Search results for: exploratory data analysis

Authors: Priya Nambisan, Lien B. Nguyen

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Women participate extensively in the healthcare field, professionally (as physicians, nurses, dietitians, etc.) as well as informally (as caregivers at home). This provides them with a better understanding of the health needs of people. Women are also in the forefront of using social media and other mobile health related apps. Further, many health mobile apps are specifically designed for women users. All of these indicate the potential for women to be successful entrepreneurs in healthcare, especially, in the area of mobile health app development. However, extant research in entrepreneurship has paid limited attention to women entrepreneurship in healthcare. The objective of this study is to determine the key factors that shape the intentions and actions of women entrepreneurs with regard to their entrepreneurial pursuits in the healthcare field. Specifically, the study advances several hypotheses that relate key variables such as personal skills and capabilities, experience, support from institutions and family, and perceptions regarding entrepreneurship to individual intentions and actions regarding entrepreneurship (specifically, in the area of mobile apps). The study research model will be validated using survey data collected from potential women entrepreneurs in the healthcare field – students in the area of health informatics and engineering. The questionnaire-based survey relates to woman respondents’ intention to become entrepreneurs in healthcare and the key factors (independent variables) that may facilitate or inhibit their entrepreneurial intentions and pursuits. The survey data collection is currently ongoing. We also plan to conduct semi-structured interviews with around 10-15 women entrepreneurs who are currently developing mobile apps to understand the key issues and challenges that they face in this area. This is an exploratory study and as such our goal is to combine the findings from the regression analysis of the survey data and that from the content analysis of the interview data to inform on future research on women entrepreneurship in healthcare. The study findings will hold important policy implications, specifically for the development of new programs and initiatives to promote women entrepreneurship, particularly in healthcare and technology areas.

Keywords: women entrepreneurship, healthcare, mobile apps, health apps

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Authors: Diamantino Ribeiro, António Pedro Costa, Jorge Remondes

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Dubai and the UAE governments have been investing in technology and digital communication for a long time. These governments are pioneers in introducing innovative strategies, policies and projects. They are also recognized worldwide for defining and implementing long term public programs. In terms of eGovernment Dubai and the UAE rank among the world’s most advanced. Both governments have surprised the world a few years ago by creating a Happiness Ministry. This paper focuses on UAE’s government digital strategies and its approach to the next era. The main goal of this exploratory study is to understand the new era of eGovernment and transfer of the happiness and wellness programs. Data were collected from the corpus latente and analysis was anchored in qualitative methodology using content analysis and observation as analysis techniques. The study allowed to highlight that the 2020 government reshuffle has a strong focus on digital reorganisation and digital sustainability, one of the newest trends in sustainability. Regarding happiness and wellbeing portfolio, we were able to observe that there has been a major change within the government organisation: The Ministry of Happiness was extinct and the Ministry of Community Development will manage the so-called ‘Happiness Portfolio’. Additionally, our observation allowed to note the government dual approach to governance: one through digital transformation, thus enhancing the digital sustainability process and, the second one trough government development.

Keywords: ministry of happiness, eGovernment, communication, digital sustainability

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Authors: Mukesh Ranjan, Varsha Nagargoje

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Applying the exploratory factor analysis (EFA), Women Employment Participation Perception Index (WEPPI) has been made through 13 components. The basic purpose of the WEPPI is to develop an index or search for the latent factors which will capture the attitude or perception of the Mumbai’s slum women towards women’s employment participation in the job market through primary survey based on 160 observations. Majority of the response analyzed under various socio-economic and demographic characteristics falls in the strongly agree or agree category. It means whether it is age wise, marital status-wise, caste, religion or economic dimension-wise women responded that they should participate in employment in Mumbai. Value of KMO test was 0.544 and chronbac’s alpha value was between 0.5-0.6, so the index falls in poor category and can be improved upon by adding more number of items.

Keywords: WEPPI, exploratory factor analysis, KMO test, Chronbac alpha

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Authors: Özge Özsoy

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Teachers usually complain that their students do not study enough to further practice the subjects they have covered in class. Teachers tend to focus on how often and hard they should study rather than finding out the main reasons why most students avoid doing any extra work to improve their skills. In this study, with the use of exploratory practice method, 40 English preparatory class students at Anadolu University will discuss this puzzle through an in-class discussion and create posters describing the reasons for and solutions to it. The overlapping data from the posters will be categorized in two sections as reasons and solutions in a final poster. The study aims at revealing the student perspective of a common puzzle that troubles many teachers.

