Qualitative Assumptions Research Articles

Digital transition in a turbulent world: European regional growth opportunities in 17 years' time

ABSTRACT Recently, the digital transition has been highlighted as a strategy fostering economic competitiveness. How the advantages from the digital transition will distribute over time and space is still unclear. This paper builds scenarios comparing the effects of a digital transition. Technological assumptions are consistently related to the assumptions on other structural changes. The internal coherence among assumptions is guaranteed by a larger overarching framework based on how the geopolitical context will evolve. A deeper within-EU integration and global cooperation, or a fragmented Europe torn in a world polarized in two blocs, enhance the digital transition and the main structural changes in different ways. Qualitative assumptions, validated through a Delphi analysis, are translated into quantitative ones and included in our MAcroeconometric, Social, Sectoral, Territorial 5 (MASST5) model, a regional macro-econometric forecasting model, through which simulations of GDP growth rates at regional (NUTS2) level are simulated for the period 2021–2038. Results show that if the digital investment plans stimulate growth, they only partially reverse regional growth divergence trends caused by advantages stemming from an integrated market, even if distributed with the aim of closing the digital gap among European countries.

STRATEGIES FOR THE PREVENTION AND CONTROL OF HYPERTENSION IN RURAL COMMUNITIES

Hypertension is a prevalent chronic condition that can lead to serious complications such as cardiovascular disease, stroke and kidney failure. The prevalence of hypertension is especially high in rural communities, where factors such as limited access to health care, lack of health information and socioeconomic conditions can contribute to a lack of control of the disease. Prevention and control strategies tailored to these communities are crucial to reducing the burden of hypertension and improving the overall health of the rural population. This brief explores effective strategies for the prevention and control of hypertension in rural communities, highlighting innovative approaches and associated challenges. The aim of this paper is to review and analyze strategies for the prevention and control of hypertension in rural communities. It aims to identify the main interventions that have proved effective, discuss approaches adapted to the needs and resources of rural communities, and address the challenges faced in implementing these strategies. This is a literature review, using qualitative and quantitative assumptions, using the PubMed, Scopus and Google Scholar databases. The health descriptors “Hypertension”, “Hypertension Management in Rural Areas”, “Telemedicine”, “Community Interventions” and “Rural Health Policies” were used to refine the research. The time frame covers the years 2018 to 2022. The focus is to promote a comprehensive understanding of best practices and areas that need more attention to improve the management of hypertension in rural contexts. Preventing and controlling hypertension in rural communities requires a multifaceted approach that takes into account the particularities of these areas, including barriers to accessing healthcare, resource limitations and specific cultural needs. Education is key to preventing hypertension. Awareness programs about risk factors, the importance of regular blood pressure monitoring and lifestyle changes can have a significant impact. Community initiatives such as workshops, lectures and local media campaigns can help raise awareness of hypertension and promote healthy habits. Improving access to healthcare is crucial for hypertension control in rural areas. This can include the implementation of mobile health units, telemedicine and partnerships with health organizations to provide regular medical services and blood pressure monitoring. Community screening programs and regular consultations can help identify and treat cases of hypertension early. Interventions that encourage a healthy diet, rich in fruit, vegetables and low in sodium, as well as regular physical activity, are essential for the prevention and control of hypertension. Nutritional education programs and the promotion of community physical activities, such as walking groups or sports, can be effective in promoting a healthy lifestyle. Thus, many people with hypertension also have other health conditions, such as diabetes or obesity. Integrated strategies that address hypertension alongside other health conditions can improve overall outcomes. This includes chronic disease management programs that provide support for managing multiple health conditions simultaneously. Involving the community in the planning and implementation of prevention and control strategies can increase the acceptance and effectiveness of interventions. Community groups, local leaders and non-governmental organizations can play an important role in promoting health programs and mobilizing resources. The effective implementation of hypertension control strategies in rural communities faces several challenges. Barriers can include a lack of adequate health infrastructure, a shortage of health professionals, limited access to medicines and treatments, and cultural factors that can affect the acceptance of interventions. In addition, logistical and financial difficulties can limit the ability to implement and maintain health programs in remote areas. Therefore, the approach to preventing and controlling hypertension in rural communities must be adapted to the specific needs and characteristics of these areas. Effective strategies include community education and awareness, improving access to health care, promoting a healthy lifestyle and integrated management of comorbidities. Involving the community and overcoming challenges related to infrastructure and resources are critical aspects for the success of these strategies. With a multifaceted and collaborative approach, it is possible to reduce the prevalence and impact of hypertension in rural communities, promoting better health and an improved quality of life for their inhabitants.

