Aim/Purpose: This study seeks to present a learning model of discrete mathematics elements, elucidate the content of teaching, and validate the effectiveness of this learning in a digital education context. Background: Teaching discrete mathematics in the realm of digital education poses challenges, particularly in crafting the optimal model, content, tools, and methods tailored for aspiring computer science teachers. The study draws from both a comprehensive review of relevant literature and the synthesis of the authors’ pedagogical experiences. Methodology: The research utilized a system-activity approach and aligned with the State Educational Standard. It further integrated the theory of continuous education as its psychological and pedagogical foundation. Contribution: A unique model for instructing discrete mathematics elements to future computer science educators has been proposed. This model is underpinned by informative, technological, and personal competencies, intertwined with the mathematical bedrock of computer science. Findings: The study revealed the importance of holistic teaching of discrete mathematics elements for computer science teacher aspirants in line with the Informatics educational programs. An elective course, “Elements of Discrete Mathematics in Computer Science”, comprising three modules, was outlined. Practical examples spotlighting elements of mathematical logic and graph theory of discrete mathematics in programming and computer science were showcased. Recommendations for Practitioners: Future computer science educators should deeply integrate discrete mathematics elements in their teaching methodologies, especially when aligning with professional disciplines of the Informatics educational program. Recommendation for Researchers: Further exploration is recommended on the seamless integration of discrete mathematics elements in diverse computer science curricula, optimizing for varied learning outcomes and student profiles. Impact on Society: Enhancing the quality of teaching discrete mathematics to future computer science teachers can lead to better-educated professionals, driving advancements in the tech industry and contributing to societal progress. Future Research: There is scope to explore the wider applications of the discrete mathematics elements model in varied computer science sub-disciplines, and its adaptability across different educational frameworks.

Aim/Purpose: The study examined how the developed mobile courseware can be used as instructional material to improve senior high school statistics and probability learning, particularly during distance learning caused by the COVID-19 pandemic. The study also aims to assess the gamified mobile courseware’s engagement, functionality, aesthetics, and information quality using the Mobile App Rating Scale (MARS) and a researcher-made Gamified Mobile Courseware Assessment Tool (GMCET). Background: The need to investigate the effectiveness of incorporating game-based elements into mathematics courses through innovative instructional materials inspired the study. The COVID-19 pandemic has made distance learning a necessity, and gamified mobile courseware is a potential solution to improve learning outcomes and engagement in mathematics courses. Methodology: The study employed a descriptive-evaluative method with quantitative and qualitative data to achieve its objectives. Five IT practitioners assessed the developed courseware using the MARS regarding engagement, functionality, aesthetics, and information. A researcher-made GMCET was also used to evaluate the app’s content quality, learning objectives, content presentation, learning assessment, and usability. Five math experts and 12 math teachers rated the app using the GMCET. The study used weighted mean to analyze the quantitative data and content analysis for the qualitative data. Contribution: The study provides insights into the strengths and weaknesses of gamified mobile courseware from the perspective of IT practitioners, math experts, and math teachers. The study’s findings can inform improvements in future iterations of courseware, and the study provides a valuable guide for practitioners looking to develop gamified mobile courseware for mathematics courses. Findings: The quantitative results based on the weighted mean indicate that the IT practitioners had a moderately positive perception of the developed courseware across all categories. At the same time, the math teachers and math experts showed highly positive perceptions of the gamified mobile courseware in Statistics and Probability, rating it highly across all categories. The qualitative data analysis through content analysis highlights the need for improving the user interface, usability, user experience design, user control, flexibility in interaction, data quality, reliability, and user privacy of the developed app. Recommendations for Practitioners: Practitioners can use the study’s findings to improve the design of gamified mobile courseware for mathematics courses and other content areas. The study recommends that practitioners focus on improving the user interface, usability, user experience design, user control, flexibility in interaction, data quality, reliability, and user privacy of gamified mobile courseware. Recommendation for Researchers: Future research can build on this study’s findings by exploring the use of gamified mobile courseware in other mathematical courses and other subject areas. Further research can also examine how gamified mobile courseware can improve learning outcomes for students with different learning needs. Impact on Society: The study’s findings could improve the effectiveness of gamified mobile courseware in enhancing student learning outcomes in mathematics courses. This can lead to better student performance, improved engagement, and increased interest in mathematics courses, positively impacting society. Future Research: Future research can explore using gamified mobile courseware in other mathematics courses and other subject areas. Additionally, future studies can examine how gamified mobile courseware can improve learning outcomes for students with different learning needs. Further research can also investigate the impact of gamified mobile courseware on student motivation, interest, and performance in mathematics courses.

