Use Of Expert Judgment Research Articles

Influence of Individual Consideration on Organizational Performance on private Universities in Nairobi County, Kenya

Transformational leadership is a process in which leaders and followers help each other to advance to a higher level of morale and motivation, creating significant changes in the lives of people and organizations. The purpose of the study was to investigate the influence of individualized consideration style on organization performance in private universities in Nairobi City County, Kenya. The objective that guided the study was to: establish the influence of individualized consideration on organizational performance in private universities in Nairobi City County, Kenya. The study used Transformational Leadership Theory by Burns (1978) and the conceptual framework was based on the relationship between individualized consideration style and organizational performance. The sample comprised of 288 lecturers and 7 Deans of Academic Affairs of the chartered private universities, and questionnaires and interview guides were used to gather data. Validation of the questionnaires was through Cronbach’s Alpha and the use of expert judgement. The coefficient value was 0.879 at alpha = 0.05. Data was analyzed using both descriptive and inferential statistics, and the regression analysis showed a strong relationship between individualized consideration and the performance of private universities. The results revealed a positive and significant relationship between individualized consideration (β = .370, p<.05). It was concluded that offering leadership support and advice, mentorship, career development, and solving individual needs among their employees influences performance. The following recommendations were made: the management of private universities ought to offer leadership support and advice to their employees as this is important in improving their performance. Furthermore, they should realize that in order to realize higher performance for their institutions they need to ensure that the leaders in the institutions act as both mentors and coaches for the employees and recognize their contributions to the organization. The Deans of Academic Affairs need to attend seminars and training in transformational leadership so that they can be well-versed in transformational leadership skills, responsibilities, and characteristics.

INFORMATION CAPABILITIES OF THE TRANSITION FUNCTION OF THE HYDROGEN STORAGE AND SUPPLY SYSTEM GAS GENERATOR TO ASSESS ITS FIRE HAZARD LEVEL

To estimate the level of fire hazard of hydrogen storage and supply systems, it is advisable to use reliability indicators, in particular, of their core element, which is a gas generator. Such indicators include indicators of amplitude and phase reliability of the gas generator, which are determined through the Laplace function. The arguments of this function are the relative errors of the parameters of the gas generator, which are determined by its transition function. Three options for determining the parameters of the gas generator have been developed, the feature of which is obtaining information about its parameters at two a priori given moments. The magnitudes of these moments do not exceed the time of the transient process. In the a priori specified moments, the values of the transient fraction are measured (option 1), and the values of its derivative (option 2) or the value of the integral of the transient function (option 3) are additionally measured. Algorithms for processing the received information have been developed for all options, the implementation of which ensures the determination of gas generator parameters. In terms of its structure, the second option for determining gas generator parameters is the simplest, which has 1.5 times fewer arithmetic operations compared to the first option. In addition, the second option for determining the parameters of the gas generator is invariant to the value of the time parameters that determine the measurement moments. The results of determining the parameters of the gas generator and their nominal values are used to determine the arguments of the Laplace functions. We have specified that when determining the parameters of the gas generator, the complexing method can be used, which involves the implementation of all three information processing algorithms. As an example of the second option of determining gas generator parameters, the structural diagram of the algorithm is presented. We have emphasised that the implementation of the developed algorithms for determining the parameters of the gas generator has no subjective factor associated with the use of expert judgments. Keywords: gas generator, fire hazard level, transition function.

Expert Judgment in the 2022 Aotearoa New Zealand National Seismic Hazard Model

AbstractThe 2022 revision of the New Zealand National Seismic Hazard Model—Te Tauira Matapae Pūmate Rū i Aotearoa (NZ NSHM 2022) is, like other regional and national seismic hazard models, a collection of many component models that are combined via logic trees to calculate various parameters of seismic hazard. Developing, selecting, and combining component models for the NZ NSHM 2022 requires expert judgment. Informal and unstructured use of expert judgment can lead to biases. Drawing on a broad body of literature on potential biases in expert judgment and how to mitigate them, we used three approaches to incorporate expert judgment with the aim to minimize biases and understand uncertainty in seismic hazard results. The first approach applied two closely aligned group structures—the Science Team Working Groups and the Technical Advisory Group (TAG). The groups between them defined the project and made the scientific decisions necessary to produce the final model. Second, the TAG provided the function of a participatory review panel, in which the reviewers of the NSHM were actively engaged throughout the project. The third approach was performance-based weighting of expert assessments, which was applied to the weighting of the logic trees. It involved asking experts so-called calibration questions with known answers, which were relevant to the questions of interest, that is, the logic-tree weights. Each expert provided their best estimates with uncertainty, from which calibration and information scores were calculated. The scores were used to weight the experts’ assessments. The combined approach to incorporating expert judgment was intended to provide a robust and well-reviewed application of seismic hazard analysis for Aotearoa, New Zealand. Robust expert judgment processes are critical to any large science project, and our approach may provide learnings and insights for others.

