Critical Decisions Research Articles

Balancing Playability and Historical Accuracy in Game Design - Developing the Struve Geodetic Arc Mobile Game

Numerous digital games and media with historical themes aim to educate or engage audiences with heritage. When developing a historical video game, some of the most critical design decisions are centered around finding the balance between an accurate presentation of historical facts and engaging gameplay. This paper examines the game developer's role in selecting and presenting historical content through a case study of a mobile game development process. The design choices and developers' positions related to using historical facts are examined through thematic analysis of interview data and game design documents. Recurring themes in the data reveal a constant interplay between game features and historical content, supported by historical knowledge and technology. In small teams, individual developers often make crucial design decisions while performing multiple roles. As key findings, we report that the resourcing and timing of historical research significantly impacts developers' decisions, and note varying degrees of interest in historical accuracy within the development team.

Exploring the predictor of teaching quality using the job demands-resources model

Introduction. Maintaining teaching quality in higher education in Indonesia is challenging due to the significant job demands placed on lecturers, who are expected to fulfill multiple roles as educators, researchers, and mentors.Aim. The present research aimed to explore the potential of using the job demands-resources (JD-R) model to evaluate the quality of teaching in higher education.Methodology and research methods. This study employs structural equation modelling (SEM) using SmartPLS 3.0 software to analyse the research model based on a random sample of 285 lecturers from nineteen universities in Indonesia.Results. It has been established that role conflict among lecturers negatively impacts teaching quality, whereas self-efficacy positively influences it. The paper presents methods for utilising the JD-R model to examine factors related to the performance of higher education teachers.Scientific novelty. This study, utilising the capabilities of the JD-R model, identifies factors that influence teaching quality and presents mechanisms that underlie the relationship between role conflict and the level of self-efficacy in teaching performance among high school educators in Indonesia.Practical significance. The paper out-lines the opportunities available to university administrations and the Indonesian Ministry of Education, Culture, Research, and Technology for making critical management decisions related to teaching quality. This includes restructuring the educational process to reduce the burden on teachers and expanding the methods used to assess their performance. These goals can be accomplished through the development of innovative organisational support systems.

Exploring the applicability of effectuation theory in distressed venture environments

Orientation: Once appointed, business rescue practitioners and turnaround professionals are faced with critical decision making of whether a distressed venture contains an opportunity as judged by reasonable prospect.Research purpose: This study investigated and explored the application of ‘effectuation theory’ principles and/or causation elements to the distressed venture opportunity (DVO) decision making.Motivation for the study: Effectuation is applied by experts to an entrepreneurial opportunity, and it was important to investigate whether effectuation and its principles would inform practitioners for better decision making when evaluating a DVO.Research design, approach and method: A qualitative study was conducted with 15 business rescue practitioners (BRPs) from the Gauteng province. The data were collected through in-depth semi-structured interviews and a thematic analysis was used to analyse the data.Main findings: This study reveals that all five effectuation principles and some causation elements are applicable to the DVO judgement. The findings indicate that both effectuation and causation are applied in a complementary manner depending on the context.Practical and/or managerial implications: This study highlights the importance of applying effectuation and causation elements for practitioners to achieve success. By applying both logics, BRPs can build resilience in their strategies, be creative in adapting to solutions as they arise and engage with stakeholders in the business rescue process.Contribution and/or value add: This study extended the application of effectuation theory to distressed venture environments, offering insights into how practitioners can make better decisions when evaluating a DVO.

Selection of Health Insurance Policy: Using Analytic Hierarchy Process and Combined Compromised Solution Approach Under Spherical Fuzzy Environment