Aim/Purpose: This study presents some activities that integrate computational thinking (CT) into mathematics lessons utilizing GeoGebra to promote constructionist learning. Background: CT activities in the Indonesian curriculum are dominated by worked examples with less plugged-mode activities that might hinder students from acquiring CT skills. Therefore, we developed mathematics and CT (math+CT) lessons to promote students’ constructionist key behaviors while learning. Methodology: The researchers utilized an educational design research (EDR) to guide the lesson’s development. The lesson featured 11 applets and 22 short questions developed in GeoGebra. To improve the lesson, it was sent to eight mathematics teachers and an expert in educational technology for feedback, and the lesson was improved accordingly. The improved lessons were then piloted with 17 students, during which the collaborating mathematics teachers taught the lessons. Data were collected through the students’ work on GeoGebra, screen recording when they approached the activities, and interviews. We used content analysis to analyze the qualitative data and presented descriptive statistics to quantitative data. Contribution: This study provided an example and insight into how CT can be enhanced in mathematics lessons in a constructionist manner. Findings: Students were active in learning mathematics and CT, especially when they were engaged in programming and debugging tasks. Recommendations for Practitioners: Educators are recommended to use familiar mathematics software such as GeoGebra to support students’ CT skills while learning mathematics. Additionally, our applets are better run on big-screen devices to optimize students’ CT programming and debugging skills. Moreover, it is recommended that students work collaboratively to benefit from peer feedback and discussion. Recommendation for Researchers: Collaboration with teachers will help researchers better understand the situation in the classroom and how the students will respond to the activities. Additionally, it is important to provide more time for students to get familiar with GeoGebra and start with fewer errors to debug. Future Research: Further research can explore more mathematics topics when integrating CT utilizing GeoGebra or other mathematics software or implement the lessons with a larger classroom size to provide a more generalizable result and deeper understanding.

Action recognition plays a vital role in analysing human body behaviour and has significant implications for research and education. However, traditional recognition methods often suffer from issues such as inaccurate time and spatial feature vectors. Therefore, this study addresses the problem of inaccurate recognition of aerobic gymnastics action image data and proposes a visualised three-dimensional convolutional neural network algorithm-based action recognition model. This model incorporates unsupervised visualisation methods into the traditional network and enhances data recognition capabilities through the introduction of a random noise perturbation enhancement algorithm. The research results indicate that the data augmented with noise perturbation achieves the lowest mean square error, reducing the error value from 0.3352 to 0.3095. The use of unsupervised visualisation analysis enables clearer recognition of human actions, and the algorithm model is capable of accurately recognising aerobic movements. Compared to traditional algorithms, the new algorithm exhibits higher recognition accuracy and superior performance.

Numerous scandals due to unethical decisions occur despite the growing use of decision support systems (DSS). Several scholars recommend incorporating ethical attributes along with business requirements in DSS design. However, little guidance exists to indicate which ethical attributes to include and the importance ethical attributes should be given in comparison to business requirements. This study addresses this deficiency by identifying ethical requirements to integrate in DSS design drawn from the business ethics literature. This study conducted a large-scale empirical survey with information technology decision-makers to examine the relative importance of DSS fit with ethical and business requirements as well as the appropriate balance of those requirements on perceived DSS performance. The results show that decision makers perceive better DSS performance when the ethical and business requirements align with its organisation's beliefs than from ethical or business requirements alone.