PHYSICAL FACILITIES AVAILABILITY ON CHILDREN ATTENDANCE IN PUBLIC PRE-PRIMARY SCHOOLS IN MWATATE SUB-COUNTY, KENYA

The purpose of this study is to establish the influence of physical facilities availability on children attendance in public pre-primary schools in Mwatate Sub-County, Kenya. The study employed a descriptive survey research design. The target population for this study was 279 comprising of 184 Pre-Primary School teachers in 92 School Committee Chairpersons and 3 ECDE supervisors in three zones of Mwatate sub-county. The study employed a census method to determine the sample sizes of Pre-Primary School teachers (184) and ECDE supervisors (3); and a ten percent rule for School Committee Chairpersons (11). This gave a total sample size of 178 persons. Three instruments namely: the Pre-Primary School Teacher Questionnaire, School Committee Chairperson Interview and ECDE supervisors Interview Schedule was employed to collect data from the respondents. The instruments were validated by use of expert judgment while reliability of the tools was established through the use of test-retest procedures. Internal consistency of the tool was be established through the use of the Cronbach’s Alpha measures. The questionnaire was deemed to be reliable after attaining a reliability value of α = 0.88. Data was analyzed statistically using percentages and Chi-Square measures at 0.05 confidence levels. The study established that there is a statistically significant relationship between physical facilities availability and children attendance in public pre-primary schools in Mwatate Sub-County, Kenya. Overall, the study highlights the need for institutions and parents to work together to create a conducive learning environment for all children. This includes providing a variety of learning resources, maintaining clean and orderly classrooms and encouraging regular school attendance. By addressing these issues, institutions and parents can help ensure that all children have access to quality education and the opportunity to reach their full potential.

Expert judgment in climate science: How it is used and how it can be justified

Like any science marked by high uncertainty, climate science is characterized by a widespread use of expert judgment. In this paper, we first show that, in climate science, expert judgment is used to overcome uncertainty, thus playing a crucial role in the domain and even at times supplanting models. One is left to wonder to what extent it is legitimate to assign expert judgment such a status as an epistemic superiority in the climate context, especially as the production of expert judgment is particularly opaque. To begin answering this question, we highlight the key components of expert judgment. We then argue that the justification for the status and use of expert judgment depends on the competence and the individual subjective features of the expert producing the judgment since expert judgment involves not only the expert's theoretical knowledge and tacit knowledge, but also their intuition and values. This goes against the objective ideal in science and the criteria from social epistemology which largely attempt to remove subjectivity from expertise.

Bayesian Hierarchical Framework from Expert Elicitation in the South African Coal Mining Industry for Compliance Testing.

Occupational exposure assessment is important in preventing occupational coal worker's diseases. Methods have been proposed to assess compliance with exposure limits which aim to protect workers from developing diseases. A Bayesian framework with informative prior distribution obtained from historical or expert judgements has been highly recommended for compliance testing. The compliance testing is assessed against the occupational exposure limits (OEL) and categorization of the exposure, ranging from very highly controlled to very poorly controlled exposure groups. This study used a Bayesian framework from historical and expert elicitation data to compare the posterior probabilities of the 95th percentile (P95) of the coal dust exposures to improve compliance assessment and decision-making. A total of 10 job titles were included in this study. Bayesian framework with Markov chain Monte Carlo (MCMC) simulation was used to draw a full posterior probability of finding a job title to an exposure category. A modified IDEA ("Investigate", "Discuss", "Estimate", and "Aggregate") technique was used to conduct expert elicitation. The experts were asked to give their subjective probabilities of finding coal dust exposure of a job title in each of the exposure categories. Sensitivity analysis was done for parameter space to check for misclassification of exposures. There were more than 98% probabilities of the P95 exposure being found in the poorly controlled exposure group when using expert judgments. Historical data and non-informative prior tend to show a lower probability of finding the P95 in higher exposure categories in some titles unlike expert judgments. Expert judgements tend to show some similarity in findings with historical data. We recommend the use of expert judgements in occupational risk assessment as prior information before a decision is made on current exposure when historical data are unavailable or scarce.