Keywords: exploratory practice, extra work, puzzle, students, teachers

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Authors: Chaiyaset Promsri

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Based on the synthesis of related literatures, this research found thirteen related dimensions that involved the development of natural disaster preparedness awareness including hazard knowledge, hazard attitude, training for disaster preparedness, rehearsal and practice for disaster preparedness, cultural development for preparedness, public relations and communication, storytelling, disaster awareness game, simulation, past experience to natural disaster, information sharing with family members, and commitment to the community (time of living).  The 40-item of natural disaster preparedness awareness questionnaire was developed based on these thirteen dimensions. Data were collected from 595 participants in Bangkok metropolitan and vicinity. Cronbach's alpha was used to examine the internal consistency for this instrument. Reliability coefficient was 97, which was highly acceptable.  Exploratory Factor Analysis where principal axis factor analysis was employed. The Kaiser-Meyer-Olkin index of sampling adequacy was .973, indicating that the data represented a homogeneous collection of variables suitable for factor analysis. Bartlett's test of Sphericity was significant for the sample as Chi-Square = 23168.657, df = 780, and p-value < .0001, which indicated that the set of correlations in the correlation matrix was significantly different and acceptable for utilizing EFA. Factor extraction was done to determine the number of factors by using principal component analysis and varimax.  The result revealed that four factors had Eigen value greater than 1 with more than 60% cumulative of variance. Factor #1 had Eigen value of 22.270, and factor loadings ranged from 0.626-0.760. This factor was named as "Knowledge and Attitude of Natural Disaster Preparedness".  Factor #2 had Eigen value of 2.491, and factor loadings ranged from 0.596-0.696. This factor was named as "Training and Development". Factor #3 had Eigen value of 1.821, and factor loadings ranged from 0.643-0.777. This factor was named as "Building Experiences about Disaster Preparedness".  Factor #4 had Eigen value of 1.365, and factor loadings ranged from 0.657-0.760. This was named as "Family and Community". The results of this study provided support for the reliability and construct validity of natural disaster preparedness awareness for utilizing with populations similar to sample employed.

Keywords: natural disaster, disaster preparedness, disaster awareness, Thai citizens

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Authors: Patsawut Sukserm

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Oracy proficiency, encompassing both speaking and listening skills, is vital for EFL learners, yet Thai university students often face significant challenges in developing these abilities. This study aims to identify and analyze the barriers that hinder oracy proficiency in EFL learners. To achieve this, a questionnaire was developed based on a comprehensive review of the literature and administered to a large cohort of Thai EFL students. The data were subjected to exploratory factor analysis (EFA) to validate the questionnaire and uncover the underlying factors influencing learners’ performance. The results revealed that the Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy was 0.912, and Bartlett’s test of sphericity was significant at 2345.423 (p < 0.05), confirming the suitability for factor analysis. There are five main barriers in oracy proficiency, namely Listening and Comprehension Obstacles (LCO), Accent and Speech Understanding (ASU), Speaking Anxiety and Confidence Issues (SACI), Fluency and Expression Issues (FEI), and Grammar and Conversational Understanding (GCU), with eigenvalues ranging from 1.066 to 12.990, explaining 60.305 % of the variance of the 32 variables. These findings highlight the complexity of the challenges faced by Thai EFL learners and emphasize the need for diverse and authentic listening experiences, a supportive classroom environment, or balanced grammar instruction. The findings of the study suggest that educators, curriculum developers, and policy makers should implement evidence-based strategies to address these barriers in order to improve Thai EFL learners’ oral proficiency and enhance their overall academic and professional success. Also, this study will discuss these findings in depth, offering evidence-based strategies for addressing these barriers. Recommendations include integrating diverse and authentic listening experiences, fostering a supportive classroom environment, and providing targeted instruction in both speaking fluency and grammar. The study’s implications extend to educators, curriculum developers, and policymakers, offering practical solutions to enhance learners’ oracy proficiency and support their academic and professional development.

Keywords: exploratory factor analysis, barriers, oracy proficiency, EFL learners

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Authors: Muna Al-Kalbani, Thuwayba Al Barwani, Otherine Neisler, Hussain Alkharusi, David Clayton, Humaira Al-Sulaimani, Mohammad Khan, Hamad Al-Yahmadi

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This study describes the psychometric properties and factor structure of the University Readiness Survey (URS). Survey data were collected from sample of 2652 students from Sultan Qaboos University. Exploratory factor analysis identified ten significant factors underlining the structure. The results of Confirmatory factor analysis showed a good fit to the data where the indices for the revised model were χ2(df = 1669) = 6093.4; CFI = 0.900; GFI =0.926; PCLOSE = 1.00 and RMSAE = 0.030 where each of these indices were above threshold. The overall value of Cronbach’s alpha was 0.899 indicating that the instrument score was reliable. Results imply that the URS is a valid measure describing the college readiness pattern among Sultan Qaboos University students and the Arabic version could be used by university counselors to identify students’ readiness factors. Nevertheless, further validation of the of the USR is recommended.