The process of health insurance policy selection is a critical decision with far–reaching financial implications. The complexity of health insurance policy selection necessitates a structured approach to facilitate informed decision-making amidst numerous criteria and provider options. This study addresses the health insurance policy selection problem by employing a comprehensive methodology integrating Spherical Fuzzy Analytic Hierarchy Process (SF–AHP) and Combined Compromise Solution (CoCoSo) Algorithm. Eight experienced experts, four from academia and industry each, were engaged, and eleven critical factors were identified through literature review, survey, and expert opinions. SF–AHP was utilized to assign weights to these factors, with Claim settlement ratio (C9) deemed the most significant. Subsequently, CoCoSo Algorithm facilitated the ranking of insurance service providers, with alternative A6 emerging as the superior choice. The research undertakes sensitivity analysis, confirming the stability of the model across various scenarios. Notably, alternative A6 consistently demonstrates superior performance, reaffirming the reliability of the decision-making process. The study’s conclusion emphasizes the efficacy of the joint SF–AHP and CoCoSo approach in facilitating informed health insurance policy selection, considering multiple criteria and their interdependencies. Practical implications of the research extend to individuals, insurance companies, and policymakers. Individuals benefit from making more informed choices aligned with their healthcare needs and financial constraints. Insurance companies can tailor policies to customer preferences, enhancing competitiveness and customer satisfaction. Policymakers gain insights to inform regulatory decisions, promoting fair practices and consumer protection in the insurance market. This study underscores the significance of a structured approach in navigating the intricate health insurance landscape, offering practical insights for stakeholders and laying a foundation for future research advancements.

Guidelines for Bibliometric‐Systematic Literature Reviews: 10 steps to combine analysis, synthesis and theory development

AbstractThe steady increase in academic production has been paralleled by a surge in the number of bibliometric and systematic literature reviews (SLRs) published. Over the years, scholars began to combine bibliometric analyses with SLRs. However, such combined approaches relied on fragmented methodological suggestions without clear guiding frameworks. This article introduces integrated guidelines for undertaking multi‐method literature reviews, combining bibliometric analyses with SLRs and theory development, which we call ‘Bibliometric‐Systematic Literature Review’ (B‐SLR). In doing so, we develop a 10‐step process on how to apply the B‐SLR. In each of the proposed steps, we discuss critical decisions and best practices to support researchers while crafting meaningful and theoretically relevant literature reviews. The B‐SLR is intended as a flexible toolbox designed to accommodate diverse research objectives in the miner–prospector continuum, spanning from reviewing, theorising, tracing future roadmaps or creating bridges among different topics. The B‐SLR incorporates the pillars of critical analysis, timeliness, coverage, rigour, coherence and originality of contribution, also emphasising the need for a novel and relevant theoretical contribution. The B‐SLR is supported by a companion website, providing additional resources to assist researchers in this 10‐step process: https://www.b‐slr.org.

Bangalore International Airport Limited: A Hub for the Future

Bangalore International Airport Limited (BIAL) has been operating the Kempegowda International Airport near Bengaluru, India, since 2008. Bengaluru, the capital of Karnataka, is a major hub for startups and the IT industry. The COVID-19 pandemic significantly impacted the aviation industry, including BIAL, leading to a substantial decline in passenger traffic and cargo handling, weakening profitability. Mr Agarwal, the Corporate Strategist for BIAL, has been tasked with developing new strategies to address the various challenges the company faces. These challenges include the impending expiration of a concession agreement, decreasing non-aeronautical revenues and making critical strategic decisions related to infrastructure development. This case allows students to explore strategic decision-making using the Ansoff matrix. It encourages them to identify the key pillars of airport business growth that are internationally recognized, which can help BIAL expand its business portfolio, meet increasing demand, ensure infrastructure development in the right direction and achieve sustainability over the next 30 years.

A comparative study on machine learning approaches for rock mass classification using drilling data

Current rock engineering design in drill and blast tunnelling primarily relies on engineers' observational assessments. Measure While Drilling (MWD) data, a high-resolution sensor dataset collected during tunnel excavation, is underutilised, mainly serving for geological visualisation. This study aims to automate the translation of MWD data into actionable metrics for rock engineering. It seeks to link data to specific engineering actions, thus providing critical decision support for geological challenges ahead of the tunnel face. Leveraging a large and geologically diverse dataset of ∼500,000 drillholes from 15 tunnels, the research introduces models for accurate rock mass quality classification in a real-world tunnelling context. Both conventional machine learning and image-based deep learning are explored to classify MWD data into Q-classes and Q-values—examples of metrics describing the stability of the rock mass—using both tabular- and image data. The results indicate that the K-nearest neighbours algorithm in an ensemble with tree-based models using tabular data effectively classifies rock mass quality. It achieves a cross-validated balanced accuracy of 0.86 in classifying rock mass into the Q-classes A, B, C, D, E1, E2, and 0.95 for a binary classification with E versus the rest. Classification using a CNN with MWD-images for each blasting round resulted in a balanced accuracy of 0.82 for binary classification. Regressing the Q-value from tabular MWD-data achieved cross-validated R2 and MSE scores of 0.80 and 0.18 for a similar ensemble model as in classification. High performance in regression and classification boosts confidence in automated rock mass assessment. Applying advanced modelling on a unique dataset demonstrates MWD data's value in improving rock mass classification accuracy and advancing data-driven rock engineering design, reducing manual intervention.