Quality criteria in MOOC: Comparative and proposed indicators.

The MOOC (Massive Open Online Course) offer revolution requires processes that can define their quality, especially due to the number of courses offered, as well as the high number of students enrolled in them. The objective of this study is to identify the main requirements and indicators of the MOOC following the ENQA (The European Association for Quality Assurance in Higher Education) considerations. To establish this system, the study has been carried out through the Delphi method with successive rounds of application from the systematic use of expert judgment. This method has been applied to achieve consensus on a set of requirements and indicators of 20 experts from eight different institutions in the field of application and development of MOOCs that assessed the indicators according to the quality criteria according to three aspects: relevance, feasibility, and comparability. Therefore, the outcome of this study is a system as a mechanism for the university to approve or disapprove a MOOC (checklist) and assess its quality.

Demand Forecast Modelling of Vehicles as a Decision Support: The Case of Toyota Ghana

Purpose: This purpose of this paper is to develop a mathematical demand forecast model as an alternative to expert-intensive methods for decision support in automobile companies using Toyota Ghana as a case. The paper explores the challenges associated with reliance on experts’ judgment in demand forecasting. 
 Design/Methodology/Approach: The methodology involved analysing stock reports, lost sales reports, and financial reports from Toyota Ghana to understand the effect of poor forecasting. Using data from two key managers and six sales staff, the project examines the perspectives of staff regarding the use of expert judgment for demand forecasting. Further data was collected via a questionnaire from five authorized automobile distributors and dealerships.
 Findings: The results revealed the adverse effects of expert-opinion forecasting, which include irregular stock quantities leading to lost sales, vehicle quality challenges leading to deterioration, and long-term negative impact on profitability. Yet demand forecasting by reliance on experts was very prevalent in the automobile industry. The developed forecast model relies on Mean Absolute Percentage Error with a smoothing constant of 0.4. was validated using recent historical data revealing a 2% variance with actual demand values, while for expert judgment the variation margin was 14%. This strongly indicated that the model yielded more accurate predictions of demand than expert predictions.
 Research Limitation: The case-study nature of the study means a more generalized study was still needed before the findings could be more widely applied across the automobile industry.
 Practical implication: The study recommended further development of scientific forecasting models for predicting demand across the automobile industry since they carried positive implications for the smooth running of the industry. This could help mitigate the challenges associated with using expert opinions in demand forecasting. Beyond this, the model could serve to provide valuable information to vehicle manufacturers, thereby yielding efficiencies in their value chains.
 Social implication: Accurate demand forecasting and management have positive implications for operational efficiency that minimizes customer disappointment. 
 Originality / Value: The model offers a better alternative for predicting demand more accurately, promoting correct stock holding quantities, avoiding stock deterioration, and reducing expenditure on quality checks, thus ultimately increasing profitability.

The phronesis of expert searchers on using forward citation searching and web searching to search for studies for systematic reviews: A hermeneutic phenomenological analysis

Systematic reviews aim to use formalised and explicitly described methods. However, studies show that systematic reviews pose challenges which can only be resolved using expert judgement that is resistant to explicit formulation. The expertise required to make such judgements can be understood as practical knowledge or phronesis, and is based on lived experiences rather than following clearly defined rules. This study used qualitative methods to investigate the phronesis of expert searchers in the development and conduct of searches for studies for systematic reviews. In particular, the study focused on two ‘supplementary’ search methods: forward citation searching and web searching. Data collection used semi-structured interviews with 15 expert searchers and the analysis used a hermeneutic phenomenological approach. The findings describe five habits of phronesis when searching for studies: Outcome-oriented; persistent; adaptive; critically engaged and holistic. The study brings attention to the use of expert judgement when searching for studies for systematic reviews.