Keywords: college readiness, confirmatory factor analysis, reliability, validity

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Authors: Iannick Gagnon, Alain April

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The performance of a metaheuristic on a given problem class depends on the class itself and the choice of parameters. Parameter tuning is the most time-consuming phase of the optimization process after the main calculations and it often nullifies the speed advantage of metaheuristics over traditional optimization algorithms. Several off-the-shelf parameter tuning algorithms are available, but when the objective function is expensive to evaluate, these can be prohibitively expensive to use. This paper presents a surrogate-like method for finding adequate parameters using fitness landscape analysis on simple benchmark functions and real-world objective functions. The result is a simple compound similarity metric based on the empirical correlation coefficient and a measure of convexity. It is then used to find the best benchmark functions to serve as surrogates. The near-optimal parameter set is then found using fractional factorial design. The real-world problem of NACA airfoil lift coefficient maximization is used as a preliminary proof of concept. The overall aim of this research is to reduce the computational overhead of metaheuristics parameterization.

Keywords: metaheuristics, stochastic optimization, particle swarm optimization, exploratory landscape analysis

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Authors: Elizabeth Valenti

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Objective: To date, there remains an absence of literature examining perceptions of mindfulness and mindful practices among college students, particularly among Christian students. The purpose of this mixed-methods, exploratory study was to gain a better understanding of students’ perception of mindfulness and assess current mindful practices. Methods: The mixed-methods, exploratory study examined data from freshmen undergraduate college students (N=107) enrolled in an introductory psychology course at a private, non-profit Christian university. Students completed a researcher-developed questionnaire containing both Likert and opened ended questions to assess knowledge about and perceptions of mindfulness, as well as current mindful practices. Results: Results of the thematic analysis revealed approximately half of the students had a limited understanding of mindfulness, with several reporting disadvantages. Most students listed prayer as a consistent practice, with a much smaller percentage of students consistently engaging in other mindful activities. Discussion: Implications for mindfulness education and the promotion of evidence-based methods, particularly in Christian communities, are discussed.

Keywords: mindfulness, mindful practices, perception, Christian, university students, mental health

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Authors: Hiromi Takafuji

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It is a well-known fact that health care and welfare are the foundation of human activities, and human service professionals such as nurses and child care workers support these activities. COVID-19 pandemic has made the severity of the working environment in these fields even more known. It is high time to discuss the work of human service workers for the sustainable development of the human environment. Early turnover has been recognized as a long-standing issue in these fields. In Japan, the attrition rate within three years of graduation for these occupations has remained high at about 40% for more than 20 years. One of the reasons for this is Reality Shock: RS, which refers to the stress caused by the gap between pre-employment expectations and the post-employment reality experienced by new workers. The purpose of this study was to academically elucidate the mechanism of RS among human service professionals and to contribute to countermeasures against it. Firstly, to explore the structure of the relationship between professionalism and workers' RS, an exploratory interview survey was conducted and analyzed by text mining and content analysis. The results showed that the expectation of professionalism influences RS as a pre-employment job expectation. Next, the expectations of professionalism were quantified and categorized, and the responses of a total of 282 human service work professionals, nurses, child care workers, and caregivers; were finalized for data analysis. The data were analyzed using exploratory factor analysis, confirmatory factor analysis, multiple regression analysis, and structural equation modeling techniques. The results revealed that self-control orientation and authority orientation by qualification had a direct positive significant impact on RS. On the other hand, interpersonal helping orientation and altruistic orientation were found to have a direct negative significant impact and an indirect positive significant impact on RS.; we were able to clarify the structure of work expectations that affect the RS of welfare professionals, which had not been clarified in previous studies. We also explained the limitations, practical implications, and directions for future research.

Keywords: human service professional, new hire turnover, SEM, reality shock

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Authors: Tai Mei Kin, Omar Abdull Kareem

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In light of globalization, educational reform has become a top priority for many countries. However, the task of leading change effectively requires a multidimensional set of competencies. Over the past two decades, technical competencies of principal change leadership have been extensively analysed and discussed. Comparatively, little research has been conducted in Malaysian education context on non-technical competencies or popularly known as emotional intelligence, which is equally crucial for the success of change. This article provides a validation of the Principal Change Leadership Non-Technical Competencies (PCLnTC) Scale, a tool that practitioners can easily use to assess school principals’ level of change leadership non-technical competencies that facilitate change and maximize change effectiveness. The overall coherence of the PCLnTC model was constructed by incorporating three theories: a)the change leadership theory whereby leading change is the fundamental role of a leader; b)competency theory in which leadership can be taught and learned; and c)the concept of emotional intelligence whereby it can be developed, fostered and taught. An exploratory factor analysis (EFA) was used to determine the underlying factor structure of PCLnTC model. Before conducting EFA, five important pilot test approaches were conducted to ensure the validity and reliability of the instrument: a)reviewed by academic colleagues; b)verification and comments from panel; c)evaluation on questionnaire format, syntax, design, and completion time; d)evaluation of item clarity; and e)assessment of internal consistency reliability. A total of 335 teachers from 12 High Performing Secondary School in Malaysia completed the survey. The PCLnTCS with six points Liker-type scale were subjected to Principal Components Analysis. The analysis yielded a three-factor solution namely, a)Interpersonal Sensitivity; b)Flexibility; and c)Motivation, explaining a total 74.326 per cent of the variance. Based on the results, implications for instrument revisions are discussed and specifications for future confirmatory factor analysis are delineated.