Strategic configurations: exploring MNEs’ commitment reduction in an emerging economy

AbstractCommitment reduction in the foreign investment process is a critical strategic decision for multinational enterprises (MNEs). Recognizing the complex nexus of drivers for MNE commitment reduction in an emerging economy, this study investigates the configurations of resource, industrial, and institutional factors that collectively influence the strategic decision of commitment reduction. We incorporated the strategic tripod framework to reveal the interactive patterns among various sets of factors influencing the investment reduction decisions of MNEs. Using fuzzy-set qualitative comparative analysis (fsQCA), we analyzed foreign investment reduction by MNEs operating across nineteen industries in China between 2013 and 2019. Our findings identify configurational solutions leading to four propositions, applying the strategic tripod to explain MNE commitment reduction decisions. This study concludes with important theoretical and practical implications, suggesting ideas for pushing the research landscape further.

Value of Factor V in the diagnosis of early graft dysfunction after liver transplantation: Internal validation.

Primary graft dysfunction is a major early complication following liver transplantation, potentially leading to retransplantation or patient death. Coagulation Factor V (FV) and ALT have emerged as important biomarkers in assessing liver function, yet their role as early predictors of graft loss has not been fully validated. The aim of this study is to conduct an internal validation of published results on the applicability of FV and ALT for diagnosing graft dysfunction and its predictive ability for graft loss within the first 90 days. We conducted a retrospective cohort study including 513 adult recipients from 2012 to 2023 at the Regional University Hospital of Málaga. FV and ALT levels were measured on postoperative day 2, and patients were categorized based on FV <37.5 and ALT >1539. The association with 90-day graft loss was analyzed. Graft loss occurred in 43 patients (8.4%) within the first 90 days. The combination of FV <37.5 and ALT >1539 on postoperative day 2 demonstrated a specificity of 99% and a test efficiency of 94% in predicting graft loss. Patients meeting both criteria had a 74-fold increased risk of graft loss, with most losses occurring within the first week, and a median survival of 4 days. These findings suggest that FV and ALT on postoperative day 2 are reliable early markers for predicting graft loss, enabling risk stratification and guiding critical decisions regarding early retransplantation in the immediate postoperative period.

An AI-Based Approach for Developing a Recommendation System for Underground Mining Methods Pre-Selection

Selecting the most appropriate mining method to recover mineral resources is a critical decision-making task in mining project development. This study introduces an artificial intelligence-based mining methods recommendation system (AI-MMRS) for the pre-selection of underground mining methods. The study integrates and evaluates the capability of two approaches for mining methods selection (MMS): the memory-based collaborative filtering (CF) approach aided by the UBC-MMS system to predict the top-3 relevant mining methods and supervised machine learning (ML) classification algorithms to enhance the effectiveness and novelty of the AI-MMRS, addressing the limitations of the CF approach. The results reveal that the memory-based CF approach achieves an accuracy ranging from 81.8% to 87.9%. Among the classification algorithms, artificial neural network (ANN) and k-nearest neighbors (KNN) classifiers perform the best, with accuracy levels of 66.7% and 63.6%, respectively. These findings demonstrate the effectiveness and viability of both approaches in MMS, acknowledging their limitations and the need for continuous training and optimization. The proposed AI-MMRS for the pre-selection stage supplemented by the direct involvement of mining professionals in later stages of MMS, has the potential to significantly aid in the MMS decision-making, providing data-driven and experience-based recommendations following the ongoing evolution of mining practices.

