Key Performance Areas Research Articles

Effective Strategies for Working with Children with Autism in Mainstream Classrooms

In this study the education is identified as one of the key performance areas in the occupational therapy practice framework. The occupation of education includes academic (math, reading , writing), non-academic (recess, lunch, self-help skills), extracurricular (sports, band, cheerleading, clubs), and prevocational and vocational activities. The objective of this study is to assess out the effectiveness of classroom strategies for children with autism in mainstream classroom. The methodology adopted under this study is 20 children with autism and 24 teachers were included in the control group(N=10,12) and experimental group(N=10,12). Canadian Occupational Performance Scale, Teachers Attitude Towards Inclusion Scale, and Behaviour Rating Scale were used before and after the use of classroom strategies. The results of this study are experimental group, COPM, there was a significant difference in the pretest and post-test of the performance (p=0.005) and satisfaction (p=0.013) component of COPM. When goals addressed in the control and experiment groups were compared between groups, it showed significant difference in the post tests of both the groups in performance (u=0.005) and satisfaction (u=0.005) component of COPM. The pre-test and post-test of the POS (attitude towards student with disability in inclusive setting) and total component showed significant difference (p= 0.003 and 0.004) whereas there is no significant difference in the control group. The classroom strategies have a positive effect on the occupational performance for children with autism in mainstream classroom. It is effective in bringing positive attitude among teachers towards inclusion. It also improves the classroom behaviours of the children.

Comparative Assessment of Environmental Disclosure Practices: A Case Study of Two Leading Oil Companies in India

This study conducts a comparative analysis of the environmental disclosure practices of two leading oil companies in India: OIL and Petronet LNG. By evaluating their sustainability reports and publicly available environmental information, the research assesses key performance areas such as energy consumption, greenhouse gas emissions, water management, waste treatment, and biodiversity conservation. The analysis reveals that both companies exhibit similar environmental disclosure practices, with strengths in energy and emissions management but significant gaps in compliance, biodiversity, and grievance mechanisms. A statistical t-test shows no significant difference between the environmental performance of the two companies, indicating comparable transparency levels. The study highlights the need for both organizations to improve their environmental reporting and align more closely with global sustainability standards, such as the Global Reporting Initiative (GRI) and Carbon Disclosure Project (CDP). Recommendations include enhancing biodiversity conservation efforts, strengthening supplier environmental assessments, and improving compliance with environmental regulations. Keywords: Environmental disclosure, Oil industry, Sustainability reporting, Comparative analysis, India, Greenhouse gas emissions, Energy management, Water usage, Biodiversity, Compliance, Corporate social responsibility (CSR).

B-128 Delivering Predictable Operational efficiency gains at our core central reference laboratory with strategic deployment of navify® Lab Operation informatics automation solution

Abstract Background Metropolis Healthcare Limited (‘MHL’) operates a network of 200 clinical labs in India, our Global Reference Lab (GRL) in Mumbai processes over 25000 tests in a 17 hour daily operation. We are an ISO15189 and CAP accredited laboratory. Our strategic goals have been to bring operational standardization across the network & Improve speed of service with a predictable 90 percentile production TAT. To achieve this goal, we chose digitalization of our laboratory operation with the deployment of navify ® Lab Operations informatics automation solution in. This poster explains our journey of planning, implementing, and delivering on our goal of predictable operational efficiency. The digital solution is at the forefront of all digitalization IQC processes, auto validation and auto approval of patient results passing through a framework of autoverification rules and complete traceability of processes. Methods The poster will explain the scope, planning and implementation approach of this digital automation project at Global reference Lab in 5 stepsHL7 order structure for communication between LIS and navify ® Lab Operations22 analyzers from 11 different manufacturers being connected to the Informatics solution. the corresponding testingTest & IQC (Internal Quality Control) Master with specific ranges and rules10 steps AV (Auto verification) Rules decision framework of concentric sieves across lab disciplines (155 AV Algorithms have been configured at GRL)Structured user acceptance testing framework before Go Live. Results This poster discusses the outcomes that we have seen on deployment of this digital solution.Over 25,000 test results are subject AV engine daily with AV rate of 75 % of all results in Biochemistry, Immunoassays.66 % of all Complete Blood Count (CBC) reports 95 % of all HbA1c results 93 % of all ESR resultsOver 1750 QC results are subject to QC rules dailyThere is an improvement of 4 hours observed in production TAT Conclusions Laboratory Informatics solution has enhanced lab performance in terms of TAT & efficiency of review. The staff satisfaction (both technical and medical) has increased and we see an impact on production outcome. The poster will also show an incremental effectiveness analysis by comparing key performance areas such as predictability in production, staff time saved, before and after technology deployment.