Keywords: exploratory factor analysis, principal change leadership non-technical competencies (PCLnTC), interpersonal sensitivity, flexibility, motivation

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Authors: Ana Paula Camelo, Keila Sanches

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The Geographically Weighted Regression (GWR) was proposed in geography literature to allow relationship in a regression model to vary over space. In Brazil, the agricultural exploitation of the Cerrado Biome is the main cause of deforestation. In this study, we propose a methodology using geostatistical methods to characterize the spatial dependence of deforestation in the Cerrado based on agricultural production indicators. Therefore, it was used the set of exploratory spatial data analysis tools (ESDA) and confirmatory analysis using GWR. It was made the calibration a non-spatial model, evaluation the nature of the regression curve, election of the variables by stepwise process and multicollinearity analysis. After the evaluation of the non-spatial model was processed the spatial-regression model, statistic evaluation of the intercept and verification of its effect on calibration. In an analysis of Spearman’s correlation the results between deforestation and livestock was +0.783 and with soybeans +0.405. The model presented R²=0.936 and showed a strong spatial dependence of agricultural activity of soybeans associated to maize and cotton crops. The GWR is a very effective tool presenting results closer to the reality of deforestation in the Cerrado when compared with other analysis.

Keywords: deforestation, geographically weighted regression, land use, spatial analysis

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Authors: Sadeq Al Yaari, Ayman Al Yaari, Adham Al Yaari, Montaha Al Yaari, Aayah Al Yaari, Sajedah Al Yaari

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Objective: This research aims at investigating the research ethics in the field of science. Method: It is an exploratory research wherein the researchers attempted to cover the phenomenon at hand from all specialists’ viewpoints. Results Discussion is based upon the findings resulted from the analysis the researcher undertook. Concerning the results’ prediction, the researcher needs first to seek highly qualified people in the field of research as well as in the field of statistics who share the philosophy of the research. Then s/he should make sure that s/he is adequately trained in the specific techniques, methods and statically programs that are used at the study. S/he should also believe in continually analysis for the data in the most current methods.

Keywords: research ethics, legal, rights, psychoneurolinguistics

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Authors: Michael Gidey Gebru

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Most of Data Envelopment Analysis models operate in a static environment with input and output parameters that are chosen by deterministic data. However, due to ambiguity brought on shifting market conditions, input and output data are not always precisely gathered in real-world scenarios. Fuzzy numbers can be used to address this kind of ambiguity in input and output data. Therefore, this work aims to expand crisp Data Envelopment Analysis into Data Envelopment Analysis with fuzzy environment. In this study, the input and output data are regarded as fuzzy triangular numbers. Then, the Data Envelopment Analysis model with fuzzy environment is solved using a multi-objective method to gauge the Decision Making Units' efficiency. Finally, the developed Data Envelopment Analysis model is illustrated with an application on real data 50 educational institutions.

Keywords: efficiency, Data Envelopment Analysis, fuzzy, higher education, input, output

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Authors: Ammarah Irum, Muhammad Ali Tahir

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Document-level sentiment analysis in Urdu is a challenging Natural Language Processing (NLP) task due to the difficulty of working with lengthy texts in a language with constrained resources. Deep learning models, which are complex neural network architectures, are well-suited to text-based applications in addition to data formats like audio, image, and video. To investigate the potential of deep learning for Urdu sentiment analysis, we implemented five different deep learning models, including Bidirectional Long Short Term Memory (BiLSTM), Convolutional Neural Network (CNN), Convolutional Neural Network with Bidirectional Long Short Term Memory (CNN-BiLSTM), and Bidirectional Encoder Representation from Transformer (BERT). In this study, we developed a hybrid deep learning model called BiLSTM-Single Layer Multi Filter Convolutional Neural Network (BiLSTM-SLMFCNN) by fusing BiLSTM and CNN architecture. The proposed and baseline techniques are applied on Urdu Customer Support data set and IMDB Urdu movie review data set by using pre-trained Urdu word embedding that are suitable for sentiment analysis at the document level. Results of these techniques are evaluated and our proposed model outperforms all other deep learning techniques for Urdu sentiment analysis. BiLSTM-SLMFCNN outperformed the baseline deep learning models and achieved 83%, 79%, 83% and 94% accuracy on small, medium and large sized IMDB Urdu movie review data set and Urdu Customer Support data set respectively.