Early Versus Late Initiation of Dialysis in CKD Stage 5: Time for a Consensus

Chronic kidney disease (CKD), a major global public health problem, emerged as one of the leading causes of death, affecting over 800 million individuals worldwide, with significant burden to patients and their caregivers, and may lead to end-stage kidney disease (ESKD). The decision on optimal initiation of chronic dialysis is a common problem faced by nephrologists, patients and caregivers due to lack of adequate data. Determining the ideal time to initiate maintenance dialysis for individuals struggling with ESKD has remained a puzzle.Currently, there is no consensus among guidelines as to the best time to initiate dialysis. Discrepancies in guidelines stem from lack of adequate data, necessitating larger randomized controlled trials to fill this major gap and come to a universal acceptance by the nephrology community about the optimal time to initiate dialysis for patients with advanced CKD. The fact that there has been only one randomized controlled trial that addressed early vs. late dialysis initiation is inadequate to convincingly answer such as critical clinical decision making.In this review, we will analyze the available literature and try to come with recommendations for further studies to guide nephrologist to the best time to initiate dialysis for patient approaching ESKD.

RECOMMENDATIONS TO DEVELOP TEACHING METHODS IN MEDICAL SCIENCES

The rapid advancements in medical sciences necessitate continuous updates in teaching methods to ensure that healthcare professionals are adequately prepared to meet the demands of modern medicine. Traditional lecture-based teaching, though foundational, is increasingly supplemented by innovative pedagogical strategies designed to enhance student engagement, critical thinking, and practical skills. This paper outlines key recommendations for developing effective teaching methods in medical sciences, emphasizing the integration of technology, active learning, and interprofessional education. First, the incorporation of simulation-based learning and virtual reality can provide immersive, hands-on experiences, allowing students to practice clinical skills in a safe, controlled environment. Second, the use of problem-based learning (PBL) and case-based discussions promotes critical thinking and clinical reasoning, shifting the focus from rote memorization to the application of knowledge in real-world scenarios. Third, flipped classrooms—where students engage with theoretical content before class and use classroom time for discussion and problem-solving—foster active learning and student-centered education. Additionally, incorporating interprofessional education encourages collaboration among medical, nursing, and allied health students, reflecting the team-based approach required in modern healthcare settings. Finally, continuous assessment and feedback, facilitated by digital tools, allow for personalized learning pathways and early identification of knowledge gaps, enabling targeted interventions. In conclusion, adopting a multi-faceted approach to teaching in medical sciences—one that embraces technology, active learning, and collaborative education—can improve both the learning experience and the competency of future healthcare professionals. These recommendations aim to produce graduates who are not only knowledgeable but also skilled in critical thinking, decision-making, and teamwork, ultimately improving patient care outcomes.

The influence of institutional void and socio-cultural factors on the internationalization of emerging multinationals

The process of firm internationalization involves critical decisions regarding location, entry mode, investment levels, and the management of foreign operations. This paper examines the effects of institutional voids and socio-cultural factors on the internationalization of Emerging Multinationals (EMNCs) from the BRICS and MENA regions. We draw on institutional and internationalization theories to explain how socio-cultural factors influence the internationalization of EMNCs. Using the two-step system GMM over a ten-year period (2012–2021), we find that institutional voids, religious diversity, power distance, and a firm’s ESG practices at home positively impact internationalization. These findings contribute to the literature by quantifying key variables related to internationalization, cultural diversity, institutional voids, power distance, and ESG engagement.

Explainable AI (XAI) in healthcare: Enhancing trust and transparency in critical decision-making

The integration of artificial intelligence (AI) in healthcare is revolutionizing diagnostic and treatment procedures, offering unprecedented accuracy and efficiency. However, the opacity of many advanced AI models, often described as "black boxes," creates challenges in adoption due to concerns around trust, transparency, and interpretability, particularly in high-stakes environments like healthcare. Explainable AI (XAI) addresses these concerns by providing a framework that not only achieves high performance but also offers insight into how decisions are made. This research explores the application of XAI techniques in healthcare, focusing on critical areas such as disease diagnostics, predictive analytics, and personalized treatment recommendations. The study will analyse various XAI methods, including model-agnostic approaches (LIME, SHAP), interpretable deep learning models, and domain-specific applications of XAI. It also evaluates the ethical implications, such as accountability and bias mitigation, and how XAI can foster collaboration between clinicians and AI systems. Ultimately, the goal is to create AI systems that are both powerful and trustworthy, promoting broader adoption in the healthcare sector while ensuring ethical and safe outcomes for patients.