The phenomenon of cloud computing and related innovations such as Big Data have given rise to many fundamental changes that are evident in information and data. Managing, measuring and developing business value from the plethora of this new data has significant impact on many corporate agendas, particularly in relation to the successful implementation of business intelligence and analytics (BI&A). However, although the influence of Big Data has fundamentally changed the IT application landscape, the metrics for measuring success and in particular, the quality of information, have not evolved. The measurement of information quality and the antecedent factors that influence information has also been identified as an area that has suffered from a lack of research in recent decades. Given the rapid increase in data volume and the growth and ubiquitous use of BI&A systems in organisations, there is an urgent need for accurate metrics to identify information quality.

This paper analyses the adoption of learning analytics to predict at-risk students. A total of 233 research articles between 2004 and 2023 were collected from Scopus for this study. They were analysed in terms of the relevant types and sources of data, targets of prediction, learning analytics methods, and performance metrics. The results show that data related to students' academic performance, socio-demographics, and learning behaviours have been commonly collected. Most studies have addressed the identification of students who have a higher chance of poor academic performance or dropping out of their courses. Decision trees, random forests, and artificial neural networks are the most frequently used techniques for prediction, with ensemble methods gaining popularity in recent years. Classification accuracy, recall, sensitivity, and true positive rate are commonly used as performance metrics for evaluation. The results reveal the potential of learning analytics for informing timely and evidence-based support for at-risk students.

International universities are changing with their style of operation, mode of teaching and learning operations. This change is noticeable rapidly in India and also in international contexts due to healthy and innovative methods, educational strategies, and nomenclature throughout the world. Technologies are changing rapidly, including ICT. Different subjects are developed in the fields of IT and computing with the interaction or applications to other fields, viz. health informatics, bio informatics, agriculture informatics, and so on. Agricultural informatics is an interdisciplinary subject dedicated to combining information technology and information science utilisation in agricultural sciences. The digital agriculture is powered by agriculture informatics practice. For teaching, research and development of any subject educational methods is considered as important and various educational programs are there in this regard viz. Bachelor of Education, Master of Education, PhD in Education, etc. Degrees are also available to deal with the subjects and agricultural informatics should not be an exception of this. In this context, Doctor of Education (EdD or DEd) is an emerging degree having features of skill sets, courses and research work. This paper proposed on EdD program with agricultural informatics specialisation for improving healthy agriculture system. Here, a proposed model core curriculum is also presented.

In the digital technology era, mobile devices have an important rule to deploy a copy of data and information through the network. An electronic reader (eReader) allows readers to read written materials in an electronic manner that is available in many models. The objective of this study is to evaluate the usage of eReader by higher education students. We firstly identified the most frequently used eReader by surveying higher education students. The survey results showed that Apple iPad, Amazon Kindle, and Samsung Tablet are the most popular eReader devices used by higher education students. We presented these results, and then we analyzed the surveyed results in detail in order to develop an evaluation metric of the eReader in a mobile platform that clearly allows the selection of the most suitable eReader for higher education students. The main contribution of this paper is the development of a set of criteria that can be used by students in the selection of an eReader that matches their specific needs and requirements.

Aim/Purpose: Assess the affordances and constraints of SimReal+ in teacher education Background There is a huge interest in visualizations in mathematics education, but there is little empirical support for their use in educational settings Methodology: Single case study with 22 participants from one class in teacher education. Quantitative and qualitative methods to collect students’ responses to a survey questionnaire and open-ended questions Contribution: The paper contributes to the understanding of affordances and constraints of visualization tools in mathematics education Findings: The visualization tool SimReal+ has potential for learning mathematics in teacher education, but the user interface should be improved to make it more usable for different users. Teachers need to consider technological and pedagogical affordances of SimReal+ at the student, classroom, and mathematics subject level Recommendations for Practitioners: Address technological and pedagogical affordances of SimReal+ Recommendation for Researchers: Improve the design of SimReal+ to make it technologically and pedagogically more usable Impact on Society: Understand the affordances and constraints of visualization tools in education Future Research: Implement a next cycle of experimentation with SimReal+ in teacher education to ensure more validity and reliability