Elaboration of a method for strategic analysis of the development of the Armed Forces

Previous research has shown the need to increase the validity of the strategic decisions taken to determine (clarify) the strategy for developing the capabilities of the Ukrainian Armed Forces in the long term. It is necessary to elaborate a multi-criteria method for the strategic analysis of the development of the Armed Forces, based on statistical data obtained from the results of the defence reform and the defence review, minimising the use of expert judgments. To elaborate this method a conceptual approach was used, which combines choosing priority areas for developing the capabilities of the Armed Forces and a recursive way of organising the computing process. The process involved the creation of a system of indicators and performance criteria, their weight coefficients, and recursive and objective functions for constructing a mathematical model for synthesising the results of the strategic analysis of the development of the Armed Forces. The method of strategic analysis of the development of the Armed Forces using the recursive method of organising the computational process (SADAF-RW) produces objective assessments and the expected effect of implementing the chosen strategy for making strategic decisions. It is aimed at improving the state of readiness and capabilities of the Armed Forces in the long term, minimising the use of expert judgments. The method can be used in the elaboration of a scientific and methodological apparatus for assessing the effectiveness of strategic planning for the development of the Armed Forces since it is based on the principles of objectivity of assessments, consistency, and effectiveness of strategic decisions.

Exploring dependencies among global environmental, socioeconomic, and technological risks

Global risks pose a serious challenge to the success of international businesses due to the direct impact of risks on business operations. The World Economic Forum (WEF) regularly conducts surveys involving experts to help governments and businesses understand the significance of global risks across different regions. The WEF has classified 30 global risks of doing business into five distinct risk categories, including economic, environmental, geopolitical, societal, and technological risks. This study aims to utilize the survey data by WEF for establishing statistical dependencies among different risk categories. Rather than using the conventional approach of prioritizing risks based on probability and impact ratings, which has been criticized in the literature for several limitations, we make use of expert judgment regarding the relative importance of risks. The results of this study reveal strong statistical dependencies among five risk categories and associated risks. Another merit of this paper is to capture the uncertainty surrounding risk assessments. There is a negative correlation observed between the two risk ranking schemes while considering the probability of individual global risks being in the top five and bottom five risks.

Data-driven recognition and modelling of deterioration patterns in the US National Bridge Inventory: A genetic algorithm-artificial neural network framework

Preserving infrastructures in a functioning and safe state requires maintenance or rehabilitation actions for the structures’ future needs. Thus, reliable estimations of deterioration rates are necessary for prioritizing structural interventions and assisting decision-makers in optimally allocating budgets. Among various approaches employed to model structural deterioration, statistical modelling using infrastructure databases is an increasingly utilized practice. Information contained within infrastructure databases usually concerns structural and operational attributes of the inventoried structures. Incorporating additional deterioration factors (e.g. environmental conditions) requires extracting content from other data sources. This process may result in large databases that entail complex nonlinear relationships among the included factors. The use of expert judgment or performing a subjective selection of deterioration factors from a small pre-specified set of factors has led to the generation of more manageable data collections, facilitating information processing. This study proposes an unbiased data-driven framework that combines Genetic Algorithms (GAs) and Artificial Neural Networks (ANNs) to simultaneously: (a) optimize the architecture of a suitable ANN model predicting infrastructure condition status and (b) perform a feature selection of essential deterioration factors among explanatory variables contained in the data collection used. The combined GA-ANN framework is applied to a dataset for 223,369 bridge structures located across the conterminous US region, which includes 53 variables with data from the National Bridge Inventory (NBI) and other reliable data sources. The results of the framework's application are assessed for their accuracy, efficiency and relevance, showing that artificial intelligence-based pattern recognition renders promising prospects for objectively capturing essential bridge deterioration information.

