Aim/Purpose: Multi-threaded software design is considered to be difficult, especially to novice programmers. In this study, we explored how students cope with a task that its solution requires a multi-threaded architecture to achieve optimal runtime. Background: An efficient exploit of multicore processors architecture requires computer programs that use parallel programming techniques. However, parallel programming is difficult to understand and apply by novice programmers. Methodology: The students had to address a two-stage problem: (1) design an optimal runtime solution to a given problem with no additional instructions; and (2) provide an optimal runtime multi-threaded design to the same problem. Interviews were conducted with a representative group of students to understand the underlying causes of their provided designs. We used qualitative research methods to gain refined insights regarding the students’ decision making during the design process. To analyze the gained data, we used content analysis tools. Contribution: The case study presented in this paper will help the teacher to stress the merits and limitations of various parallel architectures and confront students with the consequences of their solutions via performances’ benchmark. Findings: Analysis of the student’s solutions to the first stage revealed that the majority of them did not provide a multi-threaded solution ignoring the optimal runtime requirement. At the second stage, seven various architectures were provided differing in the number of involved threads, the data structures used, and the synchronization mechanism employed. The majority of the solutions were sub-optimal and only a few students provided an optimal one. Recommendations for Practitioners: We recommend conducting class discussions that will follow a task similar to the one used in this study. Recommendation for Researchers: To be able to generalize the received results this research should be repeated with larger study participant groups from various academic institutions. Impact on Society: Understanding the difficulties of novice programmers may lead to quality software systems. Future Research: To be able to generalize the received results this research should be repeated with larger study participant groups from various academic institutions.

Aim/Purpose: This study examines the use of the Emergency Remote Teaching (ERT) platform by undergraduates of the University of Ibadan, Nigeria, during the COVID-19 pandemic using the constructs of the UTAUT2 model. Five constructs of the UTAUT2 model were adopted to investigate the use of the ERT platform by undergraduates of the university. Background: The Coronavirus (COVID-19) outbreak disrupted academic activities in educational institutions, leading to an unprecedented school closure globally. In response to the pandemic, higher educational institutions adopted different initiatives aimed at ensuring the uninterrupted flow of their teaching and learning activities. However, there is little research on the use of ERT platforms by undergraduates in Nigerian universities. Methodology: The descriptive survey research design was adopted for the study. The multi-stage random sampling technique was used to select 334 undergraduates at the University of Ibadan, Nigeria, while a questionnaire was used to collect data from 271 students. Quantitative data were collected and analyzed using frequency counts, percentages, mean and standard deviation, Pearson Product Moment Correlation, and regression analysis. Contribution: The study contributes to understanding ERT use in the educational institutions of Nigeria – Africa’s most populous country. Furthermore, the study adds to the existing body of knowledge on how the UTAUT2 Model could explain the use of information technologies in different settings. Findings: Findings revealed that there was a positive significant relationship between habit, hedonic motivation, price value, and social influence on the use of ERT platforms by undergraduates. Hedonic motivation strongly predicted the use of ERT platforms by most undergraduates. Recommendations for Practitioners: As a provisional intervention in times of emergencies, the user interface, navigation, customization, and other aesthetic features of ERT platforms should be more appealing and enjoyable to ensure their optimum utilization by students. Recommendation for Researchers: More qualitative research is required on users’ satisfaction, concerns, and support systems for ERT platforms in educational institutions. Future studies could consider the use of ERT by students in different countries and contexts such as students participating in English as a Foreign Language (EFL) and the English for Speakers of other languages (ESOL) programs. Impact on Society: As society faces increased uncertainties of the next global pandemic, this article reiterates the crucial roles of information technology in enriching teaching and learning activities in educational institutions. Future Research: Future research should focus on how different technology theories and models could explain the use of ERT platforms at different educational institutions in other geographical settings and contexts.