Optimizing biomass supply chains: A probabilistic approach to managing uncertainties in southwest Nigeria

Efficient and sustainable use of biomass resources is crucial to meet the increasing demand for bio-based products and renewable energy. The biomass supply chain, which includes harvesting, collecting, logistics, storage, and pre-treatment, faces challenges due to uncertainties such as market fluctuations, equipment availability, weather conditions, and transportation constraints. These uncertainties often hinder the optimisation of the supply chain. This research work explores the performance of the biomass supply chain by optimizing operations while accounting for these uncertainties. Nigeria is faced with power issues and there are resources to combat the problem through generation of cleaner energy from biomass. Using mathematical modelling, the study evaluates the impact of uncertainty on key performance areas like feedstock supply, inventory management, transportation efficiency, and processing capacity. The research demonstrates the importance of incorporating uncertainty-aware solutions to minimize risks and improve the flexibility of the biomass supply chain. Sensitivity analyses and case studies shows that the proposed probabilistic modelling approach provides valuable insights into system vulnerabilities and effective strategies for optimizing operations under uncertain conditions. The findings highlight the potential of this approach to improve decision making, resource allocation, and promote sustainable practices in the biomass sector. Ultimately, the study contributes to advancing biomass supply chain management, paving the way for a more resilient and efficient use of bioresources.

DEVELOPMENT OF LOW-LATENCY WIRELESS COMMUNICATION SYSTEMS FOR AUTONOMOUS VEHICLES

This research aims to explore the effectiveness of the L3 protocol in improving the V2X communication systems in key performance areas such as low communication delay, high-speed protocols and the effectiveness under real-life conditions. According to the L3 protocol simulation of the research, which employs platforms including COOJA and NS-3, the latency of the networks is lowered compared to that of the conventional DSRC/LTE system at below one millisecond levels adopting the time slots and the capture effect. These challenges observed on other systems such as delays experienced due to high-speed data transfer are addressed in the protocol. The performed measurements prove that the L3 protocol of the system exhibits reliability and scalability depending on the number of vehicles on the road as well as the network load, and retains its workability even with the number of 225 automobiles. These results conform with prior works that underlined the necessity of using sophisticated and low-latency relative communication systems for the usage of an autonomous vehicle network. In doing so, the study shows a future of L3 protocol in enhancing the current V2X communication systems in aspects of low latency, fast data transfer and competency in various conditions. It is suggested that future research include extensive field trials, the increase of security measures, the analysis of the scalability and integration of the new technologies, and the solution of the interoperability issues for the further investigation of the possibilities of the application of the L3 protocol.