Aim/Purpose: Student dropout in higher education institutions is a universal problem. This study identifies the characteristics of dropout. In addition, it develops a mathematical model to predict students who may dropout. Methodology: The paper develops a mathematical model to predict students who may dropout. The sample includes 555 freshmen in a non-profit private university. The study uses both descriptive statistics, such as cross tabulation, and a binary regression model to predict student dropout. Contribution: There are two major contributions for the paper. First, it identifies the dropout rates of each group, a finding that may be used to better allocate resources at higher education institutions. Second, it develops a predictive model that may be used in order to predict the probability of a student dropping out and take preventive actions. Findings: This study compared dropout rates of one and a half year of enrollment among Traditional Undergraduate Students. Two major findings are the following: (1) Some of the resources designed to assist student are misallocated, and (2) Predictive models can be used to calculate the probability of a student dropping out. Recommendations for Practitioners: The study recommends that institutions must create initiatives to assist freshmen students and have annual assessment to measure the success of the initiatives. Recommendation for Researchers: Two, mathematical models may be used to predict dropout rates, the paper includes a model that predicted with 66.6% accuracy students who will dropout.

Aim/Purpose: The purpose of this study is to identify how Advanced level Mathematics and Mathematics course units offered at university level do impact on the academic performance of theoretical Computer Science course units. Background: In Sri Lankan state universities, students have been enrolled only from the Physical Science stream to do a degree program in Computer Science. In addition to that, universities have been offering some course units in Mathematics to provide the required mathematical maturity to Computer Science undergraduates. Despite of this it is observed that the failure rates in fundamental theoretical Computer Science course units are much higher than other course units offered in the general degree program every year. Methodology : Academic records comprised of all 459 undergraduates from three consecutive batches admitted to the degree program in Computer Science from a university were considered for this study. Contribution: This study helps academics in identifying suitable curricula for Mathematics course units to improve students’ performance in theoretical Computer Science courses. Findings: Advanced level Mathematics does not have any significant effect on the academic performance of theoretical Computer Science course units. Even though all Mathematics course units offered were significantly correlated with academic performance of every theoretical Computer Science course unit, only the Discrete Mathematics course unit highly impacted on the academic performance of all three theoretical Computer Science course units. Further this study indicates that the academic performance of female undergraduates is better than males in all theoretical Computer Science and Mathematics course units. Future Research: Identifying other critical success factors contributing to the students’ academic performance of the theoretical Computer Science through empirical studies

This paper explores the issues facing digital forensics in South Africa. It examines particular cyber threats and cyber threat levels for South Africa and the challenges in addressing the cybercrimes in the country through digital forensics. The paper paints a picture of the cybercrime threats facing South Africa and argues for the need to develop a skill base in digital forensics in order to counter the threats through detection of cybercrime, by analyzing cybercrime reports, consideration of current legislation, and an analysis of computer forensics course provision in South African universities. The paper argues that there is a need to develop digital forensics skills in South Africa through university programs, in addition to associated training courses. The intention in this paper is to promote debate and discussion in order to identify the cyber threats to South Africa and to encourage the development of a framework to counter the threats – through legislation, high tech law enforcement structures and protocols, digital forensics education, digital forensics skills development, and a public and business awareness of cybercrime threats.

Aim/Purpose: The aim of this study was to analyze various performance metrics and approaches to their classification. The main goal of the study was to develop a new typology that will help to advance knowledge of metrics and facilitate their use in machine learning regression algorithms Background: Performance metrics (error measures) are vital components of the evaluation frameworks in various fields. A performance metric can be defined as a logical and mathematical construct designed to measure how close are the actual results from what has been expected or predicted. A vast variety of performance metrics have been described in academic literature. The most commonly mentioned metrics in research studies are Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), etc. Knowledge about metrics properties needs to be systematized to simplify the design and use of the metrics. Methodology: A qualitative study was conducted to achieve the objectives of identifying related peer-reviewed research studies, literature reviews, critical thinking and inductive reasoning. Contribution: The main contribution of this paper is in ordering knowledge of performance metrics and enhancing understanding of their structure and properties by proposing a new typology, generic primary metrics mathematical formula and a visualization chart Findings: Based on the analysis of the structure of numerous performance metrics, we proposed a framework of metrics which includes four (4) categories: primary metrics, extended metrics, composite metrics, and hybrid sets of metrics. The paper identified three (3) key components (dimensions) that determine the structure and properties of primary metrics: method of determining point distance, method of normalization, method of aggregation of point distances over a data set. For each component, implementation options have been identified. The suggested new typology has been shown to cover a total of over 40 commonly used primary metrics Recommendations for Practitioners: Presented findings can be used to facilitate teaching performance metrics to university students and expedite metrics selection and implementation processes for practitioners Recommendation for Researchers: By using the proposed typology, researchers can streamline development of new metrics with predetermined properties Impact on Society: The outcomes of this study could be used for improving evaluation results in machine learning regression, forecasting and prognostics with direct or indirect positive impacts on innovation and productivity in a societal sense Future Research: Future research is needed to examine the properties of the extended metrics, composite metrics, and hybrid sets of metrics. Empirical study of the metrics is needed using R Studio or Azure Machine Learning Studio, to find associations between the properties of primary metrics and their “numerical” behavior in a wide spectrum of data characteristics and business or research requirements