Aim/Purpose: The teaching of appropriate problem-solving techniques to novice learners in undergraduate software development education is often poorly defined when compared to the delivery of programming techniques. Given the global need for qualified designers of information technology, the purpose of this research is to produce a foundational template for an educational software development methodology grounded in the established literature. This template can be used by third-level educators and researchers to develop robust educational methodologies to cultivate structured problem solving and software development habits in their students while systematically teaching the intricacies of software creation. Background: While software development methodologies are a standard approach to structured and traceable problem solving in commercial software development, educational methodologies for inexperienced learners remain a neglected area of research due to their assumption of prior programming knowledge. This research aims to address this deficit by conducting an integrative review to produce a template for such a methodology. Methodology: An integrative review was conducted on the key components of Teaching Software Development Education, Problem Solving, Threshold Concepts, and Computational Thinking. Systematic reviews were conducted on Computational Thinking and Software Development Education by employing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) process. Narrative reviews were conducted on Problem Solving and Threshold Concepts. Contribution: This research provides a comprehensive analysis of problem solving, software development education, computational thinking, and threshold concepts in computing in the context of undergraduate software development education. It also synthesizes review findings from these four areas and combines them to form a research-based foundational template methodology for use by educators and researchers interested in software development undergraduate education. Findings: This review identifies seven skills and four concepts required by novice learners. The skills include the ability to perform abstraction, data representation, decomposition, evaluation, mental modeling, pattern recognition, and writing algorithms. The concepts include state and sequential flow, non-sequential flow control, modularity, and object interaction. The teaching of these skills and concepts is combined into a spiral learning framework and is joined by four development stages to guide software problem solving: understanding the problem, breaking into tasks, designing, coding, testing, and integrating, and final evaluation and reflection. This produces the principal finding, which is a research-based foundational template for educational software development methodologies. Recommendations for Practitioners: Focusing introductory undergraduate computing courses on a programming syllabus without giving adequate support to problem solving may hinder students in their attainment of development skills. Therefore, providing a structured methodology is necessary as it equips students with essential problem-solving skills and ensures they develop good development practices from the start, which is crucial to ensuring undergraduate success in their studies and beyond. Recommendation for Researchers: The creation of educational software development methodologies with tool support is an under-researched area in undergraduate education. The template produced by this research can serve as a foundational conceptual model for researchers to create concrete tools to better support computing undergraduates. Impact on Society: Improving the educational value and experience of software development undergraduates is crucial for society once they graduate. They drive innovation and economic growth by creating new technologies, improving efficiency in various industries, and solving complex problems. Future Research: Future research should concentrate on using the template produced by this research to create a concrete educational methodology adapted to suit a specific programming paradigm, with an associated learning tool that can be used with first-year computing undergraduates.

This study inquires about the COVID-19-generated momentum and how it resulted in transformative opportunities for the hard-hit tourism industry in Hawai'i. It also investigates the type of sustainability-based management strategies that were favoured by actors from the industry to help navigate uncertain times and capture transformative opportunities. Findings indicate that actors from the tourism industry in Hawai'i perceived the COVID-19 pandemic as a huliau, or a point of transformation, to reflect and re-evaluate the tourism industry's responsibility and shift toward a recovery focused on sustainability. This research confirms that the pandemic-driven momentum accelerated opportunities for changing and transforming traditional business models and indicators of progress within the tourism industry in Hawai'i. Further research may explore additional Pacific Island countries to gain a deeper understanding of the problem within the region's context.

Dynamic nature of the FMCG sector perpetually provides a tricky challenge for organisational leaders to nurture their employees. High demand for products, less shelf life and tough competitors always challenge the leaders to uphold their products in the market. Due to technology and e-commerce, many new competitors have joined the market, vying with the industry's veterans. Due to their unique business models that match client needs, these firms are expected to boost FMCG industry income in the future. Managers' leadership styles depend primarily on emotional intelligence. This quantitative study examines how emotional intelligence influences West Bengal FMCG senior managers' leadership styles. 500 FMCG managers were selected. PLS-SEM is used to study. Emotionally competent leaders choose transactional and transformational leadership styles depending on the occasion. Managers' transactional leadership style is strongly influenced by their sympathetic awareness, as shown by a path coefficient of 0.755. Transformational leadership style has a path coefficient of 0.693, indicating that managers' empathy affects their organisational management. Thus, sympathetic awareness and emotion regulation predict good management leadership.

The article describes the integrated adoption of Fedora Commons and MediaMosa for managing a digital repository. The integration was experimented along with the development of a cooperative project, Sapienza Digital Library (SDL). The functionalities of the two applications were exploited to built a weaving factory, useful for archiving, preserving and disseminating of multi-format and multi-protocol audio video contents, in different fruition contexts. The integration was unleashed by means of both repository-to-repository interaction, and mapping of video Content Model's disseminators to MediaMosa's Restful services. The outcomes of this integration will lead to a more flexible management of the dissemination services, as well as to economize the overproduction of different dissemination formats.

In 2008 the University of Hull, Stanford University and University of Virginia decided to collaborate with Fedora Commons (now DuraSpace) on the Hydra project. This project has sought to define and develop repository-enabled solutions for the management of multiple digital content management needs that are multi-purpose and multi-functional in such a way as to allow their use across multiple institutions. This article describes the evolution of Hydra as a project, but most importantly as a community that can sustain the outcomes from Hydra and develop them further. The data modelling and technical implementation are touched on in this context, and examples of the Hydra heads in development or production are highlighted. Finally, the benefits of working together, and having worked together, are explored as a key element in establishing a sustainable open source solution.