THE INTERRELATIONSHIP AMONG THE COMPONENTS OF STUDENTS’ INTERNET INSIGHT AS AN ACADEMIC RESOURCE TOOL IN KENYAN UNIVERSITIES

Purpose: The aim of this paper was to determine the interrelationship among the components of students’ internet insight as an academic resource tool in universities based in Kenya.
 Methodology: The study was conducted in Moi University and Daystar University. The study was based on the Social learning theory by Bandura focusing on internet self-efficacy and supplemented by Technology acceptance model by Davies. Quantitative research approach was undertaken. An ex post facto research survey design was adopted. The researcher used a representative sample of 435 (Moi University) and 175 (Daystar University)adding up to 610 Third year student teachers of the academic year 2015/2016 drawn from School of Education in the two universities. Stratified sampling was used to categorize students by gender from each stratum; participants were chosen randomly. Questionnaires were used as instruments of data collection. Content validity was established by use of expert judgment in the school of education. Test re-test method was applied to check if the instruments that collected data were reliable. Descriptive and inferential statistics were applied to analyze data. In descriptive statistics data frequencies, percentages and mean was used. Analysis of variance (ANOVA), Chi square and Post hoc tests were used to test the hypotheses.
 Results: The findings indicated that more participants concurred with almost all accounts on internet knowledge, self-efficacy, perceived internet usefulness and perceived internet ease of use.
 Unique contribution to theory, practice and policy: The results of the paper are useful in designing educational programs in Kenyan institutions of higher learning and also, present a platform to close the gap of knowledge in digital divide field which is used later in technology acceptance studies.

INFLUENCE OF TYPE OF UNIVERSITY ON STUDENTS’ INTERNET INSIGHT AS AN ACADEMIC RESOURCE TOOL IN KENYAN UNIVERSITIES

Purpose: The aim of the paper is to find out the impact of type of university on the insight students have about the internet as a learning tool in universities based in Kenya.
 Methodology: Quantitative research approach was undertaken. An ex post facto research survey design was adopted. The researcher used a representative sample of 435 (Moi University) and 175 (Daystar University)adding up to 610 Third year student teachers of the academic year 2015/2016 drawn from School of Education in the two universities. Stratified sampling was used to categorize students by gender from each stratum; participants were chosen randomly. Questionnaires were used as instruments of data collection. Content validity was established by use of expert judgment in the school of education. Test re-test method was used to establish the reliability of instruments of data collection. Descriptive and inferential statistics were applied for data analysis. The inferential statistics used was mean, data frequencies and percentages. Several tests were used to test the hypothesis, that is, Post hoc, Chi square and Analysis of variance.
 Results: The study findings indicated no significant relationship between university type and internet knowledge, perceived internet ease of use and internet self-efficacy.
 Unique contribution to practice, theory, and strategy: The outcome of the study is useful in designing educational programs in Kenyan institutions of higher learning and also, creates an avenue to link the knowledge gap in digital divide research field, to be used later for technology acceptance studies.

Influence of Principals’ Intellectual Stimulation on Students’ Performance at Kenya Certificate of Secondary Education in Public Secondary Schools, Kenya

Transformational leadership is an ideal characteristic which enables the principals to engage with followers and raise consciousness about the significance of specific outcomes and new ways in which those outcomes might be achieved. The purpose of this study was to investigate the influence of principals’ intellectual stimulation on student academic performance in Kenya Certificate Secondary Examination (K.C.S.E) in Makueni County, Kenya. The objective that guided the study was to: assess how the principals’ intellectual stimulation influences students results at Kenya Certificate of Secondary Education. with the hypothesis that there is no relationship between principals’ intellectual stimulation with students’ mean scores at Kenya Certificate of Secondary Education Makueni County, Kenya. The study used transformational leadership theory by Burns (1978) which has four dimensions namely idealized influence, inspirational motivation, intellectual stimulation and individual consideration; all of which are critical in determining academic achievement. The conceptual framework of this study is based on the relationship between the principal transformational with academic performance. The sample comprised of 111 principals, 729 teachers and 12 Ministry of Education officials drawn from 388 secondary schools. Questionnaires and interview guide were used to collect data. Validation of the questionnaires were through test re-test method and use of expert judgment. The coefficient value was 0.85 at alpha = 0.05. Data was analysed using both descriptive Pearson’s correlation coefficient that test showed a strong correlation for Intellectual stimulation and KCSE students’ mean scores. The result indicated a negative and strong correlation between intellection stimulation and students’ KCSE performance (r=-.195, p-value<0.05) respectively. It was concluded that principals’ collective sense of mission and valuing ideas of the followers increased performance. The following recommendations were made: KEMI to carry out transformational leadership seminars for the principals on intellectual practices. The Ministry of Education Policy makers should establish policies on transformational leadership. Teacher trainers especially the universities ought to review curriculum to include analysis of school managers and also have simulated situations.