Aim/Purpose: The understanding of disease spread dynamics in the context of air travel is crucial for effective disease detection and epidemic intelligence. The Susceptible-Exposed-Infectious-Recovered-Hospitalized-Critical-Deaths (SEIR-HCD) model proposed in this research work is identified as a valuable tool for capturing the complex dynamics of disease transmission, healthcare demands, and mortality rates during epidemics. Background: The spread of viral diseases is a major problem for public health services all over the world. Understanding how diseases spread is important in order to take the right steps to stop them. In epidemiology, the SIS, SIR, and SEIR models have been used to mimic and study how diseases spread in groups of people. Methodology: This research focuses on the integration of air traffic network data into the SEIR-HCD model to enhance the understanding of disease spread in air travel settings. By incorporating air traffic data, the model considers the role of travel patterns and connectivity in disease dissemination, enabling the identification of high-risk routes, airports, and regions. Contribution: This research contributes to the field of epidemiology by enhancing our understanding of disease spread dynamics through the application of the SIS, SIR, and SEIR-HCD models. The findings provide insights into the factors influencing disease transmission, allowing for the development of effective strategies for disease control and prevention. Findings: The interplay between local outbreaks and global disease dissemination through air travel is empirically explored. The model can be further used for the evaluation of the effectiveness of surveillance and early detection measures at airports and transportation hubs. The proposed research contributes to proactive and evidence-based strategies for disease prevention and control, offering insights into the impact of air travel on disease transmission and supporting public health interventions in air traffic networks. Recommendations for Practitioners: Government intervention can be studied during difficult times which plays as a moderating variable that can enhance or hinder the efficacy of epidemic intelligence efforts within air traffic networks. Expert collaboration from various fields, including epidemiology, aviation, data science, and public health with an interdisciplinary approach can provide a more comprehensive understanding of the disease spread dynamics in air traffic networks. Recommendation for Researchers: Researchers can collaborate with international health organizations and authorities to share their research findings and contribute to a global understanding of disease spread in air traffic networks. Impact on Society: This research has significant implications for society. By providing a deeper understanding of disease spread dynamics, it enables policymakers, public health officials, and practitioners to make informed decisions to mitigate disease outbreaks. The recommendations derived from this research can aid in the development of effective strategies to control and prevent the spread of infectious diseases, ultimately leading to improved public health outcomes and reduced societal disruptions. Future Research: Practitioners of the research can contribute more effectively to disease outbreaks within the context of air traffic networks, ultimately helping to protect public health and global travel. By considering air traffic patterns, the SEIR-HCD model contributes to more accurate modeling and prediction of disease outbreaks, aiding in the development of proactive and evidence-based strategies to manage and mitigate the impact of infectious diseases in the context of air travel.

