust and comprehensive re-assessment supported by long-term data collection, rather than primarily relying on external reference pricing. Each type of stakeholders exhibited a cautious approach to their specific uncertainties, suggesting that new funding strategies should accommodate these uncertainties with predefined guidelines and agreements prior to the initiation of managed entry agreements.

Marketing strategies in reward-based crowdfunding: The role of demand uncertainties

Reward-based crowdfunding has become an attractive option for entrepreneurs to launch new businesses. This paper examines crowdfunding entrepreneurs’ decision-making facing uncertain demands to shed light on the quality, pricing, and advertising strategies of crowdfunded products. At the core of our analysis are two fundamental mechanisms of reward-based crowdfunding (the All-Or-Nothing, or AON, funding model and ex-ante pricing) and their interactions with two prevalent demand uncertainties in new product development, namely market size uncertainty and consumer valuation uncertainty. Our results show that AON benefits the entrepreneur by safeguarding against both types of demand uncertainty. Ex-ante pricing, however, is harmful through the uncertainty in consumer valuation but not in market size. AON boosts product quality, price, and advertising spending while ex-ante pricing hinders these important marketing decisions. In further examining how market size versus consumer valuation uncertainty affects new product profitability, we find that greater uncertainty in market size can be profit-enhancing. However, the case of consumer valuation uncertainty is more nuanced, featuring a U-shaped impact on profitability. The paper offers insights and managerial implications to entrepreneurs, investors, and crowdfunding platforms.

Application of Forward Chaining Method, Certainty Factor, and Bayes Theorem for Cattle Disease

Indonesia is a country that has many natural resources, especially mammals. The Papua and West Papua regions are large provinces with abundant natural resources and tremendous livestock potential. The availability of natural resources in the form of live cattle provides a great opportunity to develop animal husbandry in West Papua province. This research was conducted to create a new expert system with a knowledge base to solve the problems that occur and be useful for the community, especially cattle breeders. The current problem is the delay and lack of medical personnel in diagnosing cattle diseases, the distance that must be traveled, which is still very difficult to travel, and the lack of understanding of farmers in early handling when implications indicate animals. So, the Certainty Factor Method and Bayes Theorem with Forward-Chaining search are used to handle current problems. From the results of manual calculations, Certainty Factor Forward Chaining search is a method that has an uncertainty value of 99.84% for 3-day fever compared to Bayes Theorem Forward Chaining search with a value of 50% for worms, 50% for 3-day fever and 50% for nail rot, if applied then Certainty Factor Forward Chaining search is the most appropriate. Likewise, updating the knowledge base must be done from time to time. So that in the future, it can be compared with other methods and Android-based to facilitate current breeders.

Self-characterization of irradiation facility using synthetic multi-element standard for determination of k0-factors of seven radionuclides of interest

In this study, the k0 factors of 7 short and medium-lived radionuclides (i.e. 66Cu, 52V, 38Cl, 134mCs, 128I, 140La and 56Mn) were experimentally determined at the Dalat nuclear research reactor (DNRR). The synthetic multi-element standard (SMELS) samples were irradiated at Channel 7-1 installed at the DNRR. SMELS Type III contains Au and Zr, thus also allowing the self-characterization of the irradiation facility (i.e. the direct determination of f and a with only this material). The k0 factors of 7 short and medium-lived radionuclides obtained in this work are a good agreement with the reference k0 factors. The ratio values between the experimental and reference k0 values were 66Cu of 1.004, 52V of 1.004, 38Cl of 0.961 and 0.976 (1642.7 keV and 2167.4keV, respectively), 134mCs of 0.831, 128I of 1.022, 140La of 0.936, 0.955 and 0.955 (328.8 keV, 487.0 keV and 1596.2keV, respectively), 56Mn of 0.985 and 0.973 (846.8 keV and 1810.7keV, respectively). The radionuclides range of uncertainty values were lower than 7%. The present result indicates that the k0 factors can be determined by using synthetic multi-element standards (SMELS).

Empirical Verification of Satellite Data on Solar Radiation and Cloud Cover over the Baltic Sea

Abstract Empirical verification of the reliability of estimating the amount of solar radiation entering the sea surface is a challenging topic due to the quantity and quality of data. The collected measurements of total and diffuse radiation from the Multifilter Rotating Shadowband Radiometer (MRF-7) commercial device over the Baltic Sea were compared with the satellite results of using modeling data. The obtained results, also divided into individual spectral bands, were analyzed for usefulness in satellite cloud and aerosol detection. The article presents a new approach to assessing radiation and cloud cover based on the use of models supported by satellite data. Measurement uncertainties were estimated for the obtained results. To reduce uncertainty, the results were averaged to the time constant of the device, day, and month. The effectiveness of the method was determined by comparison against the SM Hel measurement point. The empirical results obtained confirm the effectiveness of using satellite methods for estimating radiation along with cloud-cover detection over the sea with the adopted uncertainty values. Significance Statement The difference in the amount of solar energy reaching the sea surface between cloudless and cloudy areas reaches tens of percent. Empirical results confirm the effectiveness of using satellite methods to estimate solar radiation along with cloud-cover detection. Over the sea in comparison to land, the amount of empirical data is limited. This research uses new empirical results of radiation to determine the accuracy of satellite estimation results. Experimental results show that the proposed method is effective and adequately parameterizes the detection of satellite image features.