Spatial Modeling of Maritime Risk Using Machine Learning.

Managing navigational safety is a key responsibility of coastal states. Predicting and measuring these risks has a high complexity due to their infrequent occurrence, multitude of causes, and large study areas. As a result, maritime risk models are generally limited in scale to small regions, generalized across diverse environments, or rely on the use of expert judgement. Therefore, such an approach has limited scalability and may incorrectly characterize the risk. Within this article a novel method for undertaking spatial modeling of maritime risk is proposed through machine learning. This enables navigational safety to be characterized while leveraging the significant volumes of relevant data available. The method comprises two key components: aggregation of historical accident data, vessel traffic, and other exploratory features into a spatial grid; and the implementation of several classification algorithms that predicts annual accident occurrence for various vessel types. This approach is applied to characterize the risk of collisions and groundings in the United Kingdom. The results vary between hazard types and vessel types but show remarkable capability at characterizing maritime risk, with accuracies and area under curve scores in excess of 90% in most implementations. Furthermore, the ensemble tree-based algorithms of XGBoost and Random Forest consistently outperformed other machine learning algorithms that were tested. The resultant potential risk maps provide decisionmakers with actionable intelligence in order to target risk mitigation measures in regions with the greatest requirement.

Influence of Principals’ Intellectual Stimulation on Students’ Performance in the Kenya Certificate of Secondary Education in Public Secondary Schools, Kenya

Transformational leadership is an ideal characteristic which enables the principals to engage with followers and raise consciousness about the significance of specific outcomes and new ways in which those outcomes might be achieved. The purpose of this study was to investigate the influence of principals’ intellectual stimulation on student academic performance in Kenya Certificate Secondary Examination (K.C.S.E) in Makueni County, Kenya. The objective that guided the study was to: assess how the principals’ intellectual stimulation influences students results at Kenya Certificate of Secondary Education, with the hypothesis that there is no relationship between principals’ intellectual stimulation and students’ mean scores at Kenya Certificate of Secondary Education Makueni County, Kenya. The study used Transformational Leadership Theory by Burns (1978) which has four dimensions namely idealized influence, inspirational motivation, intellectual stimulation and individual consideration; all of which are critical in determining academic achievement. The conceptual framework of this study is based on the relationship between the principal transformational qualities with academic performance. The sample comprised 111 principals, 729 teachers and 12 Ministry of Education officials drawn from 388 secondary schools. Questionnaires and interview guide were used to collect data. Validation of the questionnaires were through test retest method and use of expert judgment. The coefficient value was 0.85 at alpha = 0.05. Data was analyzed using both descriptive Pearson’s correlation coefficient that test showed a strong correlation for Intellectual stimulation and KCSE students’ mean scores. The result indicated a negative and strong correlation between intellection stimulation and students’ KCSE performance (r=-.195, p-value<0.05)respectively. It was concluded that principals collective sense of mission and valuing ideas of the followers increased performance The following recommendations were made: KEMI to carry out ransformational leadership seminars for the principals on intellectual practices. The Ministry of Education Policy makers should establish policies on transformational leadership. Teacher trainers especially the universities ought to review curriculum to include analysis of school managers and also have simulated situations.