ADVANCES AND CHALLENGES IN MINIMALLY INVASIVE SURGERY FOR THE TREATMENT OF HERNIAS

Minimally invasive surgery has revolutionized the treatment of various medical conditions, offering significant benefits in terms of recovery and fewer complications. In the field of hernia surgery, technological advances and innovative techniques have provided a less invasive approach that minimizes pain, reduces recovery time and improves aesthetic results. However, despite notable advances, there are still challenges and limitations that need to be addressed in order to optimize results and expand the use of these techniques. This summary examines the main advances in minimally invasive surgery for the treatment of hernias and discusses the challenges that remain in this field. The aim of this paper is to review recent advances in minimally invasive surgery for the treatment of hernias, highlighting technological innovations, improved techniques and clinical benefits. It also aims to identify the challenges and limitations associated with these techniques, providing a comprehensive overview of the positive and negative aspects of minimally invasive surgery in current clinical practice. This is a bibliographic review, using qualitative and quantitative assumptions, using the PubMed, Scopus and Web of Science databases. The health descriptors “Abdominal Hernia”, “Minimally Invasive Surgery”, “Hernia Repair”, “Hernia Mesh” and “Technological Advances” were used to refine the research. The time frame covers the years 2015 to 2021. Advances in minimally invasive hernia surgery have been driven by the development of advanced technologies such as laparoscopy and robotics. Laparoscopy, which uses small incisions and a camera to guide the surgery, has established itself as the standard technique for treating inguinal and umbilical hernias. This approach offers several advantages over traditional open surgery, including less post-operative pain, a lower risk of infection, reduced hospitalization time and a faster recovery. Laparoscopy allows the surgeon to accurately visualize and repair the hernia, while the small incisions result in smaller scars and a better aesthetic appearance. Recently, robotic surgery has emerged as a significant innovation in the field of minimally invasive surgery. Robotic systems offer a three-dimensional view and greater precision during the operation, allowing for more delicate and complex maneuvers. This technology is particularly useful in cases of complex or recurrent hernias, where precision and control are critical. Studies have shown that robotic surgery can further reduce post-operative pain and recovery time, although the high cost and need for specialized training are challenges associated with this technology. However, the adoption and effectiveness of minimally invasive techniques for hernias face several challenges. One of the main challenges is the learning curve associated with new technologies. Laparoscopy and robotic surgery require advanced technical skills and extensive training, which can limit access to these techniques in some medical centers and regions. Furthermore, although minimally invasive surgery offers significant advantages, patient selection is crucial. Patients with very large hernias, additional complications or medical conditions that increase surgical risk may not benefit as much from these techniques. Another challenge is cost. The advanced technologies and equipment required for minimally invasive surgery are generally more expensive than traditional approaches. These costs can be a barrier to widespread adoption, especially in settings with limited budgets or where access to cutting-edge technologies is restricted. It is concluded that advances in minimally invasive surgery for the treatment of hernias have provided significant benefits, including less post-operative pain, faster recovery and better aesthetic results. Technologies such as laparoscopy and robotic surgery have revolutionized the surgical approach, offering new opportunities for treatment with less impact on the patient. However, challenges such as the learning curve, high cost and the need for careful patient selection need to be addressed in order to optimize practice and expand access to these techniques. As technology continues to evolve and more data becomes available, minimally invasive surgery is expected to become even more effective and affordable, offering continuous improvements in care and outcomes for hernia patients.

Causal discovery reveals complex patterns of drought-induced displacement

The increasing frequency and severity of droughts present a significant risk to vulnerable regions of the globe, potentially leading to substantial human displacement in extreme situations. Drought-induced displacement is a complex and multifaceted issue that can perpetuate cycles of poverty, exacerbate food and water scarcity, and reinforce socio-economic inequalities. However, our understanding of human mobility in drought scenarios is currently limited, inhibiting accurate predictions and effective policy responses. Drought-induced displacement is driven by numerous factors and identifying its key drivers, causal-effect lags, and consequential effects is often challenging, typically relying on mechanistic models and qualitative assumptions. This paper presents a novel, data-driven methodology, grounded in causal discovery, to retrieve the drivers of drought-induced displacement within Somalia from 2016 to 2023. Our model exposes the intertwined vulnerabilities and the leading times that connect drought impacts, water and food security systems along with episodes of violent conflict, emphasizing that causal mechanisms change across districts. These findings pave the way for the development of algorithms with the ability to learn from human mobility data, enhancing anticipatory action, policy formulation, and humanitarian aid.