Aim/Purpose: The aim of this research paper is to suggest a comprehensive model that incorporates the technology acceptance model with the task-technology fit model, information quality, security, trust, and managerial support to investigate the intended usage of big data analytics (BDA) in banks and insurance companies. Background: The emergence of the concept of “big data,” prompted by the widespread use of connected devices and social media, has been pointed out by many professionals and financial institutions in particular, which makes it necessary to assess the determinants that have an impact on behavioral intention to use big data analytics in banks and insurance companies. Methodology: The integrated model was empirically assessed using self-administered questionnaires from 181 prospective big data analytics users in Moroccan banks and insurance firms and examined using partial least square (PLS) structural equation modeling. The results cover sample characteristics, an analysis of the validity and reliability of measurement models’ variables, an evaluation of the proposed hypotheses, and a discussion of the findings. Contribution: The paper makes a noteworthy contribution to the BDA adoption literature within the finance sector. It stands out by ingeniously amalgamating the Technology Acceptance Model (TAM) with Task-Technology Fit (TTF) while underscoring the critical significance of information quality, trust, and managerial support, due to their profound relevance and importance in the finance domain. Thus showing BDA has potential applications beyond the finance sector. Findings: The findings showed that TTF and trust’s impact on the intention to use is considerable. Information quality positively impacted perceived usefulness and ease of use, which in turn affected the intention to use. Moreover, managerial support moderates the correlation between perceived usefulness and the intention to use, whereas security did not affect the intention to use and managerial support did not moderate the influence of perceived ease of use. Recommendations for Practitioners: The results suggest that financial institutions can improve their adoption decisions for big data analytics (BDA) by understanding how users perceive it. Users are predisposed to use BDA if they presume it fits well with their tasks and is easy to use. The research also emphasizes the importance of relevant information quality, managerial support, and collaboration across departments to fully leverage the potential of BDA. Recommendation for Researchers: Further study may be done on other business sectors to confirm its generalizability and the same research design can be employed to assess BDA adoption in organizations that are in the advanced stage of big data utilization. Impact on Society: The study’s findings can enable stakeholders of financial institutions that are at the primary stage of big data exploitation to understand how users perceive BDA technologies and the way their perception can influence their intention toward their use. Future Research: Future research is expected to conduct a comparison of the moderating effect of managerial support on users with technical expertise versus those without; in addition, international studies across developed countries are required to build a solid understanding of users’ perceptions towards BDA.