DIII-D research to provide solutions for ITER and fusion energy

The DIII-D tokamak has elucidated crucial physics and developed projectable solutions for ITER and fusion power plants in the key areas of core performance, boundary heat and particle transport, and integrated scenario operation, with closing the core-edge integration knowledge gap being the overarching mission. New experimental validation of high-fidelity, multi-channel, non-linear gyrokinetic turbulent transport models for ITER provides strong confidence it will achieve Q ⩾ 10 operation. Experiments identify options for easing H-mode access in hydrogen, and give new insight into the isotopic dependence of transport and confinement. Analysis of 2,1 islands in unoptimized low-torque IBS demonstration discharges suggests their onset time occurs randomly in the constant β phase, most often triggered by non-linear 3-wave coupling, thus identifying an NTM seeding mechanism to avoid. Pure deuterium SPI for disruption mitigation is shown to provide favorable slow cooling, but poor core assimilation, suggesting paths for improved SPI on ITER. At the boundary, measured neutral density and ionization source fluxes are strongly poloidally asymmetric, implying a 2D treatment is needed to model pedestal fuelling. Detailed measurements of pedestal and SOL quantities and impurity charge state radiation in detached divertors has validated edge fluid modelling and new self-consistent ‘pedestal-to-divertor’ integrated modeling that can be used to optimize reactors. New feedback adaptive ELM control minimizes confinement reduction, and RMP ELM suppression with sustained high core performance was obtained for the first time with the outer strike point in a W-coated, compact and unpumped small-angle slot divertor. Advances have been made in integrated operational scenarios for ITER and power plants. Wide pedestal intrinsically ELM-free QH-modes are produced with more reactor-relevant conditions, Low torque IBS with W-equivalent radiators can exhibit predator-prey oscillations in T e and radiation which need control. High-β P scenarios with q min > 2, q 95–7.9, β N > 4, β T–3.3% and H 98y2 > 1.5 are sustained with high density ( n¯ = 7E19 m−3, f G–1) for 6 τ E, improving confidence in steady-state tokamak reactors. Diverted NT plasmas achieve high core performance with a non-ELMing edge, offering a possible highly attractive core-edge integration solution for reactors.

(Invited) Commercialization of CO2 Electrolysis

The atmospheric accumulation of greenhouse gasses such as carbon dioxide (CO2) is the primary cause of global climate irregularities. Over three trillion tons of greenhouse gasses have been emitted since the start of the industrial revolution, and natural disasters such as heatwaves, wildfires, and hurricanes have only increased in regularity. Decarbonization has become an important strategic priority for nations and industries worldwide. Numerous technologies have been developed to capture and store carbon dioxide from the atmosphere. However, most of these approaches do not address the ubiquitous demand for fossil fuel derivatives and their associated petroleum-based infrastructure, nor are these approaches capable of extracting value from the captured CO2.Our company, Twelve, is developing a highly efficient and scalable electrochemical reactor that converts CO2 into valuable building blocks for chemicals and fuels such as carbon monoxide (CO). The resulting CO can be blended with green hydrogen from water electrolyzers to form syngas, enabling an ultra-low carbon pathway to sustainable aviation fuel (SAF) via the Fischer-Tropsch process. Twelve has previously produced SAF for the Air Force, where we demonstrated the first ever SAF pathway based on CO2 electrolysis. Beyond SAF, Twelve has completed commercial projects for chemicals and materials made from CO2 with global brands that include Procter & Gamble and Mercedes Benz. At scale, this technology can enable a closed loop ecosystem wherein CO2, water, and electricity are converted into manufacturing inputs that are currently derived from fossil fuels. We are presently translating the performance of our lab-scale prototypes to the industrial scale, focusing on SAF as our beachhead market.In this talk, Twelve’s CO2 electrolysis technology will be used as a model to describe the commercialization challenges and market opportunities of industrial CO2 electrolysis. Key performance metrics and areas of active collaborative research between Twelve and academic institutions will be discussed. Finally, commercial demonstrations of our technology in the automobile, aviation, consumer chemicals, and apparel industries, as well as our company’s long-term vision and goals will be presented.