TELLTALE SIGN OF CALIBER TEACHERS: SURFEIT OF BRILLIANCE IN TEACHING ELEMENTARY LEARNERS

The teacher is the main actor in the learning process. It is teachers who consider effective learning strategies to be applied. A large number of variations in the quality of teaching are explained by the characteristics of teachers, teachers are central in the process teaching and learning, teachers have a major role in building student personality, teachers are learning managers in a large system of education that enlightens students. This study will lend to the body of knowledge surrounding teacher effectiveness. It is also significant in that it is transferable to a greater population of teachers in kindergarten through sixth grade and therefore may have implications for understanding and improving teacher effectiveness in the greater education population. The researcher made these qualitative assumptions that consist of the methods used in the process of qualitative research (Creswell 2003). The procedures used by the researcher are inductive and are based on the researchers own experience in collecting and analyzing data. The research here is the product of the values of the researcher. The implication for positive social change of this qualitative phenomenological research study was guided by four research questions. Despite the limitations, after the review and ensuing analysis of interview transcripts and observation notes the researcher was able to gain insight into the phenomenon of characteristics of high-quality teachers. It was found that high-quality teachers use a variety of strategies that are carefully chosen based on student needs, student learning styles, and student abilities to meet the needs of diverse learners. Higher-order questioning was identified as a prevalent teaching strategy used in the classrooms of high-quality teachers which resulted in a high level of classroom discussion. A high level of student engagement was also prevalent in the classrooms of high-quality teachers. A quantitative study could be conducted to further investigate the relationship between high-quality teachers and their impact on student achievement about GPA and scores on state standardized tests. A qualitative study could be conducted to determine strategies for facilitating the development and growth of the identified characteristics of high-quality teaching to improve teaching quality KEYWORDS: Telltale Sign, Caliber Teachers, Surfeit of Brilliance, Teaching Elementary Learners

Linear Deconfounded Score Method: Scoring DAGs With Dense Unobserved Confounding.

This article deals with the discovery of causal relations from a combination of observational data and qualitative assumptions about the nature of causality in the presence of unmeasured confounding. We focus on applications where unobserved variables are known to have a widespread effect on many of the observed ones, which makes the problem particularly difficult for constraint-based methods, because most pairs of variables are conditionally dependent given any other subset, rendering the causal effect unidentifiable. In this article, we show that under the principle of independent mechanisms, unobserved confounding in this setting leaves a statistical footprint in the observed data distribution that allows for disentangling spurious and causal effects. Using this insight, we demonstrate that a sparse linear Gaussian directed acyclic graph (DAG) among observed variables may be recovered approximately and propose a simple adjusted score-based causal discovery algorithm that may be implemented with general-purpose solvers and scales to high-dimensional problems. We find, in addition, that despite the conditions we pose to guarantee causal recovery, performance in practice is robust to large deviations in model assumptions, and extensions to nonlinear structural models are possible.

Fractional dynamics and computational analysis of food chain model with disease in intermediate predator

<abstract> <p>In this paper, a fractional food chain system consisting of a Holling type Ⅱ functional response was studied in view of a fractional derivative operator. The considered fractional derivative operator provided nonsingular as well as a nonlocal kernel which was significantly better than other derivative operators. Fractional order modeling of a model was also useful to model the behavior of real systems and in the investigation of dynamical systems. This model depicted the relationship among four types of species: prey, susceptible intermediate predators (IP), infected intermediate predators, and apex predators. One of the significant aspects of this model was the inclusion of Michaelis-Menten type or Holling type Ⅱ functional response to represent the predator-prey link. A functional response depicted the rate at which the normal predator consumed the prey. The qualitative property and assumptions of the model were discussed in detail. The present work discussed the dynamics and analytical behavior of the food chain model in the context of fractional modeling. This study also examined the existence and uniqueness related analysis of solutions to the food chain system. In addition, the Ulam-Hyers stability approach was also discussed for the model. Moreover, the present work examined the numerical approach for the solution and simulation for the model with the help of graphical presentations.</p> </abstract>

Patient satisfaction mediates the influence of trust, service quality and hospital sharia compliance on patient loyalty in Sharia hospitals in Riau province from an Islamic perspective