Keywords: urdu sentiment analysis, deep learning, natural language processing, opinion mining, low-resource language

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Authors: Abdulrahman S. Alqahtani

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The revolution of computing and networks could revolutionise terrorism in the same way that it has brought about changes in other aspects of life. The modern technological era has faced countries with a new set of security challenges. There are many states and potential adversaries who have the potential and capacity in cyberspace, which makes them able to carry out cyber-attacks in the future. Some of them are currently conducting surveillance, gathering and analysis of technical information, and mapping of networks and nodes and infrastructure of opponents, which may be exploited in future conflicts. This poster presents the results of the quantitative study (survey) to test the validity of the proposed theoretical framework for the cyber terrorist threats. This theoretical framework will help to in-depth understand these new digital terrorist threats. It may also be a practical guide for managers and technicians in critical infrastructure, to understand and assess the threats they face. It might also be the foundation for building a national strategy to counter cyberterrorism. In the beginning, it provides basic information about the data. To purify the data, reliability and exploratory factor analysis, as well as confirmatory factor analysis (CFA) were performed. Then, Structural Equation Modelling (SEM) was utilised to test the final model of the theory and to assess the overall goodness-of-fit between the proposed model and the collected data set.

Keywords: cyberterrorism, critical infrastructure, , national security, theoretical framework, terrorism

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Authors: Pawit Mongkolprasit, Proud Arunrangsiwed

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Lately, many media organizations in Thailand have started to produce 3D animation, so the quality of personnel should be identified. As an instructor in the school of Animation and Multimedia, the researchers have to prepare the students, suitable for the need of industry. The current study used exploratory research design to establish the knowledge of about this issue, including the required qualification of employees and the potential of animation industry in Thailand. The interview sessions involved three key informants from three well-known organizations. The interview data was used to design a questionnaire for the confirmation phase. The overall results showed that the industry needed an individual with 3D animation skill, computer graphic skills, good communication skills, a high responsibility, and an ability to finish the project on time. Moreover, it is also found that there were currently various kinds of media where 3D animation has been involved, such as films, TV variety, TV advertising, online advertising, and application on mobile device.

Keywords: 3D animation, animation industry, marketing trend, Thailand animation

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Authors: Liang Zhao, Jili Zhang, Chongquan Zhong

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Energy consumption data, in particular those involving public buildings, are impacted by many factors: the building structure, climate/environmental parameters, construction, system operating condition, and user behavior patterns. Traditional methods for data analysis are insufficient. This paper delves into the data mining technology to determine its application in the analysis of building energy consumption data including energy consumption prediction, fault diagnosis, and optimal operation. Recent literature are reviewed and summarized, the problems faced by data mining technology in the area of energy consumption data analysis are enumerated, and research points for future studies are given.

Keywords: data mining, data analysis, prediction, optimization, building operational performance

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Authors: Shengnan Chen, Shuhua Wang

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Successful production of hydrocarbon from unconventional tight oil reserves has changed the energy landscape in North America. The oil contained within these reservoirs typically will not flow to the wellbore at economic rates without assistance from advanced horizontal well and multi-stage hydraulic fracturing. Efficient and economic development of these reserves is a priority of society, government, and industry, especially under the current low oil prices. Meanwhile, society needs technological and process innovations to enhance oil recovery while concurrently reducing environmental impacts. Recently, big data analysis and artificial intelligence become very popular, developing data-driven insights for better designs and decisions in various engineering disciplines. However, the application of data mining in petroleum engineering is still in its infancy. The objective of this research aims to apply intelligent data analysis and data-driven models to exploit unconventional oil reserves both efficiently and economically. More specifically, a comprehensive database including the reservoir geological data, reservoir geophysical data, well completion data and production data for thousands of wells is firstly established to discover the valuable insights and knowledge related to tight oil reserves development. Several data analysis methods are introduced to analysis such a huge dataset. For example, K-means clustering is used to partition all observations into clusters; principle component analysis is applied to emphasize the variation and bring out strong patterns in the dataset, making the big data easy to explore and visualize; exploratory factor analysis (EFA) is used to identify the complex interrelationships between well completion data and well production data. Different data mining techniques, such as artificial neural network, fuzzy logic, and machine learning technique are then summarized, and appropriate ones are selected to analyze the database based on the prediction accuracy, model robustness, and reproducibility. Advanced knowledge and patterned are finally recognized and integrated into a modified self-adaptive differential evolution optimization workflow to enhance the oil recovery and maximize the net present value (NPV) of the unconventional oil resources. This research will advance the knowledge in the development of unconventional oil reserves and bridge the gap between the big data and performance optimizations in these formations. The newly developed data-driven optimization workflow is a powerful approach to guide field operation, which leads to better designs, higher oil recovery and economic return of future wells in the unconventional oil reserves.