The role of mentoring in developing leaders’ emotional intelligence: exploring mentoring types, emotional intelligence, organizational factors, and gender

Emotional awareness, emotional regulation, empathy, and resilience are key components of emotional intelligence. Twenty-first-century leaders require such competencies, and prior research establishes a positive impact of emotional intelligence on leadership and well-being. The mechanisms through which leaders develop these competencies remain unclear. Mentoring, a developmental tool linked with well-being, has not been extensively studied for its role in emotional intelligence development. The current study investigates this relationship within the context of vocational education and training in South Africa. The mentoring framework includes individual, peer group, and key performance area mentoring. In previous research on this mentoring framework, leaders perceived emotional well-being as the most important outcome of mentoring and development, constituting another vital factor. Data were collected from a treatment group of leaders who have participated in the mentoring framework and a control group of leaders and lecturers (N = 139). The present study used exploratory and confirmatory factor analysis to validate the Schutte Self-Report Emotional Intelligence Test within this context. In the next step, we employed descriptive analysis to answer which mentoring type was best perceived to support emotional intelligence. Using the Mann–Whitney U test, we tested for significant differences in the identified factors between treatment and control group. Mediated and moderated mediation analyses explored variables such as gender, occupational role, organization, and work sector. Results indicate a six-factor structure of emotional intelligence, with significant differences observed between groups in the factor empathy difficulty. Peer group mentoring emerged as an effective method for emotional intelligence development among leaders. The perceived importance of emotional intelligence for one’s job position, the organization, and the work sector mediated five of the six factors. The moderated mediation analyses showed an indirect effect of gender, where being male was associated with more trustworthy visionary and empathy. The findings underscore the significance of peer mentoring practices and organizational factors in nurturing emotional intelligence, highlighting its value for personal and organizational well-being. Overall, the study sheds light on developing emotional intelligence at all organizational levels to support individual and collective well-being.

Perceptions of the effect of the COVID-19 pandemic on academics’ teaching, and research key performance areas (KPAs)

Perceptions of the effect of the COVID-19 pandemic on academics’ teaching, and research key performance areas (KPAs)

Preliminary Impact Assessment of the Ad Hoc Separation Minima: A New Separation Mode

A major challenge for the Air Traffic Management system is the need to boost airspace capacity, which is near saturation in some situations. Separation minima are one of the factors related to airspace capacity, and the SESAR program promotes research into advanced separation modes. Ad Hoc separation, a novel separation mode, involves applying different pairwise separation minima in the same volume of airspace depending on a set of factors, such as encounter geometry, aircraft models, and flight level, among others. This research examines the impact of implementing this concept in different en-route scenarios. The goal is to determine whether applying this concept proves advantageous or, conversely, results in an increase in the complexity of the system without significant benefits in the key performance areas of capacity, environment, and cost-efficiency. Fast Time Simulations are conducted in RAMS software, with the concept being implemented in the LECMZMU, LECMTLU, and LECMDGU sectors of the Madrid ACC. The results reveal favorable capacity outcomes with increases of around 2% and the LECMZMU sector exhibits the most significant environmental and cost-efficiency benefits. Furthermore, implementing the Ad Hoc concept in a larger scenario could yield even greater environmental and cost-efficiency benefits.

A Study on Competency Mapping Factor in Faurecia Emissions Control Technologies India Pvt Ltd

The research is conducted to identify the key competencies required for the Information Technology professionals who all are in Faurecia Emissions Control Technologies India Pvt Ltd. The competencies used for the study were based on the following key areas such as Personal competencies, Technical Competency, Knowledge level competencies, Job-related competencies, Interpersonal Competencies and Management competencies which is involved with the performance of the employees and it also make them to become successful in their career. In order to attain the competitive position in the business field, every employee in the organization should involve in increasing the productivity and efficiency at the workplace. Similarly, the organization also has a challenging part in retaining the employees and motivating them to update themselves relevant to their specialization or key performance areas. Therefore, the competency mapping will be helpful for the researcher in identifying the expected skills of every individual with the actual skill set of the job. Based on the observed gaps, specific training and necessary steps to be taken to overcome the skill deficiency of the employees.