Benchmarking of Various Flexible Soft-Computing Strategies for the Accurate Estimation of Wind Turbine Output Power

This computational study explores the potential of several soft-computing techniques for wind turbine (WT) output power (kW) estimation based on seven input variables of wind speed (m/s), wind direction (°), air temperature (°C), pitch angle (°), generator temperature (°C), rotating speed of the generator (rpm), and voltage of the network (V). In the present analysis, a nonlinear regression-based model (NRM), three decision tree-based methods (random forest (RF), random tree (RT), and reduced error pruning tree (REPT) models), and multilayer perceptron-based soft-computing approach (artificial neural network (ANN) model) were simultaneously implemented for the first time in the prediction of WT output power (WTOP). To identify the top-performing soft computing technique, the applied models’ predictive success was compared using over 30 distinct statistical goodness-of-fit parameters. The performance assessment indices corroborated the superiority of the RF-based model over other data-intelligent models in predicting WTOP. It was seen from the results that the proposed RF-based model obtained the narrowest uncertainty bands and the lowest quantities of increased uncertainty values across all sets. Although the determination coefficient values of all competitive decision tree-based models were satisfactory, the lower percentile deviations and higher overall accuracy score of the RF-based model indicated its superior performance and higher accuracy over other competitive approaches. The generator’s rotational speed was shown to be the most useful parameter for RF-based model prediction of WTOP, according to a sensitivity study. This study highlighted the significance and capability of the implemented soft-computing strategy for better management and reliable operation of wind farms in wind energy forecasting.

Excess cash and equity option liquidity

AbstractWe examine the relation between excess corporate cash holdings and equity option market liquidity from January 3, 2005 to December 31, 2019. We show that the level of cash reserve in excess of what can be captured by firm characteristics significantly explains stock option liquidity. Trading volume and option open interest increase in companies with a higher magnitude of excess cash, whereas the bid–ask spreads of stock options decline in excess cash. Our findings confirm the theoretical prediction that excess cash improves option market liquidity as it reduces adverse selection problems caused by uncertainty in firm valuations. This relation remains more pronounced with put options, out‐of‐the‐money contracts, and short‐maturity contracts. In addition, excess cash has a stronger impact on option liquidity within firms that have a greater degree of informed trading and during high‐volatility periods in financial markets. Our results show that when uncertainty about firm prospects rises, excess cash becomes more valuable and affects option market liquidity.

Propagation of uncertainty for an epipole-dependent model for convergent stereovision structure computation

An analytic model incorporating stereo epipoles is proposed for structure computation using a convergent stereovision setup. The developed model is predicated on the image parameters of both CCD camera sensors, together with two extrinsic parameters, namely the stereo baseline distance and the stereo projection angle of the scene point of interest. In the model, the points on the image planes are measured relative to the principal points, stereo epipoles are featured, and only focal length-normalized camera sensor coordinates are required for structure computation. The reconstruction model could be employed in active vision-based metrology in which the stereo imaging cameras are systematically rotated about their vertical axes relative to each other. The performance of the model is studied, and its accuracy tested by comparing the 3-space coordinates it predicted to the those obtained by a gold standard triangulation and to the ground truth results. In terms of execution speed the proposed reconstruction model exhibited a computation time of 0.6 ms compared to 6.2 ms and 9.9 ms recorded for the direct linear transformation and gold standard triangulation algorithms respectively. The coordinate measurement uncertainties determined by experimental methods are subsequently compared with those obtained by a theoretical approach based on the analytic reconstruction model. Strong correlations were found to exist between the two sets of uncertainty values obtained.

Unlocking the luminescent potential of Pr3+/Yb3+ Co-doped Y2Mo4O15 for advanced thermometry applications

In this study, Pr3+/Yb3+ co-doped Y2Mo4O15 phosphor was successfully synthesized using the traditional citric acid-assisted sol-gel method, with the aim of exploring its potential for optical thermometry applications. The structural and morphological characteristics of the synthesized sample were comprehensively analyzed through X-ray powder diffraction (XRD) and scanning electron microscopy (SEM). Photoluminescence and temperature-dependent luminescence properties were systematically investigated within a temperature range of 298 K to 508 K, employing 473 nm excitation. To assess the temperature sensing capabilities of the phosphors, we utilized both the fluorescence intensity ratio (FIR) and fluorescence lifetime (FL) techniques. Optical temperature measurements were conducted through both thermal and non-thermal coupling methods. Our findings revealed that the FIR (I547/I490) exhibited an outstanding maximum sensitivity of 1.27% K−1, accompanied by remarkably low-temperature uncertainty (δT) values ranging from 0.04 to 0.83 K. This outstanding performance makes it the optimal choice for temperature sensing. Additionally, when employing FL thermometry, the maximum sensitivity (Sr) was determined to be 0.21% K−1 at 538 K. These results strongly indicate the significant potential of Y2Mo4O15: Pr3+/Yb3+ in the realm of optical thermometry, offering promising opportunities for precise and reliable temperature measurements.