Keywords: big data, artificial intelligence, enhance oil recovery, unconventional oil reserves

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Authors: Minaj Ranjita Singh, Meghna Mukherjee, Abhijeet Jadhav

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This is an exploratory qualitative study with an objective to understand the bathing practices followed by rural women and its consequences. Access to safe bathing spaces in rural India is a neglected issue due to which women are affected in various ways. Today, government policies are largely focused towards the building of toilets, but no importance has been given to the construction of bathrooms. Both qualitative and quantitative data were collected using in-depth interviews and focused group discussions with rural women in six villages of Odisha, India. The study was approved by an Institutional Research and Ethics Committee, and informed consent was taken from participants. For most of the participants, the access to water, bathing space and toilet was compromised posing various challenges in their daily lives. Women's daily schedule, hygiene practices, dignity, and health are greatly affected due to this lack. Since bathing in the open has been an ancient practice, the community's perception is benign towards the hardship of women. Lack of exposure to concealed bathing, necessary funds, and competing priorities are some of the household level factors which never let them think about having bathrooms and the lack of water supply, proper drainage system, subsidy or financial support are the governance and policy related factors which prevent their access to secured bathing spaces.

Keywords: bathrooms, dignity, exploratory, rural, qualitative, women's health, women

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Authors: Dimitriu Corneliu-Ioan, Gheorghe FrațIlă

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Romania has one of the highest rates of road deaths among European Union Member States, and there is a concern that the country will not meet its goal of "zero deaths" by 2050. The European Union also aims to halve the number of people seriously injured in road accidents by 2030. Therefore, there is a need to improve road infrastructure safety management in Romania. The aim of this study is to analyze road accident data through statistical methods to assess the current state of road infrastructure safety in Bucharest. The study also aims to identify trends and make forecasts regarding serious road accidents and their consequences. The objective is to provide insights that can help prioritize measures to increase road safety, particularly in urban areas. The research utilizes statistical analysis methods, including exploratory analysis and descriptive statistics. Databases from the Traffic Police and the Romanian Road Authority are analyzed using Excel. Road risks are compared with the main causes of road accidents to identify correlations. The study emphasizes the need for better quality and more diverse collection of road accident data for effective analysis in the field of road infrastructure engineering. The research findings highlight the importance of prioritizing measures to improve road safety in urban areas, where serious accidents and their consequences are more frequent. There is a correlation between the measures ordered by road safety auditors and the main causes of serious accidents in Bucharest. The study also reveals the significant social costs of road accidents, amounting to approximately 3% of GDP, emphasizing the need for collaboration between local and central administrations in allocating resources for road safety. This research contributes to a clearer understanding of the current road infrastructure safety situation in Romania. The findings provide critical insights that can aid decision-makers in allocating resources efficiently and institutionally cooperating to achieve sustainable road safety. The data used for this study are collected from the Traffic Police and the Romanian Road Authority. The data processing involves exploratory analysis and descriptive statistics using the Excel tool. The analysis allows for a better understanding of the factors contributing to the current road safety situation and helps inform managerial decisions to eliminate or reduce road risks. The study addresses the state of road infrastructure safety in Bucharest and analyzes the trends and forecasts regarding serious road accidents and their consequences. It studies the correlation between road safety measures and the main causes of serious accidents. To improve road safety, cooperation between local and central administrations towards joint financial efforts is important. This research highlights the need for statistical data processing methods to substantiate managerial decisions in road infrastructure management. It emphasizes the importance of improving the quality and diversity of road accident data collection. The research findings provide a critical perspective on the current road safety situation in Romania and offer insights to identify appropriate solutions to reduce the number of serious road accidents in the future.

Keywords: road death rate, strategic objective, serious road accidents, road safety, statistical analysis

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Authors: Phan Thi Song Thuong

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This paper uses data from a survey on the green consumption behavior of Hanoi residents in October 2022. Data was gathered from a survey conducted in ten districts in the center of Hanoi, with 393 respondents. The hypothesis focuses on understanding the factors that may affect green consumption behavior, such as demographic characteristics, concerns about the environment and health, people living around, self-efficiency, and mass media. A number of methods, such as the T-test, exploratory factor analysis, and a linear regression model, are used to prove the hypotheses. Accordingly, the results show that gender, age, and education level have separate effects on the green consumption behavior of respondents.

Keywords: green consumption, urban residents, environment, sustainable, linear regression

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Authors: José Luis Gallizo, Pilar Gargallo, Ramon Saladrigues, Manuel Salvador

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This study offers an exploratory statistical analysis of the persistence of annual profits across a sample of firms from different European Union (EU) countries. To this end, a hierarchical Bayesian dynamic model has been used which enables the annual behaviour of those profits to be broken down into a permanent structural and a transitory component, while also distinguishing between general effects affecting the industry as a whole to which each firm belongs and specific effects affecting each firm in particular. This breakdown enables the relative importance of those fundamental components to be more accurately evaluated by country and sector. Furthermore, Bayesian approach allows for testing different hypotheses about the homogeneity of the behaviour of the above components with respect to the sector and the country where the firm develops its activity. The data analysed come from a sample of 23,293 firms in EU countries selected from the AMADEUS data-base. The period analysed ran from 1999 to 2007 and 21 sectors were analysed, chosen in such a way that there was a sufficiently large number of firms in each country sector combination for the industry effects to be estimated accurately enough for meaningful comparisons to be made by sector and country. The analysis has been conducted by sector and by country from a Bayesian perspective, thus making the study more flexible and realistic since the estimates obtained do not depend on asymptotic results. In general terms, the study finds that, although the industry effects are significant, more important are the firm specific effects. That importance varies depending on the sector or the country in which the firm carries out its activity. The influence of firm effects accounts for around 81% of total variation and display a significantly lower degree of persistence, with adjustment speeds oscillating around 34%. However, this pattern is not homogeneous but depends on the sector and country analysed. Industry effects depends also on sector and country analysed have a more marginal importance, being significantly more persistent, with adjustment speeds oscillating around 7-8% with this degree of persistence being very similar for most of sectors and countries analysed.