Educational Supervision: Analysis of Learning the Qur'an Tilawati Method at User Institutions in Surabaya

The Al-Qur'an tilawati learning method was developed to facilitate the study of the Al-Qur'an for Muslims. This research intends to examine the implementation of the tilawati technique in 5 Al-Quran learning institutions in Surabaya. This qualitative study employs descriptive approaches to elucidate and characterise supervision practices at five formal educational institutions in Surabaya that utilise the tilawati method. This study employed three data gathering methods: observation, interviews, and field documentation. This research includes data reduction, data display, and verification as part of the data analysis process. The research findings indicated that the Key Performance Area (KPA) was functioning efficiently in SD Khadijah 2 Surabaya, but was not meeting its intended purpose in the other four schools. The learning process does not correspond with the tilawati method. Teachers are inadequately trained to utilise this method. Attendance records, schedules, and achievements are not consistently shared with all teachers. Most classrooms have a layout of benches and tables with parallel shapes. The learning facilities are not fully developed in all elementary schools involved in the study. The tilawati method of learning has been implemented in 5 schools in Surabaya. The impact of implementing the tilawati method in schools is that students are more able to read the Al-Quran well and easily.

DeepVAQ : an adaptive deep learning for prediction of vascular access quality in hemodialysis patients

BackgroundChronic kidney disease is a prevalent global health issue, particularly in advanced stages requiring dialysis. Vascular access (VA) quality is crucial for the well-being of hemodialysis (HD) patients, ensuring optimal blood transfer through a dialyzer machine. The ultrasound dilution technique (UDT) is used as the gold standard for assessing VA quality; however, its limited availability due to high costs impedes its widespread adoption. We aimed to develop a novel deep learning model specifically designed to predict VA quality from Photoplethysmography (PPG) sensors.MethodsClinical data were retrospectively gathered from 398 HD patients, spanning from February 2021 to February 2022. The DeepVAQ model leverages a convolutional neural network (CNN) to process PPG sensor data, pinpointing specific frequencies and patterns that are indicative of VA quality. Meticulous training and fine-tuning were applied to ensure the model’s accuracy and reliability. Validation of the DeepVAQ model was carried out against established diagnostic standards using key performance metrics, including accuracy, specificity, precision, F-score, and area under the receiver operating characteristic curve (AUC).ResultDeepVAQ demonstrated superior performance, achieving an accuracy of 0.9213 and a specificity of 0.9614. Its precision and F-score stood at 0.8762 and 0.8364, respectively, with an AUC of 0.8605. In contrast, traditional models like Decision Tree, Naive Bayes, and kNN demonstrated significantly lower performance across these metrics. This comparison underscores DeepVAQ's enhanced capability in accurately predicting VA quality compared to existing methodologies.ConclusionExemplifying the potential of artificial intelligence in healthcare, particularly in the realm of deep learning, DeepVAQ represents a significant advancement in non-invasive diagnostics. Its precise multi-class classification ability for VA quality in hemodialysis patients holds substantial promise for improving patient outcomes, potentially leading to a reduction in mortality rates.

Mapping ward managers' core competencies using the Balanced Scorecard model.

Nurse ward managers have a crucial role in the effective functioning of hospital wards and in organising the work of the nursing team. The Balanced Scorecard model is a management framework that enables strategy to be translated into action by setting goals that lead to measurable outcomes. Few studies have used the Balanced Scorecard model to map the competencies of healthcare professionals. To use the Balanced Scorecard model to map the core competencies of nurse ward managers and determine relevant key performance areas (KPAs) and key performance indicators (KPIs). This mixed-methods study was conducted in the surgical department of an Italian hospital using a qualitative and quantitative exploratory sequential approach. For the quantitative part, the authors asked a convenience sample of various clinical staff to rate a list of nine competencies in terms of their importance for the work of nurse ward managers. For the qualitative part, two focus group discussions were held with nurse ward managers. Responses to the questionnaires showed that all nine competencies were important to respondents, with no statistically significant differences between roles. Findings from the focus group discussions broadly confirmed the relevance and importance of the nine competencies. Based on these findings, the authors formulated a strategic map of the nurse ward manager role, with core competencies and associated objectives mapped against the four dimensions of the Balanced Scorecard model. A strategic map of the nurse ward manager role informed by the Balanced Scorecard model can help measure nurse ward managers' performance and provide a benchmark for them to improve their competencies.