Leveraging AWS and Java Microservices: An Analysis of Amazon\'s Scalable E-commerce Architecture

Abstract: This article presents a comprehensive case study of Amazon's journey in building a robust and scalable e-commerce platform capable of handling millions of daily transactions while maintaining high availability and performance. We examine the critical architectural decisions that facilitated Amazon's transition from a monolithic structure to a microservices-based architecture, leveraging Java and various AWS cloud services. The article explores key components of Amazon's scalable infrastructure, including the implementation of DynamoDB for high-performance database needs and the use of Elastic Load Balancing to ensure fault tolerance. We analyze the challenges encountered during this transformation and the solutions developed to address them. The article also discusses the resulting improvements in scalability, reliability, and cost-efficiency that have contributed to Amazon's position as the world's largest online retailer. Our findings provide valuable insights into best practices for architecting large-scale, cloud-native e-commerce platforms and offer implications for future developments in the field of distributed systems and cloud computing

A Comprehensive Security Risk Assesment Framework SRAF_IoV for Internet of Vehicles.

The advancement in digital world have led to the development of smart vehicles that connect to the Internet of Vehicles (IoV) Infrastructure. These smart vehicle also connects and communicate with other vehicles, and their connected components. Once connected, they can share information such as speed, direction, and distance between vehicles.IoV enhances traffic infrastructure by reducing congestion and incidents, ultimately improving road safety within highways and metropolitan cities. However, when smart vehicles use IoV to share data through vehicle-to-vehicle (V2V), Vehicle to Infrastructure (V2I), and Vehicle to everything (V2X) they also introduce security risks and vulnerabilities to the IoV Infrastructure. Malicious actors can exploit these vulnerabilities to alter data within the connected units of IoV infrastructure, such as electronic control units, onboard units, control networks, and sensors. Securing data within the IoV ecosystem is crucial to prevent malicious activity that could compromise confidentiality, integrity, and availability. In this study, a security risk assessment framework for IoV (SRAF-IoV) has been developed to identify vulnerabilities within the IoV network. SRAF-IoV that assesses security risks in connected vehicles. It evaluates vulnerabilities and assigns a risk level based on different communication channels (V2V, V2I, and V2X). The proposed framework was tested against various cyber-attacks to determine risk levels associated with different attack types (low, medium, high, and critical). The performance of SRAF-IoV was then compared to existing frameworks. By comprehensively identifying potential risks, SRAF-IoV was then compared to existing frameworks. By comprehensively identifying potential risks, SRAF-IoV can push the smart vehicle industry to make critical security decisions during the development of smart vehicles.

The illness severity scores can predict the need for respiratory and inotropic support in newborns with critical congenital heart malformations

Background and objectives. The illness severity scoring systems have been developed and validated to assess mortality risks based on clinical conditions and aid critical patient decision-making. Dextro-transposition of the great arteries (D-TGA) and coarctation of the aorta (CoA) are critical congenital heart defects that require medical treatment from birth and surgical repair in the neonatal period. The study aimed to compare the values of three scoring systems in predicting the need for respiratory and inotropic support for newborns with D-TGA and CoA. Materials and methods. An observational study was conducted on inborn infants diagnosed with D-TGA and CoA admitted to the newborn intensive care unit (NICU) within 12 hours of birth at a referral unit for congenital heart disease between January 2018 and December 2020. Newborns with D-TGA and CoA who died within the first 24 hours of birth, outborn infants, and those with a late diagnosis were excluded. For each patient, the Clinical Risk Index of Babies (CRIB-II), Score for Neonatal Acute Physiology-II (SNAP-II), and Score for Neonatal Acute Physiology-Perinatal Extension-II (SNAPPE-II) were calculated. Results. A total of 69 newborns were included in the current study, 30 (43.48 %) of whom were infants with D-TGA (D-TGA Group) and 39 (56.52 %) with CoA (CoA group). Newborns who required respiratory support had significantly higher CRIBII scores (3.03 ± 0.63 vs. 1.34 ± 0.19; p = 0.001), SNAP-II scores (26.61 ± 13.96 vs. 12.43 ± 9.98; p = 0.001), and SNAPPE-II scores (33.04 ± 2.91 vs. 14.47 ± 12.35; p = 0.001) compared to those who did not require it. Significantly higher CRIB-II (3.22 ± 0.67 vs. 1.56 ± 0.23; p = 0.003), SNAP-II (22.91 ±14.57 vs. 14.28 ± 1.58; p = 0.003), and SNAPPE-II (28.82 ± 17.95 vs. 16.58 ± 13.85; p = 0.003) scores were also associated with the need for inotropic support. When comparing the predictive efficiency of illness severity scores for detecting the risk of needing respiratory and inotropic support, they are more effective at predicting the need for ventilation and less effective at predicting the need for inotropes. Conclusions. The illness severity scores can effectively predict the need for pre-surgery interventions. Their simplicity makes them suitable for routine use.