Uncertainty-aware deep learning-based CAD system for breast cancer classification using ultrasound and mammography images

ABSTRACT Breast cancer is one of the most common types of cancer in women. Early and accurate diagnosis of breast cancer can increase the treatment chances and decrease the mortality rate. Thus, the development of accurate and reliable Computer-Aided Diagnosis (CAD) systems using breast cancer images is an immediate priority for early diagnosis. Deep learning methods have had a widespread role in CAD systems. In spite of the advantages, most deep learning-based CADs cannot quantify the uncertainty of their predictions. Uncertainty in the predicted results from a CAD system might endanger people’s life in these crucial steps for breast cancer classification. Therefore, in this study, the Monte Carlo Dropout method is utilised as an uncertainty quantification method. Furthermore, differently from most existing studies, we present an uncertainty-aware deep learning model which can classify accurately from different types of images, namely, mammograms and ultrasound. The results show the proposed model achieved the accuracy of 99.95% in mammogram dataset, namely, DDSM and 98.40% and 91.34% in ultrasound datasets, namely, BUSM and BUSI, respectively. Finally, since we found the proposed model shows high uncertainty in misclassifications, we suggested to use the uncertainty values to find appropriate subsets for further inspection to increase the performance.

How much did pandemic uncertainty affect real-estate speculation? Evidence from on-market valuation of for-sale versus rental properties

ABSTRACT We exploit a panel of Zillow Inc. property valuations to estimate the excess real-estate price growth observed in three California cities that is attributable to speculation triggered by the COVID-19 pandemic. Our research design leverages the counterfactual comparison of properties listed for sale to properties listed for rent, with the latter property class being available for habitation – just not for purchase – and thus neutral to price speculation. We implement a pre/post-2020 difference-in-difference estimation, which utilizes unit-level matching of otherwise similar sale and rental properties within a 1/2-mile radius of each other to compare differences in (a) 1-month valuation changes and (b) spot valuation uncertainties. Results indicate post-2020 property valuations in Merced and San Jose featured an excess annual price estimate growth of 22% and 14.8% points, respectively, whereas the Fresno market does not feature statistically significant excess growth.

Verification of uncertainty estimates of autonomous field measurements of marine reflectance using simultaneous observations

The primary ocean color product is the spectrum of remote sensing reflectance RRS that allows the quantification of in-water optically significant constituents and all ocean color applications. The determination of its uncertainties is thus key to the creation of comprehensive uncertainty budgets for all derived ocean color products. The assessment of satellite RRS uncertainties has largely relied on corresponding field measurements but this process is solid only if these field measurements are in turn fully characterized. Uncertainty budgets have therefore been defined and reported for the radiometric measurements collected in the framework of the Ocean Color component of the Aerosol Robotic Network (AERONET-OC). The contemporaneous deployment of two autonomous systems for 5.5 years on the Acqua Alta Oceanographic Tower (AAOT) located in the northern Adriatic Sea led to the collection of 4,449 pairs of coincident observations (collected with a time difference lower than 10 min) distributed over 659 days of data acquisitions that can be used to verify reported uncertainty values. The comparison of matched pairs showed a good agreement for RRS (with differences of typically 2%–3% between 412 and 560 nm), as well as for the aerosol optical thickness τa (3%–6%). Differences between data from the two systems appear generally consistent with their stated uncertainties, indicating that they are metrologically compatible and that uncertainties reported for AERONET-OC data are usually trustworthy (with possible exceptions depending on the level of error correlation between measurements from the two systems). Using uncertainty cone diagrams, this result holds across the range of uncertainty values with few exceptions. Independent uncertainty estimates associated with non-systematic error contributions were obtained using a collocation framework allowing for error correlation between measurements from the two systems. The resulting uncertainties appeared comparable with the reported values for τa and RRS. The related mathematical development also showed that the centered root-mean-square difference between data collected by two systems is a conservative estimate of the uncertainty associated with these data (excluding systematic contributions) if these data show a good agreement (expressed by a slope of method II regression close to 1) and if their uncertainties can be assumed similar with errors moderately correlated (typically lower than 0.5).

Examining an expanded repertoire of finance functions: effects on performance and the moderating role of environmental uncertainty

The literature recognises that the activities of contemporary finance functions are expanding. Guided by the competing values framework, we group the results of previous research into four roles performed by modern finance functions – collaboration, adhocracy, control, and compete. We examine whether finance functions that encompass all four roles have an improved ability to match the right role with the corresponding organisational demand and then test whether this leads to improved financial performance. Empirically, we use structural equation modelling to analyse data from 408 firms operating in Denmark’s manufacturing and services sectors. The results show that fulfilling multiple finance roles is positively related to the ability to assist an organisation with the right activities at the right time and with the right quality. This ability to differentiate behaviour is also positively associated with a return on invested capital and, further, environmental uncertainty positively moderates this effect. The statistical results provide evidence that cultivating multiple roles within the finance function can create value and higher degrees of uncertainty positively leverage this value.