Vocational education and training in South Africa: leaders' perceptions of a mentoring framework in a professional development programme

PurposeThe South African vocational education and training (VET) sector is required by legislation to redefine postsecondary education, advance industrialisation and expand the job market to address unemployment in the country. Yet, VET leaders' heterogenous educational and occupational backgrounds do not enable them to address the needs of the VET sector. Continuous professional development of leaders in the education sector needs to include support structures such as mentoring.Design/methodology/approachThe present study sought to investigate how VET managers in South Africa perceive three different types of mentoring – individual, peer group and expert-based key performance area (KPA) mentoring – during a part-time professional leadership development programme. Using interactive qualitative analysis (IQA), the authors collected and analysed data from focus group discussions (n = 24) and individual interviews (n = 21) from two cohorts of the programme.FindingsThe results revealed that individual mentoring represented the most important driving mechanism, followed by peer group mentoring and expert-based KPA mentoring. Participants identified leadership as a prerequisite for their development. Emotions formed the final outcome of the mentoring framework.Research limitations/implicationsBased on the findings, the authors suggest investigating the role played by leaders' interpersonal competences such as emotional competence in the workplace. Additionally, research needs to clarify if and how mentoring can support leaders to develop interpersonal competences in formal and informal settings.Originality/valueThe study offers empirical evidence on a three-pillar mentoring framework adopted in a professional development programme for leaders in VET in South Africa. It highlights the importance of individual, social and emotional factors.

RELATIONSHIP BETWEEN TRAINING EFFECTIVENESS AND EDUCATOR PERFORMANCE IN THE EASTERN CAPE DEPARTMENT OF EDUCATION, SOUTH AFRICA

Human Resource Development integrates and aligns training and development with organisational goals and strategy to improve performance. However, ingrained suboptimal skills and training opportunities in the Eastern Cape Department of Education (ECDoE) negatively impacts learning achievement outcomes. The purpose and objective of the study is to establish the relationship between training effectiveness and educator performance in the ECDoE.The study adopts a quantitative research strategy with 270 respondents. A survey questionnaire was used to collect data from participants. The survey questionnaire’s internal consistency and reliability were measured using Pearson's coefficient and Cronbach's alpha, respectively. Results show that most respondents did not view the trainings provided by the ECDoE as relevant to job performance, and the attainment of the Department’s strategic goals. The study concludes that ECDoE’s training should be closely aligned with educators’ Key Performance Areas, Performance Development Plans and individual needs analysis.

The Impact of Edema on MRI Radiomics for the Prediction of Lung Metastasis in Soft Tissue Sarcoma.

This study aimed to evaluate whether radiomic features extracted solely from the edema of soft tissue sarcomas (STS) could predict the occurrence of lung metastasis in comparison with features extracted solely from the tumoral mass. We retrospectively analyzed magnetic resonance imaging (MRI) scans of 32 STSs, including 14 with lung metastasis and 18 without. A segmentation of the tumor mass and edema was assessed for each MRI examination. A total of 107 radiomic features were extracted for each mass segmentation and 107 radiomic features for each edema segmentation. A two-step feature selection process was applied. Two predictive features for the development of lung metastasis were selected from the mass-related features, as well as two predictive features from the edema-related features. Two Random Forest models were created based on these selected features; 100 random subsampling runs were performed. Key performance metrics, including accuracy and area under the ROC curve (AUC), were calculated, and the resulting accuracies were compared. The model based on mass-related features achieved a median accuracy of 0.83 and a median AUC of 0.88, while the model based on edema-related features achieved a median accuracy of 0.75 and a median AUC of 0.79. A statistical analysis comparing the accuracies of the two models revealed no significant difference. Both models showed promise in predicting the occurrence of lung metastasis in soft tissue sarcomas. These findings suggest that radiomic analysis of edema features can provide valuable insights into the prediction of lung metastasis in soft tissue sarcomas.