Keywords: dynamic models, Bayesian inference, MCMC, abnormal returns, persistence of profits, return on assets

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Authors: Fuad A. A. Trayek, Tunku Badariah Tunku Ahmad, Mohamad Sahari Nordin, Mohammed AM Dwikat

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This paper reports on the results of an exploratory factor analysis procedure applied on the e-learning readiness data obtained from a survey of four hundred and seventy-nine (N = 479) teachers from secondary schools in Nablus, Palestine. The data were drawn from a 23-item Likert questionnaire measuring e-learning readiness based on Chapnick's conception of the construct. Principal axis factoring (PAF) with Promax rotation applied on the data extracted four distinct factors supporting four of Chapnick's e-learning readiness dimensions, namely technological readiness, psychological readiness, infrastructure readiness and equipment readiness. Together these four dimensions explained 56% of the variance. These findings provide further support for the construct validity of the items and for the existence of these four factors that measure e-learning readiness.

Keywords: e-learning, e-learning readiness, technological readiness, psychological readiness, principal axis factoring

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Authors: Alexandre Bedard, Johane Brunet, Wendellyn Reid

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Public Television is constantly trying to maintain and develop its audience. And to achieve those goals, it needs a strong and clear vision. Vision or envision is a multidimensional process; it is simultaneously a conduit that orients and fixes the future, an idea that comes before the strategy and a mean by which action is accomplished, from a business perspective. Also, vision is often studied from a prescriptive and instrumental manner. Based on our understanding of the literature, we were able to explain how envisioning, as a process, is a creative one; it takes place in the mind and uses wisdom and intelligence through a process of evaluation, analysis and creation. Through an aggregation of the literature, we build a model of the envisioning process, based on past experiences, perceptions and knowledge and influenced by the context, being the individual, the organization and the environment. With exploratory research in which vision was deciphered through the discourse, through a qualitative and abductive approach and a grounded theory perspective, we explored three extreme cases, with eighteen interviews with experts, leaders, politicians, actors of the industry, etc. and more than twenty hours of interviews in three different countries. We compared the strategy, the business model, and the political and legal forces. We also looked at the history of each industry from an inertial point of view. Our analysis of the data revealed that a legitimacy effect due to the audience, the innovation and the creativity of the institutions was at the cornerstone of what would influence the envisioning process. This allowed us to identify how different the process was for Canadian, French and UK public broadcasters, although we concluded that the three of them had a socially constructed vision for their future, based on stakeholder management and an emerging role for the managers: ideas brokers.

Keywords: envisioning process, international comparison, television, vision

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Authors: Patrizia Barroero, Saliha Yagoubi

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Mindfulness meditation practices in the context of higher education are becoming more and more common. Some of the reported benefits of mediation interventions and workshops include: improved focus, general well-being, diminished stress, and even increased resilience and grit. A series of workshops free to students, faculty, and staff was offered twice a week over two semesters at Hudson County Community College, New Jersey. The results of an exploratory study based on participants’ subjective reactions to these workshops will be presented. A qualitative approach was used to collect and analyze the data and a hermeneutic phenomenological perspective served as a framework for the research design and data collection and analysis. The data collected includes three recorded videos of semi-structured interviews and several written surveys submitted by volunteer participants.

Keywords: mindfulness meditation practices, stress reduction, resilience, grit, higher education success, qualitative research

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Authors: Muneer Abbad

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This research explains the e-learning usage in a university in Jordan. Unified theory of acceptance and use of technology (UTAUT) model has been used as a base model to explain the usage. UTAUT is a model of individual acceptance that is compiled mainly from different models of technology acceptance. This research is the initial part from full explanations of the users' acceptance model that use Structural Equation Modelling (SEM) method to explain the users' acceptance of the e-learning systems based on UTAUT model. In this part data has been collected and prepared for further analysis. The main factors of UTAUT model has been tested as different factors using exploratory factor analysis (EFA). The second phase will be confirmatory factor analysis (CFA) and SEM to explain the users' acceptance of e-learning systems.