This study seeks to assess patient satisfaction by examining the impact of trust, service quality, and hospital Sharia compliance on patient loyalty at Sharia hospitals in Pekanbaru City, Riau Province. The study takes an Islamic perspective and aims to enhance competitiveness by focusing on the development of high-quality and dependable services. The research uses structural equation modeling, a statistical methodology that combines quantitative data and qualitative causal assumptions to assess and estimate causal links. The study employed a questionnaire to gather primary data from 342 respondents, including patients. The research findings are as follows: (1) Trust has a positive and significant impact on patient loyalty. (2) Service quality does not have a significant impact on patient loyalty. (3) Sharia compliance has a positive and significant impact on patient loyalty. (4) Trust has a positive and significant impact on patient satisfaction. (5) Service quality has a positive and significant impact on satisfaction. (6) Sharia compliance has a positive and significant impact on satisfaction. (7) Satisfaction positively and significantly mediates the effect of trust on loyalty. (8) Satisfaction positively and significantly mediates the influence of service quality on patient loyalty. (9) Satisfaction positively and significantly mediates the influence of Sharia compliance on loyalty. (10) Satisfaction has a positive and significant influence on loyalty.

On the antimaximum principle for the p-Laplacian and its sublinear perturbations

We investigate qualitative properties of weak solutions of the Dirichlet problem for the equation $$-\Delta _p u = \lambda \,m(x)|u|^{p-2}u+ \eta \,a(x)|u|^{q-2}u+ f(x)$$ in a bounded domain $$\Omega \subset \mathbb {R}^N$$ , where $$q<p$$ . Under certain regularity and qualitative assumptions on the weights m, a and the source function f, we identify ranges of the parameters $$\lambda $$ and $$\eta $$ for which solutions satisfy maximum and antimaximum principles in weak and strong forms. Some of our results, especially on the validity of the antimaximum principle under low regularity assumptions, are new for the unperturbed problem with $$\eta =0$$ , and among them there are results providing new information even in the linear case $$p=2$$ . In particular, we show that for any $$p>1$$ solutions of the unperturbed problem satisfy the antimaximum principle in a right neighborhood of the first eigenvalue of the p-Laplacian provided $$m,f \in L^\gamma (\Omega )$$ with $$\gamma >N$$ . For completeness, we also investigate the existence of solutions.

Graphical causal modelling: an application to identify and estimate cause-and-effect relationships

ABSTRACT This paper offers an accessible discussion of graphical causal models and how such a framework can be used to help identify causal relations. A graphical causal model represents a researcher’s qualitative assumptions. As a result of the credibility revolution, there is growing interest to properly estimate cause-and-effect relationships. Using several examples, we illustrate how graphical models can and cannot be used to identify causation from observational data. Further, we offer a replication of a previous study that explored college enrolment by high school seniors who were eligible for student aid. From the original study, we use a graphical causal model to motivate the quantitative and qualitative modelling assumptions. Using a similar difference-in-difference approach based on propensity score matching, we estimate a smaller average treatment effect than the original study. The smaller estimated effect arguably stems from the graphical causal model’s delineation of the original model specification.

Predicting Highly Enantioselective Catalysts Using Tunable Fragment Descriptors.

Catalyst optimization processes typically rely on inductive and qualitative assumptions of chemists based on screening data. While machine learning models using molecular properties or calculated 3D structures enable quantitative data evaluation, costly quantum chemical calculations are often required. In contrast, readily available binary fingerprint descriptors are time- and cost-efficient, but their predictive performance remains insufficient. Here, we describe a machine learning model based on fragment descriptors, which are fine-tuned for asymmetric catalysis and represent cyclic or polyaromatic hydrocarbons, enabling robust and efficient virtual screening. Using training data with only moderate selectivities, we designed theoretically and validated experimentally new catalysts showing higher selectivities in a challenging asymmetric tetrahydropyran synthesis.

Predicting Highly Enantioselective Catalysts Using Tunable Fragment Descriptors**

AbstractCatalyst optimization processes typically rely on inductive and qualitative assumptions of chemists based on screening data. While machine learning models using molecular properties or calculated 3D structures enable quantitative data evaluation, costly quantum chemical calculations are often required. In contrast, readily available binary fingerprint descriptors are time‐ and cost‐efficient, but their predictive performance remains insufficient. Here, we describe a machine learning model based on fragment descriptors, which are fine‐tuned for asymmetric catalysis and represent cyclic or polyaromatic hydrocarbons, enabling robust and efficient virtual screening. Using training data with only moderate selectivities, we designed theoretically and validated experimentally new catalysts showing higher selectivities in a challenging asymmetric tetrahydropyran synthesis.