Mechanisms and Constraints Underpinning Ethically Aligned Artificial Intelligence Systems: An Exploration of Key Performance Areas

The unpredictability of artificial intelligence (AI) services and products pose major ethical concerns for multinational companies as evidenced by the prevalence of unfair, biased, and discriminate AI systems. Examples including Amazon’s recruiting tool, Facebook’s biased ads, and racially biased healthcare risk algorithms have raised fundamental questions about what these systems should be used for, the inherent risks they possess, and how they can be mitigated. Unfortunately, these failures not only serve to highlight the lack of regulation in AI development, but it also reveals how organisations are struggling to alleviate the dangers associated with this technology. We argue that to successfully implement ethical AI applications, developers need a deeper understanding of not only the implications of misuse, but also a grounded approach in their conception. Judgement studies were therefore conducted with experts from data science backgrounds who identified six performance areas, resulting in a theoretical framework for the development of ethically aligned AI systems. This framework also reveals that these performance areas require specific mechanisms which must be acted upon to ensure that an AI system implements and meets ethical requirements throughout its lifecycle. The findings also outline several constraints which present challenges in the manifestation of these elements. By implementing this framework, organisations can contribute to an elevated trust between technology and people resulting in significant implications for both IS research and practice. This framework will further allow organisations to take a positive and proactive approach in ensuring they are best prepared for the ethical implications associated with the development, deployment and use of AI systems.

A Novel Green Ocean Strategy for Financial Sustainability (GOSFS) in Higher Education Institutions: King Abdulaziz University as a Case Study

Financing education has recently been a big concern since educational expenditure continues to rise. As a result, there will be a gradual shift away from the “unilateral approach” to funding and toward the “diversification of financing resources”. The aim of this paper is to propose an innovative strategy plan to optimize universities’ investment sources and maintain their financial sustainability. This approach was known as the Green Ocean Strategy for Financial Sustainability (GOSFS). To effectively implement GOSFS in higher education institutions, a roadmap of 18 steps is constructed around three primary key performance areas (resource development, good governance, and regulations and legislation). The GOSFS was applied within four successive stages at King Abdulaziz University as a case study. With 18 pillars found under these three key performance areas, a long-term target for 2045 and an overall goal for 2025 were defined. In addition, the paper created novel versions of the Business Model Canvas to meet the GOSFS plan concept. To effectively employ GOSFS, four recommendations are offered to accelerate business growth and engage the university investment ecosystem, including whether to invest in highly qualified human capital, expand financial resources, or leverage technical resources. Future directions are also provided.

Strategic planning and development of Air Traffic Management system in Europe: A capacity-based review

Nowadays there may be different strategic drivers between Air Navigation Service Providers (ANSPs) in Europe. However, far less than before market deregulation and liberalization, and before 2004 when European Commission (EC) established Single European Sky (SES) initiative. The introduction of two SES regulative packages and the European ATM Master Plan imposed the obligation to the ANSPs in Europe to strive for common strategic goals - articulated in terms of Key Performance Areas (KPA): capacity, safety, environment and cost-efficiency. All the Key Performance Indicators (KPI) of listed KPAs are based on the adoption of the one-size-fits-all principle. In that respect, this paper evaluates conceptual and methodological assumptions of strategic planning and development framework of the ATM systems in Europe from a capacitive viewpoint. It provides a review of flaws identified by studying the applicability of the performance target defined by EC for capacity as one of four KPAs. As such, in a concise review, the paper reveals shortcomings of conceptual and methodological assumptions, performance differences and gaps, their implications, ANSPs with under-defined and above-defined performance targets, i.e., strategic goals from a capacitive viewpoint and points out flaws that need to be revised to better reflect settings of the ATM system in Europe.