Keywords: e-learning, moodle, adoption, Unified Theory of Acceptance and Use of Technology (UTAUT)

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Authors: Kwaku Damoah

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The agricultural sector is characterized by continuous transformation, driven by factors such as demographic shifts, evolving consumer preferences, climate change, and migration trends. This dynamic environment presents complex challenges for key stakeholders including farmers, governments, and investors, who must navigate these changes to achieve optimal investment returns. To effectively predict market trends and uncover promising investment opportunities, a systematic, data-driven approach is essential. This paper introduces the Structural Evolution Index (SEI), a machine learning-based methodology. SEI is specifically designed to analyse long-term trends and forecast the potential of emerging agricultural products for investment. Versatile in application, it evaluates various agricultural metrics such as production, yield, trade, land use, and consumption, providing a comprehensive view of the evolution within agricultural markets. By harnessing data from the UN Food and Agricultural Organisation (FAOSTAT), this study demonstrates the SEI's capabilities through Comparative Exploratory Analysis and evaluation of international trade in agricultural products, focusing on Malaysia and Singapore. The SEI methodology reveals intricate patterns and transitions within the agricultural sector, enabling stakeholders to strategically identify and capitalize on emerging markets. This predictive framework is a powerful tool for decision-makers, offering crucial insights that help anticipate market shifts and align investments with anticipated returns.

Keywords: agricultural investment, algorithm, comparative exploratory analytics, machine learning, market trends, predictive analytics, structural evolution index

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Authors: Maximilian Engelhardt, Stephan Seeck

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The COVID-19 pandemic is accelerating the already growing field of e-commerce. The resulting urban freight transport volume leads to traffic and negative environmental impact. Furthermore, the service level of parcel logistics service provider is lacking far behind the expectations of consumer. These challenges can be solved by radically reorganize the urban last mile distribution structure: parcels could be consolidated in a micro hub within the inner city and delivered within time windows by cargo bike. This approach leads to a significant improvement of consumer satisfaction with their overall delivery experience. However, this approach also leads to significantly increased costs per parcel. While there is a relevant share of online shoppers that are willing to pay for such a delivery service there are no deeper insights about this target group available in the literature. Being aware of the importance of knowing target groups for businesses, the aim of this paper is to elaborate the most important factors that determine the willingness to pay for sustainable and service-oriented parcel delivery (factor analysis) and to derive customer segments (cluster analysis). In order to answer those questions, a data set is analyzed using quantitative methods of multivariate statistics. The data set was generated via an online survey in September and October 2020 within the five largest cities in Germany (n = 1.071). The data set contains socio-demographic, living-related and value-related variables, e.g. age, income, city, living situation and willingness to pay. In a prior work of the author, the data was analyzed applying descriptive and inference statistical methods that only provided limited insights regarding the above-mentioned research questions. The analysis in an exploratory way using factor and cluster analysis promise deeper insights of relevant influencing factors and segments for user behavior of the mentioned parcel delivery concept. The analysis model is built and implemented with help of the statistical software language R. The data analysis is currently performed and will be completed in December 2021. It is expected that the results will show the most relevant factors that are determining user behavior of sustainable and service-oriented parcel deliveries (e.g. age, current service experience, willingness to pay) and give deeper insights in characteristics that describe the segments that are more or less willing to pay for a better parcel delivery service. Based on the expected results, relevant implications and conclusions can be derived for startups that are about to change the way parcels are delivered: more customer-orientated by time window-delivery and parcel consolidation, more environmental-friendly by cargo bike. The results will give detailed insights regarding their target groups of parcel recipients. Further research can be conducted by exploring alternative revenue models (beyond the parcel recipient) that could compensate the additional costs, e.g. online-shops that increase their service-level or municipalities that reduce traffic on their streets.

Keywords: customer segmentation, e-commerce, last mile delivery, parcel service, urban logistics, willingness-to-pay

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Authors: Bolin Hua

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In city governance, various data are involved, including city component data, demographic data, housing data and all kinds of business data. These data reflects different aspects of people, events and activities. Data generated from various systems are different in form and data source are different because they may come from different sectors. In order to reflect one or several facets of an event or rule, data from multiple sources need fusion together. Data from different sources using different ways of collection raised several issues which need to be resolved. Problem of data fusion include data update and synchronization, data exchange and sharing, file parsing and entry, duplicate data and its comparison, resource catalogue construction. Governments adopt statistical analysis, time series analysis, extrapolation, monitoring analysis, value mining, scenario prediction in order to achieve pattern discovery, law verification, root cause analysis and public opinion monitoring. The result of Multi-source data fusion is to form a uniform central database, which includes people data, location data, object data, and institution data, business data and space data. We need to use meta data to be referred to and read when application needs to access, manipulate and display the data. A uniform meta data management ensures effectiveness and consistency of data in the process of data exchange, data modeling, data cleansing, data loading, data storing, data analysis, data search and data delivery.

Keywords: multi-source data fusion, urban comprehensive management, information fusion, government data

Procedia PDF Downloads 399