A Simulation Tool for Physics-Informed Control of Biomimetic Soft Robotic Arms

Due to an infinite number of degrees of freedom, soft robotic arms remain challenging to control when underactuated. Past work has drawn inspiration from biological structures–for example the elephant trunk–to design and control biomimetic soft robotic arms. However, to date, the models used to inform the control of biomimetic arms lack generalizability, and largely rely on qualitative assumptions. Here, we present a computationally efficient methodology to control fiber-based slender soft robotic arms inspired by the theory of active filaments. Our approach seeks to optimize fibrillar activation under prescribed control objectives. We evaluate the methodology under various control objectives, and consider several distinct fiber architectures. Our results suggest that we can efficiently compute fibrillar activations required to match the imposed control objective. Based on our findings, we discuss the effect of actuator complexity on actuation capabilities as a function of the number and arrangement of fibers. Our method can be applied universally towards the control and design of slender soft robotic arms with embedded fibers.

Implications of Endogenous Money Growth for Some Tests of Superneutrality and the Fisher Effect

Superneutrality of money and the Fisher Effect are well-known theoretical propositions. Empirical tests of long-run versions of these hypotheses have sometimes been done by estimating how a variable responds to a permanent shock to inflation. Substituting inflation for money growth in a test for superneutrality is motivated by the widely-accepted Monetarist precept that “inflation is everywhere and always a monetary phenomenon.” Use of permanent shocks to inflation and money growth for testing such hypotheses has declined, in part because permanent movements in these variables have an endogenous component and so estimates are biased. But the sign of the bias may be determined using credible qualitative assumptions about the effects of structural shocks on variables. These results are used to re-examine multi-country findings from two di˙erent structural VAR models that estimate the effects of permanent inflation shocks. One finding is rejection of superneutrality for output in favor of a long-run positive output effect from permanently higher money growth. The second is rejection of the Fisher Effect in favor of nominal rates moving less than one-for-one in the long run with inflation. Both rejections are shown to be robust to endogenous money growth bias under a wide range of plausible structural assumptions. These results for real interest rates and output provide evidence in support of structural models which give rise to a Mundell-Tobin effect.

AN EXTENSION OF THE MABAC AND OS MODEL USING LINGUISTIC NEUTROSOPHIC NUMBERS: SELECTION OF UNMANNED AIRCRAFT FOR FIGHTING FOREST FIRES

The paper presents a new approach to the treatment of uncertainty and subjectivity in the decision-making process based on the modification of Multi-Attributive Border Approximation area Comparison (MABAC) and an Objective–Subjective (OS) model by applying Linguistic Neutrosophic Numbers (LNN) instead of traditional numerical values. By integrating these models with LNN it was shown that it is possible to a significant extent to eliminate subjective qualitative assessments and assumptions by decision makers in complex decision-making conditions. On this basis, a new hybrid LNN–OS–MABAC model was formed. This model was tested and validated on a case-study in which the optimal unmanned aircraft were selected to combat forest fires. After defining the criteria and their attributes, they were prioritized using the LNN–OS model, in which the weights of the criteria and their attributes are a combination of the objective values obtained by the method of maximum deviation and the subjective values of the criteria obtained by expert examination using LNN. The ranking and selection of the optimal unmanned aircraft from those on offer with similar characteristics was carried out using the LNN–MABAC model. Testing of the model showed that the proposed model based on LNN provides an objective expert evaluation by eliminating subjective assessments when determining the numerical values of criteria. A sensitivity analysis of the LNN–OS–MABAC model, carried out through 54 scenarios of changes in the weight coefficients, showed a high degree of stability in the solutions obtained when the alternatives were ranked. The results were validated by comparison with LNN extensions of the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) model.

Best Operating Conditions for Biogas Production in Some Simple Anaerobic Digestion Models

We consider one-step and two-step simple models of anaerobic digestion that are able to adequately capture the main dynamical behaviour of the full anaerobic digestion model ADM1. We do not consider specific growth functions. We only require them to satisfy certain qualitative assumptions. These assumptions are satisfied for concave growth functions, but they are also satisfied for a large class of growth functions found in many applications. We consider the maximisation of the biogas production with respect to the operating parameters of the model, which are the dilution rate and the substrate input concentration. We give the best operating conditions and we describe them as a subset of the set of operating parameters. Our models incorporate biomass decay terms, corresponding to maintenance. Numerical plots with specified growth functions and biological parameters illustrate the obtained results.