The UK JISC-funded Content Lifecycle Integration Framework (CLIF) project has explored the management of digital content throughout its lifecycle from creation through to preservation or disposal. Whilst many individual systems offer the capability of carrying out lifecycle stages to varying degrees, CLIF recognised that only by facilitating the movement of content between systems could the full lifecycle take advantage of systems specifically geared towards different stages of the digital lifecycle. The project has also placed the digital repository at the heart of this movement and has explored this through carrying out integrations between Fedora and Sakai, and Fedora and SharePoint. This article will describe these integrations in the context of lifecycle management and highlight the issues discovered in enabling the smooth movement of content as required.

This paper will shed light on the lack of the development of appropriate monitoring systems in the field of education. Test banks can be easily purchased. Smart phones can take and share pictures of exams. A video of an exam given through Blackboard can easily be made. A survey to determine the extent of cheating using technology was given to several university students. Evidence is provided that shows security is lacking as evidenced by the number of students who have made use of technological advances to cheat on exams. The findings and conclusion may serve as evidence for administrators and policy makers to re-assess efforts being made to increase security in online testing.

Aim/Purpose: In the midst of COVID-19, classes are transitioned online. Instructors and students scramble for ways to adapt to this change. This paper shares an experience of one instructor in how he has gone through the adaptation. Background: This section provides a contextual background of online teaching. The instructor made use of M-learning to support his online teaching and adopted the UTAUT model to guide his interpretation of the phenomenon. Methodology: The methodology used in this study is action research through participant-observation. The instructor was able to look at his own practice in teaching and reflect on it through the lens of the UTAUT conceptual frame-work. Contribution: The results helped the instructor improve his practice and better under-stand his educational situations. From the narrative, others can adapt and use various apps and platforms as well as follow the processes to teach online. Findings: This study shares an experience of how one instructor had figured out ways to use M-learning tools to make the online teaching and learning more feasible and engaging. It points out ways that the instructor could connect meaningfully with his students through the various apps and plat-forms. Recommendations for Practitioners: The social aspects of learning are indispensable whether it takes place in person or online. Students need opportunities to connect socially; there-fore, instructors should try to optimize technology use to create such opportunities for conducive learning. Recommendations for Researchers: Quantitative studies using surveys or quasi-experiment methods should be the next step. Validated inventories with measures can be adopted and used in these studies. Statistical analysis can be applied to derive more objective findings. Impact on Society: Online teaching emerges as a solution for the delivery of education in the midst of COVID-19, but more studies are needed to overcome obstacles and barriers to both instructors and students. Future Research: Future studies should look at the obstacles that instructors encounter and the barriers with technology access and inequalities that students face in online classes.

Aim/Purpose: This study examined learning during the COVID-19 crisis, as perceived by preservice teachers at the time of their academic studies and their student teaching experience. Background: The COVID-19 crisis is unexpected. On one hand, it disrupted learning in all learning frameworks, on the other, it may create a change in learning characteristics even after the end of the crisis. This study examined the pro-ductive, challenging, and thwarting factors that preservice teachers encountered during their studies and in the course of their student teaching during the COVID-19 period, from the perspective of preservice teachers. Methodology: The study involved 287 students studying at teacher training institutions in Israel. The preservice teachers were studying online, and in addition experienced online teaching of students in schools, guided by their own teacher. The study used a mixed method. The questionnaire included closed and open questions. The data were collected in 2020. Contribution: Identifying the affecting factors may deepen the understanding of online learning/teaching and assist in the optimal implementation of online learning. Findings: Online learning experience. We found that some of the lessons at institutions of higher learning were delivered in the format of online lectures. Many pre-service teachers had difficulty sitting in front of a computer for many hours—“Zoom fatigue.” Preservice teachers who had difficulty self-regulating and self-mobilizing for study, experienced accumulating loads, which caused them feelings of stress and anxiety. The word count indicated that the words that appeared most often were “load” and “stress.” Some preservice teachers wrote that collaborating in forums with others made it easier for them. Some suggested diversifying by digital means, incorporating asynchronous units and illustrative films, and easing up on online lectures, as a substitute for face-to-face lectures. Online teaching experience in schools. The preservice teachers' descriptions show that in lessons taught in the format of lectures and communication of content, there were discipline problems and non-learning. According to the preservice teachers, discipline problems stemmed from difficulties concentrating, physical distance, load, and failure to address the students' difficulties. Recommendations for Practitioners: In choosing schools for student teaching, it is recommended to reach an understanding with the school about the online learning policy and organization. It is important to hold synchronous sessions in small groups of 5 to 10 students. The sessions should focus on the mental wellbeing of the students, and on the acquisition of knowledge and skills. Students should be prepared for participation in asynchronous digital lessons, which should be produced by professionals. It should be remembered that the change of medium from face-to-face to online learning also changes the familiar learning environment for all parties and requires modifying the ways of teaching. Recommendations for Researchers: A change in the learning medium also requires a change in the definition of objectives and goals expected of each party—students, teachers, and parents. All parties must learn to view online learning as a method that enables empowerment and the application of 21st century skills. Impact on Society: Teachers' ability to deploy 21st century skills in an online environment de-pends largely on their experience, knowledge, skills, and attitude toward these skills. Future Research: This study examined the issue from the perspective of preservice teachers. It is recommended to examine it also from the perspective of teachers and students.