Eliciting Model Structures for Multivariate Probabilistic Risk Analysis

Notionally objective probabilistic risk models, built around ideas of cause and effect, are used to predict impacts and evaluate trade-offs. In this paper, we focus on the use of expert judgement to fill gaps left by insufficient data and understanding. Psychological and contextual phenomena such as anchoring, availability bias, confirmation bias and overconfidence are pervasive and have powerful effects on individual judgements. Research across a range of fields has found that groups have access to more diverse information and ways of thinking about problems, and routinely outperform credentialled individuals on judgement and prediction tasks. In structured group elicitation, individuals make initial independent judgements, opinions are respected, participants consider the judgements made by others, and they may have the opportunity to reconsider and revise their initial estimates. Estimates may be aggregated using behavioural, mathematical or combined approaches. In contrast, mathematical modelers have been slower to accept that the host of psychological frailties and contextual biases that afflict judgements about parameters and events may also influence model assumptions and structures. Few, if any, quantitative risk analyses embrace sources of uncertainty comprehensively. However, several recent innovations aim to anticipate behavioural and social biases in model construction and to mitigate their effects. In this paper, we outline approaches to eliciting and combining alternative ideas of cause and effect. We discuss the translation of ideas into equations and assumptions, assessing the potential for psychological and social factors to affect the construction of models. We outline the strengths and weaknesses of recent advances in structured, group-based model construction that may accommodate a variety of understandings about cause and effect.

Artificial Neural Network and Analytical Hierarchy Process Integration: A Tool to Estimate Business Strategy of Bank

The disruption has been occurring in financial services. Rethinking new strategy of banking is needed to make a sustainable competitive advantage innovation in organizations. The four types of business strategy for banks are prospector, analyzer, defender, and reactor. Studies mentioned that formulating strategy is very costly, time consuming, and comprehensive analysis. The banks have to get rid of execution time inefficiency, lack of flexibility, and lack of ability to present several scenarios in the dynamic business environment. The purpose of this study is to present an integrated intelligence algorithm for estimating strategic resources of the bank strategy in Indonesia. The algorithm has two basic modules which are artificial neural network (ANN) and analytical hierarchy process (AHP). ANN is utilized as an inductive algorithm in discovering predictive strategy of the bank and used to explain the strategic resources which improved in forward. AHP has the capability to handle multi-level decision-making structure with use of expert judgments in pairwise comparison process. AHP is used to measure the weight of the resources and the score is used to determine the strategy. The empirical results indicate that ANN and AHP integration is proved to predict the business strategy of the bank. The strategy choice appropriate with the condition of bank's resources. This framework can be implemented to help banker for the decision making in bank operation. Keywords: business strategy, ANN, AHP, resources

Express analysis of conducting a set of preparatory and special exercises on land for swimming students of the 1st year of the Faculty of Physical Culture and Sports of H.S. Skovoroda KhNPU

The aim of the research is to study the characteristics of practical training and the level of formation of knowledge and skills among the students of the first course of the Faculty of Physical Education and Sport. G.S.Skovoroda on the subject "Theory and Methodology of Teaching Swimming" by means of express analysis of preparatory and special exercises on land. Materials and methods. The pedagogical research involved 39 students of the 1st course in 2018 and 41 students in 2019. The study was carried out during camp fees. The complexes were explained to the students. The students were given a task to learn one of them (the one that was asked) and to spend it with the students of their group. The following indicators were recorded in the study: in order to monitor the level of knowledge and skills of students in a range of land-based exercises using the rapid analysis method, criteria were developed to assess the effectiveness of the acquisition of this skill: Organizational abilities (building, group vision during the complex, team voice); display techniques (mirror display); delivery techniques (short and in accordance with terminology); excitement, self-confidence (exercise not in order, exercise properly); aspiration to support (peek in outline, clue). Each criterion was evaluated by observation with a score of 2, and scores were added and calculated with an arithmetic average (a maximum of 10). Research results. The main criteria for the comparative characterization of the performance of land-based exercises by students were the use of expert judgement and the processing of data by mathematical statistical methods. Conclusions. Definitely that: 1. When learning the skills to conduct sets of preparatory and special exercises on land in 2018, the greatest difficulty was caused by 3.4 simulation exercises. 2. Regarding the criteria behind the evaluation of the complexes, the most difficult criteria for students were the ability to see the group and remember the activities of the complex. 3. Errors were most often encountered during the use of the display and storytelling techniques, as well as in the sequence of the exercise complex. The information on the objective state of the students' learning of the skills of conducting preparatory and special exercises made it possible to find out, That it is necessary to develop such complexes as if, due to their complexity, they are the same and take into account both simple and complex general, special and simulation exercises. Taking into account the identified difficulties in the assimilation of the preparatory and special exercise complexes in 2019, the complexes were redesigned and, after a second experiment, that due to the difficulty of assimilating the complex of preparatory and special exercises has almost evened out in average scores, the difference is no more than 1 point.