Introduction to the special section: Questioning established qualitative methods and assumptions.

Introduction to the special section: Questioning established qualitative methods and assumptions.

Accounting for internal migration in spatial population projections—a gravity-based modeling approach using the Shared Socioeconomic Pathways

Gridded population projections constitute an essential input for climate change impacts, adaptation, and vulnerability (IAV) assessments as they allow for exploring how future changes in the spatial distribution of population drive climate change impacts. We develop such spatial population projections, using a gravity-based modeling approach that accounts for rural-urban and inland-coastal migration as well as for spatial development patterns (i.e. urban sprawl). We calibrate the model (called CONCLUDE) to the socioeconomically diverse Mediterranean region, additionally considering differences in socioeconomic development in two geographical regions: the northern Mediterranean and the southern and eastern Mediterranean. We produce high-resolution population projections (approximately 1 km) for 2020–2100 that are consistent with the Shared Socioeconomic Pathways (SSPs), both in terms of qualitative narrative assumptions as well as national-level projections. We find that future spatial population patterns differ considerably under all SSPs, with four to eight times higher urban population densities and three to 16 times higher coastal populations in southern and eastern Mediterranean countries compared to northern Mediterranean countries in 2100. In the South and East, the highest urban density (8000 people km−2) and coastal population (107 million) are projected under SSP3, while in the North, the highest urban density (1500 people km−2) is projected under SSP1 and the highest coastal population (15.2 million) under SSP5. As these projections account for internal migration processes and spatial development patterns, they can provide new insights in a wide range of IAV assessments. Furthermore, CONCLUDE can be extended to other continental or global scales due to its modest data requirements based on freely available global datasets.

Adesão e aderência à prática do boxe em uma academia do Sul de Minas Gerais

Atualmente, o boxe apresenta-se como uma forma moderna de prática corporal no interior das academias, com o acréscimo de elementos da ginástica e do treinamento funcional. Nesse contexto, nossa pesquisa teve como objetivo compreender quais são os fatores relacionados à adesão e aderência dessa prática corporal em uma academia do Sul de Minas Gerais. Para tanto, realizou-se uma pesquisa assentada nos pressupostos qualitativos sob um desenho descritivo, valendo-se de entrevista semiestruturada junto a dez praticantes. Concluímos que vários aspectos estão relacionados à adesão e a aderência à prática de boxe, porém, alguns se destacaram nas narrativas. No que se refere à adesão, os motivos de maior destaque foram: a busca pela prática para promoção da saúde; a disponibilidade de horários das aulas; à paixão pelo boxe; e o aprimoramento da técnica de luta para variados fins, bem como, o incentivo da família e dos amigos. Já, em relação à aderência, os motivos mais mencionados foram: a motivação e a variabilidade de treinamento; a disponibilidade de horários de aulas e as interações sociais no interior da academia que, no caso específico, contribuem para a construção de uma identidade coletiva no sentido do pertencimento ao grupo.

Financial management and phenomenology: The role of dialogue, accountability and context in investment decisions

In this article, it will be argued that subjective assumptions play a prominent part in the way valuations are conducted and investment decisions are made by financial managers (FMs) from the perspective of agency theory. The problem is that there is a general absence of rules of compliance for financial management. Quantitative calculations are based on theories, models and accepted practices that guide FMs in their decision-making process. The selection criteria that inform these theories, models and practices rely on qualitative assumptions, which are informed by the presuppositions of the FM. The problem is that this understanding framework has traditionally not been assessed because of the assumption that the theories, models and practices are value neutral. Value neutrality generally implies that calculations, processes and projections are based on factual information and rational procedures that are objective. The scrutiny of the assumptions involved in financial management assumes that the individual has philosophical sensitivity. However, this is not traditionally part of the expected competencies of finance professionals. Philosophical competency means that the FM is capable of evaluating decision-making processes in order not to fall into the trap of circular logic or solipsism as highlighted by Gadamer. Maurice Merleau-Ponty identified that the limitations of such an understanding of reality result in a failure of responsibility and accountability. From the philosophy of Merleau-Ponty, there are three principles for philosophical analysis, namely, dialogue, accountability and context. These principles are applied to the purchase of Gourmet Burger Kitchen by Famous Brands in 2016.