Aim/Purpose. The study aimed to examine the remote and face-to-face experience of pedagogical training in kindergarten after the third COVID-19 closure in Israel. Background. The outbreak of the COVID-19 epidemic in 2020 changed the training system, and preservice teachers were required to have their practical experience in the kindergartens both remotely and face-to-face. They had to adapt to the new requirements of teacher training programs and receive professional coaching and support from the pedagogical instructor remotely. Methodology. The sample comprised 26 early childhood preservice teachers, who received academic training that includes proficiency in digital technology. The data were collected through feedback that they wrote themselves during the training period and analyzed in the interpretive approach. Contribution. The contribution of the present study is that it examines the pedagogical coaching from the perspective of preservice teachers in a kindergarten during the COVID-19 epidemic, which forced a transition from face-to-face to remote pedagogical training, then back to face-to-face pedagogical instruction. To the best of my knowledge, no such study has been carried out to date, which makes it unique. Findings. The main findings indicate the dissatisfaction of most preservice kindergarten teachers with the remote pedagogical training (about 85%) at the physical, emotional, technological, and pedagogical levels, and the satisfaction of most preservice kindergarten teachers with face-to-face pedagogical training (about 92%) at the physical, emotional, and pedagogical levels. The main conclusion is that technology is a potential barrier in training, and that preservice kindergarten teachers need a pedagogical instructor present at a professional face-to-face meeting. Recommendations for Practitioners. The findings of the study show how important in-person learning and engagement is for everyone especially for Preservice teachers’ and may be helpful for pedagogical coaching teams. Recommendations for Researchers. Preservice teachers’ awareness of the pedagogical coaching experiences could persuade the coaching teams to avoid potential difficulties, increase emotional support, and refine the use of technology to make it a closer substitute for frontal communication. Impact on Society. Face-to-face training based on interpersonal relationship, allows to develop better during the training period.

Health Data collection is one of the major components of public health systems. Decision makers, policy makers, and medical service providers need accurate and timely data in order to improve the quality of health services. The rapid growth and use of mobile technologies has exerted pressure on the demand for mobile-based data collection solutions to bridge the information gaps in the health sector. We propose a prototype using open source data collection frameworks to test its feasibility in improving the vaccination data collection in Kenya. KenVACS, the proposed prototype, offers ways of collecting vaccination data through mobile phones and visualizes the collected data in a web application; the system also sends reminder short messages service (SMS) to remind parents on the date of the next vaccination. Early evaluation demonstrates the benefits of such a system in supporting and improving vaccination of children. Finally, we conducted a qualitative study to assess challenges in remote health data collection and evaluated usability and functionality of KenVACS.

Aim/Purpose: The topic of this study is the ICT-enabled transformation of car dealerships to regional providers of sustainable mobility (e.g., car sharing). Background: Car dealerships offer specific conditions that enable a sustainable mobility offer, based on individual motorized transport like car sharing. This is especially useful in small towns or rural areas where people’s mobility is strongly dominated by private cars, and public transport coverage is limited. However, these new mobility services are combined services with the need of a deep integration of information systems, and these services are not yet related to car dealerships and customer acquisition has to be supported. Methodology: An empirical study with an inductive approach was chosen. The study consists of interviews with three focus groups of different stakeholders of car dealerships. Within the frame of the research project, “ReCaB – Regional Car-Balancing” a qualitative research approach was chosen. Within a design science approach the existing SusCRM architecture was adapted based on the elaborated requirements. Contribution: A software architecture is proposed, where Customer Relationship Management (CRM) components to market new sustainable mobility offers are vital parts and existing information systems of car dealerships are integrated. Findings: The basic feasibility of the establishment and customer acceptance has been demonstrated, at least in the area of car sharing within ReCaB. The execution in the car sharing field has already started and a number of car dealerships are already bringing their own offers to market. Major findings for the SusCRM architecture have been elaborated in a design science approach in the national German research project “Showcase for electro mobility”. Recommendations for Practitioners: There is still no fully functional prototype developed for this specific use case and evaluated in the car dealership environment. An implementation only, based on own efforts, is difficult for car dealerships because of the dealership’s lack of know-how as well as tight IT budgets. However, this approach appears particularly successful in rural areas where public transport services are heavily declining. For full implementation, the presented ICT support is imperative. Recommendation for Researchers: The research on the retailer level, especially in combination with a digital trans-formation by the use of ICT systems, is still fragmentary. Research in this area that both addresses sustainability goals on a general level and supports economical goals on the company level of automotive retailers would be useful. Impact on Society: Creation of sustainable offers as a substitute or supplement for mobility based on their own car is a promising way to reduce negative effects of mobility. Enhancement of the mobility of the people in rural areas is leading to a more active lifestyle by reaching the leisure facilities, workplaces and educational institutions in a sustainable manner. Future Research: In terms of the fast changing mobility landscape, especially on a technical level with the development of autonomous vehicles and digitalization of entire businesses new solutions are becoming accessible, that have to be integrated in further research.