Enhancing the transparency of data and ml models using explainable AI (XAI)

To this end, this paper focuses on the increasing demand for the explainability of Machine Learning (ML) models especially in environments where these models are employed to make critical decisions such as in healthcare, finance, and law. Although the typical ML models are considered opaque, XAI provides a set of ways and means to propose making these models more transparent and, thus, easier to explain. This paper describes and analyzes the model-agnostic approach, method of intrinsic explanation, post-hoc explanation, and visualization instruments and demonstrates the use of XAI in various fields. The paper also speaks about the requirement of capturing the accuracy and interpretability for creating responsible and ethical AI.

Effect of white matter uncertainty visualization in neurosurgical decision making.

Fiber tracking is a powerful technique that provides insight into the brain's white matter structure. Despite its potential, the inherent uncertainties limit its widespread clinical use. These uncertainties potentially hamper the clinical decisions neurosurgeons have to make before, during, and after the surgery. Many techniques have been developed to visualize uncertainties, however, there is limited evidence to suggest whether these uncertainty visualization influences neurosurgical decision-making. In this paper, we evaluate the hypothesis that uncertainty visualization in fiber tracking influences neurosurgeon's decisions and the confidence in their decisions. For this purpose, we designed a user study through an online interactive questionnaire and evaluate the influence of uncertainty visualization in neurosurgical decision-making. The results of this study emphasize the importance of uncertainty visualization in clinical decision making by highlighting the influence of different interval of uncertainty visualization in critical clinical decisions.

A Comparison of Postoperative Outcomes Based on Muscle versus Fasciocutaneous Flaps in Scalp Reconstruction: A Systematic Review and Meta-analysis.

Scalp reconstruction in plastic and reconstructive surgery often necessitates the transfer of soft-tissue flaps to restore form and function. The critical decision lies in choosing between muscle-containing (MC) and fasciocutaneous (FC) flaps for scalp reconstruction, and while both variants have their merits, flap composition remains a subject of ongoing debate. This scientific discussion aims to explore this contentious issue through a comprehensive meta-analysis, shedding light on the rationale behind the choice of these flaps and the potential impact on clinical outcomes. A comprehensive systematic review was conducted following PRISMA-P guidelines, encompassing six prominent databases up to the year 2023. Data were collected from studies assessing outcomes of MC and FC flaps for scalp reconstruction. Quality evaluation was performed using ASPS criteria and the ROBINS-I tool. Statistical analysis included descriptive statistics, meta-analysis, sensitivity analysis, and assessment of bias using STATA software. The meta-analysis included 28 nonrandomized studies, totaling 594 flaps (MC: 380, FC: 214). MC flaps were significantly larger than FC flaps. There were no significant differences in flap loss, flap necrosis, or wound dehiscence between the two flap types. However, the incidence of venous congestion was significantly higher in FC flaps. Sensitivity analysis confirmed the robustness of results, and publication bias assessment showed no significant evidence of bias. While both MC and FC flaps offer viable options for scalp reconstruction, the choice should be tailored to individual patient characteristics and defect size. FC flaps may provide advantages such as shorter operative times and reduced morbidity, whereas MC flaps could be preferred for addressing larger defects. Future research should focus on prospective studies and strategies to mitigate venous congestion in FC flaps, enhancing their safety and efficacy in scalp reconstruction.