Aim/Purpose: This study utilized a comprehensive framework to investigate the adoption of Business Analytics (BA) and its effects on performance in commercial banks in Jordan. The framework integrated the Technological-Organizational-Environmental (TOE) model, the Diffusion of Innovation (DOI) theory, and the Resource-Based View (RBV). Background: The recent trend of utilizing data for business operations and decision-making has positively impacted organizations. Business analytics (BA) is a leading technique that generates valuable insights from data. It has gained considerable attention from scholars and practitioners across various industries. However, guidance is lacking for organizations to implement BA effectively specific to their business contexts. This research aims to evaluate factors influencing BA adoption by Jordanian commercial banks and examine how its implementation impacts bank performance. The goal is to provide needed empirical evidence surrounding BA adoption and outcomes in the Jordanian banking sector. Methodology: The study gathered empirical data by conducting an online questionnaire survey with senior and middle managers from 13 commercial banks in Jordan. The participants were purposefully selected, and the questionnaire was designed based on relevant and well-established literature. A total of 307 valid questionnaires were collected and considered for data analysis. Contribution: This study makes a dual contribution to the BA domain. Firstly, it introduces a research model that comprehensively examines the factors that influence the adoption of BA. The proposed model integrates the TOE framework, DOI theory, and RBV theory. Combining these frameworks allows for a comprehensive examination of BA adoption in the banking industry. By analyzing the technological, organizational, and environmental factors through the TOE framework, understanding the diffusion process through the DOI theory, and assessing the role of resources and capabilities through the RBV theory, researchers and practitioners can better understand the complex dynamics involved. This integrated approach enables a more nuanced assessment of the factors that shape BA adoption and its subsequent impact on business performance within the banking industry. Secondly, it uncovers the effects of BA adoption on business performance. These noteworthy findings stem from a rigorous analysis of primary data collected from commercial banks in Jordan. By presenting a holistic model and delving into the implications for business performance, this research offers valuable insights to researchers and practitioners alike in the field of BA. Findings: The findings revealed that various technological (data quality, complexity, compatibility, relative advantage), organizational (top management support, organizational readiness), and environmental (external support) factors are crucial in shaping the decision to adopt BA. Furthermore, the study findings demonstrated a positive relationship between BA adoption and performance outcomes in Jordanian commercial banks. Recommendations for Practitioners: The findings suggest that Jordanian commercial banks should enforce data quality practices, provide clear standards, invest in data quality tools and technologies, and conduct regular data audits. Top management support is crucial for fostering a data-driven decision-making culture. Organizational readiness involves having the necessary resources and skilled personnel, as well as promoting continuous learning and improvement. Highlighting the benefits of BA helps overcome resistance to technological innovation and encourages adoption by demonstrating improved decision-making processes and operational efficiency. Furthermore, external support is crucial for banks to adopt Business Analytics (BA). Banks should partner with experienced vendors to gain expertise and incorporate best practices. Vendors also provide training and technical support to overcome technological barriers. Compatibility is essential for optimal performance, requiring managers to modify workflows and IT infrastructure. Complexity, including data, organizational, and technical complexities, is a major obstacle to BA adoption. Banks should take a holistic approach, focusing on people, processes, and technology, and prioritize data quality and governance. Building a skilled team, fostering a data-driven culture, and investing in technology and infrastructure are essential. Recommendation for Researchers: The integration of the TOE framework, the DOI theory, and the RBV theory can prove to be a powerful approach for comprehensively analyzing the various factors that influence BA adoption within the dynamic banking industry. Furthermore, this combined framework enables us to gain deeper insights into the subsequent impact of BA adoption on overall business performance. Impact on Society: Examining the factors influencing BA adoption in the banking industry and its subsequent impact on business performance can have wide-ranging societal implications. It can promote data-driven decision-making, enhance customer experiences, strengthen fraud detection, foster financial inclusion, contribute to economic growth, and trigger discussions on ethical considerations. Future Research: To further advance future research, there are several avenues to consider. One option is to broaden the scope by including a larger sample size, allowing for a more comprehensive analysis. Another possibility is to investigate the impact of BA adoption on various performance indicators beyond the ones already examined. Additionally, incorporating qualitative research methods would provide a more holistic understanding of the organizational dynamics and challenges associated with the adoption of BA in Jordanian commercial banks.

Aim/Purpose: Most people use their phones for work and communication. Businesses today require sustainable mobile phones to limit the environmental impact of mobile phones. According to the Environmental Protection Agency (EPA), a green product uses less energy. Green smartphones need low radiation emission, are made from recyclable materials, and are designed to last longer than typical smartphones. Further, the manufacturing process needs to have a low environmental impact. The present study aims to identify the influence of variables (such as Green Awareness, Environmental Concern, Altruism, and Willingness to Pay) on green smartphone purchase intention among academics in the Sri Lankan higher education sector. Background: With the swift technological advances, almost everyone has begun to use smartphones. Simultaneously, smartphone manufacturers have begun to release cutting-edge smartphone models to the general public. As a result, it has generated a significant amount of e-waste for the environment. As a result, therefore, the sustainability of green smartphones has become a major societal concern in the developed world, but this is not yet true in the developing world Methodology: The study used a qualitative research method in which the authors attempted to acquire primary data by conducting in-depth interviews with academics from the Sri Lankan higher education sector using a semi-structured interview guide. Eight interviews were conducted, audio recorded, and word-to-word transcribed for content analysis. Researchers used content analysis to determine the presence, meanings, and linkages of specific words, themes, or concepts. Contribution: The findings provide important environmental insights for smartphone makers and society, such as introducing waste reduction programs and energy-saving practices and creating awareness among people to change their consumption patterns. The study will provide valuable insights into the green smartphone phone purchasing intentions of academics in a developing country, especially helping green smartphone producers and marketers construct effective tactics with the insight of the current study based on university faculty members’ viewpoints. Findings: The current study’s findings revealed that academics acknowledge the need for environmental protection with an awareness of the green concept and environmental concerns. According to the interviews, most participants intended to move from their present smartphone to an ecologically friendly phone, as they explained on altruism. This implies that even academics in underdeveloped countries are worried about environmental issues and have shown a more robust understanding of these issues and how environmentally aware individuals’ activities may assist the earth’s sustainability. Further, academics have a willingness to pay for a green smartphone. Recommendations for Practitioners: Academics prioritize environmental conservation when making purchases. This implies that manufacturers and enterprises should focus on developing and in- novating more environmentally friendly products. Recommendation for Researchers: Using only academics as a sample approach is severely limited if the study’s population comprises people with various qualities. Nevertheless, this study presented only four independent variables, and more factors impacting green smartphone purchasing intention may exist. As a result, it is proposed that future research consider other factors. Impact on Society: It was discovered that most participants displayed altruism in their product purchases, implying that policymakers must strengthen the moral practice of concern for the welfare and happiness of other humans, even in developing countries. Future Research: A further in-depth study focusing on many perspectives such as limits and motivations for purchasing green products in various socioeconomic groups with varying moderating factors such as gender, education, rural-urban, and so on would be advantageous. Individual (emotions, habits, perceived behavioral control, trust, values, personal norm, knowledge) and situational (availability, product attributes, subjective norm, brand, eco-labeling) variables should be included in future research.