Aim/Purpose: In this article, we provide a better solution to Webpage access prediction. In particularly, our core proposed approach is to increase accuracy and efficiency by reducing the sequence space with integration of PageRank into CPT+. Background: The problem of predicting the next page on a web site has become significant because of the non-stop growth of Internet in terms of the volume of contents and the mass of users. The webpage prediction is complex because we should consider multiple kinds of information such as the webpage name, the contents of the webpage, the user profile, the time between webpage visits, differences among users, and the time spent on a page or on each part of the page. Therefore, webpage access prediction draws substantial effort of the web mining research community in order to obtain valuable information and improve user experience as well. Methodology: CPT+ is a complex prediction algorithm that dramatically offers more accurate predictions than other state-of-the-art models. The integration of the importance of every particular page on a website (i.e., the PageRank) regarding to its associations with other pages into CPT+ model can improve the performance of the existing model. Contribution: In this paper, we propose an approach to reduce prediction space while improving accuracy through combining CPT+ and PageRank algorithms. Experimental results on several real datasets indicate the space reduced by up to between 15% and 30%. As a result, the run-time is quicker. Furthermore, the prediction accuracy is improved. It is convenient that researchers go on using CPT+ to predict Webpage access. Findings: Our experimental results indicate that PageRank algorithm is a good solution to improve CPT+ prediction. An amount of though approximately 15 % to 30% of redundant data is removed from datasets while improving the accuracy. Recommendations for Practitioners: The result of the article could be used in developing relevant applications such as Webpage and product recommendation systems. Recommendation for Researchers: The paper provides a prediction model that integrates CPT+ and PageRank algorithms to tackle the problem of complexity and accuracy. The model has been experimented against several real datasets in order to show its performance. Impact on Society: Given an improving model to predict Webpage access using in several fields such as e-learning, product recommendation, link prediction, and user behavior prediction, the society can enjoy a better experience and more efficient environment while surfing the Web. Future Research: We intend to further improve the accuracy of webpage access prediction by using the combination of CPT+ and other algorithms.

Aim/Purpose: The paper’s objective is to examine the challenges of using the mobile phone to mine location data for effective contact tracing of symptomatic, pre-symptomatic, and asymptomatic individuals and the implications of this technology for public health governance. Background: The COVID-19 crisis has created an unprecedented need for contact tracing across South Africa, requiring thousands of people to be traced and their details captured in government health databases as part of public health efforts aimed at breaking the chains of transmission. Contact tracing for COVID-19 requires the identification of persons who may have been exposed to the virus and following them up daily for 14 days from the last point of exposure. Mining mobile phone location data can play a critical role in locating people from the time they were identified as contacts to the time they access medical assistance. In this case, it aids data flow to various databases designated for COVID-19 work. Methodology: The researchers conducted a review of the available literature on this subject drawing from academic articles published in peer-reviewed journals, research reports, and other relevant national and international government documents reporting on public health and COVID-19. Document analysis was used as the primary research method, drawing on the case studies. Contribution: Contact tracing remains a critical strategy in curbing the deadly COVID-19 pandemic in South Africa and elsewhere in the world. However, given increasing concern regarding its invasive nature and possible infringement of individual liberties, it is imperative to interrogate the challenges related to its implementation to ensure a balance with public governance. The research findings can thus be used to inform policies and practices associated with contact tracing in South Africa. Findings: The study found that contact tracing using mobile phone location data mining can be used to enforce quarantine measures such as lockdowns aimed at mitigating a public health emergency such as COVID-19. However, the use of technology can expose the public to criminal activities by exposing their locations. From a public governance point of view, any exposure of the public to social ills is highly undesirable. Recommendations for Practitioners: In using contact tracing apps to provide pertinent data location caution needs to be exercised to ensure that sensitive private information is not made public to the extent that it compromises citizens’ safety and security. The study recommends the development and implementation of data use protocols to support the use of this technology, in order to mitigate against infringement of individual privacy and other civil liberties. Recommendation for Researchers: Researchers should explore ways of improving digital applications in order to improve the acceptability of the use of contact tracing technology to manage pandemics such as COVID-19, paying attention to ethical considerations. Impact on Society: Since contact tracing has implications for privacy and confidentiality it must be conducted with caution. This research highlights the challenges that the authorities must address to ensure that the right to privacy and confidentiality is upheld. Future Research: Future research could focus on collecting primary data to provide insight on contact tracing through mining mobile phone location data. Research could also be conducted on how app-based technology can enhance the effectiveness of contact tracing in order to optimize testing and tracing coverage. This has the potential to minimize transmission whilst also minimizing tracing delays. Moreover, it is important to develop contact tracing apps that are universally inter-operable and privacy-preserving.

Aim/Purpose: This paper aims to analyze the availability and pricing of perishable farm produce before and during the lockdown restrictions imposed due to Covid-19. This paper also proposes machine learning and deep learning models to help the farmers decide on an appropriate market to sell their farm produce and get a fair price for their product. Background: Developing countries like India have regulated agricultural markets governed by country-specific protective laws like the Essential Commodities Act and the Agricultural Produce Market Committee (APMC) Act. These regulations restrict the sale of agricultural produce to a predefined set of local markets. Covid-19 pandemic led to a lockdown during the first half of 2020 which resulted in supply disruption and demand-supply mismatch of agricultural commodities at these local markets. These demand-supply dynamics led to disruptions in the pricing of the farm produce leading to a lower price realization for farmers. Hence it is essential to analyze the impact of this disruption on the pricing of farm produce at a granular level. Moreover, the farmers need a tool that guides them with the most suitable market/city/town to sell their farm produce to get a fair price. Methodology: One hundred and fifty thousand samples from the agricultural dataset, released by the Government of India, were used to perform statistical analysis and identify the supply disruptions as well as price disruptions of perishable agricultural produce. In addition, more than seventeen thousand samples were used to implement and train machine learning and deep learning models that can predict and guide the farmers about the appropriate market to sell their farm produce. In essence, the paper uses descriptive analytics to analyze the impact of COVID-19 on agricultural produce pricing. The paper explores the usage of prescriptive analytics to recommend an appropriate market to sell agricultural produce. Contribution: Five machine learning models based on Logistic Regression, K-Nearest Neighbors, Support Vector Machine, Random Forest, and Gradient Boosting, and three deep learning models based on Artificial Neural Networks were implemented. The performance of these models was compared using metrics like Precision, Recall, Accuracy, and F1-Score. Findings: Among the five classification models, the Gradient Boosting classifier was the optimal classifier that achieved precision, recall, accuracy, and F1 score of 99%. Out of the three deep learning models, the Adam optimizer-based deep neural network achieved precision, recall, accuracy, and F1 score of 99%. Recommendations for Practitioners: Gradient boosting technique and Adam-based deep learning model should be the preferred choice for analyzing agricultural pricing-related problems. Recommendation for Researchers: Ensemble learning techniques like Random Forest and Gradient boosting perform better than non-Ensemble classification techniques. Hyperparameter tuning is an essential step in developing these models and it improves the performance of the model. Impact on Society: Statistical analysis of the data revealed the true nature of demand and supply and price disruption. This analysis helps to assess the revenue impact borne by the farmers due to Covid-19. The machine learning and deep learning models help the farmers to get a better price for their crops. Though the da-taset used in this paper is related to India, the outcome of this research work applies to many developing countries that have similar regulated markets. Hence farmers from developing countries across the world can benefit from the outcome of this research work. Future Research: The machine learning and deep learning models were implemented and tested for markets in and around Bangalore. The model can be expanded to cover other markets within India.