In the context of asthma severity prediction, this study delves into the feature importance of various symptoms and demographic attributes. Leveraging a comprehensive dataset encompassing symptom occurrences across varying severity levels, this investigation employs visualisation techniques, such as stacked bar plots, to illustrate the distribution of symptomatology within different severity categories. Additionally, correlation coefficient analysis is applied to quantify the relationships between individual attributes and severity levels. Moreover, the study harnesses the power of random forest and the Gini importance methodology, essential tools in feature importance analytics, to discern the most influential predictors in asthma severity prediction. The experimental results bring to light compelling associations between certain symptoms, notably 'runny-nose' and 'nasal-congestion', and specific severity levels, elucidating their potential significance as pivotal predictive indicators. Conversely, demographic factors, encompassing age groups and gender, exhibit comparatively weaker correlations with symptomatology. These findings underscore the pivotal role of individual symptoms in characterising asthma severity, reinforcing the potential for feature importance analysis to enhance predictive models in the realm of asthma management and bioinformatics.

Recently, interest in the correlation between 'contract' in transaction governance and 'trust' in relational governance mechanisms has been growing. This study focuses on issues related to the evolution of contract and inter-organisational trust dynamics in transaction governance and uses mixed research method to investigate sectors related to transaction governance in Taiwan's electronics industry. The study finds higher flexibility in contract implementation to be a promoter of trust between two parties in a relationship, thereby promoting project execution efficiency in the case of Taiwanese firms. Organisational management differs between the East and West; therefore, Western firms should understand how various contractual provisions can be used to accommodate different transactions when cooperating with Taiwanese electronics companies.

This paper aims to share the author's vision on possible research directions for big knowledge-based AI. A renewed definition of big knowledge (BK) and big knowledge systems (BKS) is first introduced. Then the first BKS model, called cloud knowledge social intelligence (CKEI) is provided with a hierarchy of knowledge as a service (KAAS). At last, a new semantics, the big-and-broad step axiomatic structural operational semantics (BBASOS) for applications on BKS is introduced and discussed with a practical distributed BKS model knowledge graph network KGN and a mini example.

The emergence of service ecosystems can accelerate the industrial renewal required because of urgent global challenges. However, existing research has not sufficiently grasped the social dynamics of coevolution in ecosystems that enhance industrial renewal. This study aimed to advance ecosystem research through a practice lens and to present the key characteristics of industrial service ecosystem practice involved in industrial renewal. Consequently, its three characteristics - <i>accomplishment</i>, <i>attractiveness</i> and <i>actionability</i> - were configured based on an abductive study derived from the ecosystem literature, three practice-oriented approaches to learning, and two case ecosystem examinations. These features created the logic for resource integration and enhanced ecosystems to evolve as units, thus exceeding the actors' independent avenues of renewal. The findings of this study provided a deeper understanding of the coevolution in ecosystems needed to accelerate industrial renewal as well as a novel conceptualisation of an <i>ecosystem-as-practice</i> for further studies.