Aim/Purpose. COVID-19 was an unprecedented disruptive event that accelerated the shift to remote work and encouraged widespread adoption of digital tools in organizations. This empirical study was conducted from an organizational-strategic perspective, with the aim of examining how the COVID-19 pandemic outbreak affected employees’ use of organizational information systems (IS) as reflected in frequency. Background. To date, only a limited effort has been made, and a rather narrow perspective has been adopted, regarding the consequences of the adoption of new work environments following COVID-19. It seems that the literature is lacking in information regarding employee use of organizational IS since the outbreak of the pandemic. Specifically, this issue has not yet been examined in relation to employees’ perception about the organization’s digital efforts and technological maturity for remote work. The present study bridges this gap. Methodology. The public sector in Israel, which employs about a third of the Israeli work-force, was chosen as a case study of information-intensive organizations. During the first year of COVID-19, 716 questionnaires were completed by employees and managers belonging to four government ministries operating in Israel. The responses were statistically analyzed using a Chi-Square and Spearman’s Rho tests. Contribution. Given that the global pandemic is an ongoing phenomenon and not a passing episode, the findings provide important theoretical and practical contributions. The period prior to the COVID-19 pandemic and the period of the pandemic are compared with regard to organizational IS use. Specifically, the study sheds new light on the fact that employee perceptions motivated increased IS use during an emergency. The results contribute to the developing body of empirical knowledge in the IS field in the era of digital transformation (DT). Findings. More than half of the respondents who reported that they did not use IS before COVID-19 stated that the pandemic did not change this. We also found a significant positive correlation between the perception of the digital efforts made by organizations to enable connection to the IS for remote work and a change in frequency of IS use. This frequency was also found to have a significant positive correlation with the perception of the organization’s technological maturity to enable effective and continuous remote work. Recommendations for Practitioners. In an era of accelerating DT, this paper provides insights that may support chief information officers and chief digital officers in understanding how to promote the use of IS. The results can be useful for raising awareness of the importance of communicating managerial messages for employees regarding the organizational strategy and the resilience achieved through IS not only in routine, but also in particular in emergency situations. Recommendations for Researchers. Considering that the continual crisis has created challenges in IS research, it is appropriate to continue researching the adaptation and acclimation of organizations to the “new normal”. Impact on Society. The COVID-19 pandemic created a sudden change in employment models, which have become more flexible than ever. The research insights enrich the knowledge about the concrete consequences of this critical change. Future Research. We suggest that researchers investigate this core issue in other sectors and/or other countries, in order to be obtain new and complementary empirical insights on a comparative basis.

Aim/Purpose: This paper aims to empirically quantify the financial distress caused by the COVID-19 pandemic on companies listed on Amman Stock Exchange (ASE). The paper also aims to identify the most important predictors of financial distress pre- and mid-pandemic. Background: The COVID-19 pandemic has had a huge toll, not only on human lives but also on many businesses. This provided the impetus to assess the impact of the pandemic on the financial status of Jordanian companies. Methodology: The initial sample comprised 165 companies, which was cleansed and reduced to 84 companies as per data availability. Financial data pertaining to the 84 companies were collected over a two-year period, 2019 and 2020, to empirically quantify the impact of the pandemic on companies in the dataset. Two approaches were employed. The first approach involved using Multiple Discriminant Analysis (MDA) based on Altman’s (1968) model to obtain the Z-score of each company over the investigation period. The second approach involved developing models using Artificial Neural Networks (ANNs) with 15 standard financial ratios to find out the most important variables in predicting financial distress and create an accurate Financial Distress Prediction (FDP) model. Contribution: This research contributes by providing a better understanding of how financial distress predictors perform during dynamic and risky times. The research confirmed that in spite of the negative impact of COVID-19 on the financial health of companies, the main predictors of financial distress remained relatively steadfast. This indicates that standard financial distress predictors can be regarded as being impervious to extraneous financial and/or health calamities. Findings: Results using MDA indicated that more than 63% of companies in the dataset have a lower Z-score in 2020 when compared to 2019. There was also an 8% increase in distressed companies in 2020, and around 6% of companies came to be no longer healthy. As for the models built using ANNs, results show that the most important variable in predicting financial distress is the Return on Capital. The predictive accuracy for the 2019 and 2020 models measured using the area under the Receiver Operating Characteristic (ROC) graph was 87.5% and 97.6%, respectively. Recommendations for Practitioners: Decision makers and top management are encouraged to focus on the identified highly liquid ratios to make thoughtful decisions and initiate preemptive actions to avoid organizational failure. Recommendation for Researchers: This research can be considered a stepping stone to investigating the impact of COVID-19 on the financial status of companies. Researchers are recommended to replicate the methods used in this research across various business sectors to understand the financial dynamics of companies during uncertain times. Impact on Society: Stakeholders in Jordanian-listed companies should concentrate on the list of most important predictors of financial distress as presented in this study. Future Research: Future research may focus on expanding the scope of this study by including other geographical locations to check for the generalisability of the results. Future research may also include post-COVID-19 data to check for changes in results.

Aim/Purpose: This study presents a novel deep learning-based framework designed to enhance spontaneous micro-expression recognition by effectively increasing the amount and variety of data and balancing the class distribution to improve recognition accuracy. Background: Micro-expression recognition using deep learning requires large amounts of data. Micro-expression datasets are relatively small, and their class distribution is not balanced. Methodology: This study developed a framework using a deep learning-based model to recognize spontaneous micro-expressions on a person’s face. The framework also includes several technical stages, including image and data preprocessing. In data preprocessing, data augmentation is carried out to increase the amount and variety of data and class balancing to balance the distribution of sample classes in the dataset. Contribution: This study’s essential contribution lies in enhancing the accuracy of micro-expression recognition and overcoming the limited amount of data and imbalanced class distribution that typically leads to overfitting. Findings: The results indicate that the proposed framework, with its data preprocessing stages and deep learning model, significantly increases the accuracy of micro-expression recognition by overcoming dataset limitations and producing a balanced class distribution. This leads to improved micro-expression recognition accuracy using deep learning techniques. Recommendations for Practitioners: Practitioners can utilize the model produced by the proposed framework, which was developed to recognize spontaneous micro-expressions on a person’s face, by implementing it as an emotional analysis application based on facial micro-expressions. Recommendation for Researchers: Researchers involved in the development of a spontaneous micro-expression recognition framework for analyzing hidden emotions from a person’s face are playing an essential role in advancing this field and continue to search for more innovative deep learning-based solutions that continue to explore techniques to increase the amount and variety of data and find solutions to balancing the number of sample classes in various micro-expression datasets. They can further improvise to develop deep learning model architectures that are more suitable and relevant according to the needs of recognition tasks and the various characteristics of different datasets. Impact on Society: The proposed framework could significantly impact society by providing a reliable model for recognizing spontaneous micro-expressions in real-world applications, ranging from security systems and criminal investigations to healthcare and emotional analysis. Future Research: Developing a spontaneous micro-expression recognition framework based on spatial and temporal flow requires the learning model to classify optimal features. Our future work will focus more on exploring micro-expression features by developing various alternative learning models and increasing the weights of spatial and temporal features.

The novel coronavirus pandemic ravaged the world in 2020, making the world fall into an unprecedented period of stagnation. This research used the Sol-Idea internet public opinion analysis platform to collect, and analyses online public opinion data associated with novel coronavirus. This research finds the following situations: 1) COVID-19 online opinion leaders are more likely to post in major discussion boards. However, opinion leaders of replies but use PTT forum as the main discussion channel; 2) According to the analysis of the content and behaviour of the account 'ebola01', it is found that the content of the posts are mostly news praising the ruling party government or mocking the opposing parties, with the sources mostly coming from media considered to be more pro-ruling party. Therefore, it can be inferred that 'ebola01' may be part of cyber army with a particular political spectrum.

Aim/Purpose Information Technology students’ learning outcomes improve when teaching methodology moves away from didactic behaviorist-based pedagogy toward a more heuristic constructivist-based version of andragogy. Background There is a distinctive difference, a notable gap, between the academic community and the business community in their views of the level of preparedness of recent information technology program graduates. Understanding how Information Technology curriculum is developed and taught along with the underpinning learning theory is needed to address the deficient attainment of learning outcomes at the heart of this matter. Methodology The case study research methodology has been selected to conduct the inquiry into this phenomenon. This empirical inquiry facilitates exploration of a contemporary phenomenon in depth within its real-life context using a variety of data sources. The subject of analysis will be two Information Technology classes composed of a combination of second year and third year students; both classes have six students, the same six students. Contribution It is the purpose of this research to show that the use of improved approaches to learning will produce more desirable learning outcomes. Findings The results of this inquiry clearly show that the use of the traditional behaviorist based pedagogic model to achieve college and university IT program learning outcomes is not as effective as a more constructivist based andragogic model. Recommendations Instruction based purely on either of these does a disservice to the typical college and university level learner. The correct approach lies somewhere in between them; the most successful outcome attainment would be the product of incorporating the best of both. Impact on Society Instructional strategies produce learning outcomes; learning outcomes demonstrate what knowledge has been acquired. Acquired knowledge is used by students as they pursue professional careers and other ventures in life. Future Research Learning and teaching approaches are not “one-size-fits-all” propositions; different strategies are appropriate for different circumstances and situations. Additional research should seek to introduce vehicles that will move learners away from one the traditional methodology that has been used throughout much of their educational careers to an approach that is better suited to equip them with the skills necessary to meet the challenges awaiting them in the professional world.

Aim/Purpose: This paper describes an empirical examination of how users’ willingness to disclose personal data is influenced by the amount of information provided on landing pages – standalone web pages created explicitly for marketing or advertising campaigns. Background: Provision of information is a central construct in the IS discipline. Content is a term commonly used to describe the information made available by a website or other electronic medium. A pertinent debate among scholars and practitioners relate to the behavioral impact of content volume: Specifically, does a greater amount of information elicit engagement and compliance, or the other way around? Methodology: A series of large-scale web experiments (n= 535 and n= 27,900) were conducted employing a between-subjects design and A/B testing. Two variants of landing pages, long and short, were created based on relevant behavioral theories. Both variants included an identical form to collect users’ information, but different amounts of provided content. User traffic was generated using Google AdWords and randomized between the page using Unbounce.com. Relevant usage metrics, such as response rate (called “conversion rate”), location, and visit time were recorded. Contribution: This research contributes to the body of knowledge on information provision and its effectiveness and carries practical and theoretical implications to practitioners and scholars in Information Systems, Informing Science, Communications, Digital Marketing, and related fields. Findings: Analyses of results show that the shorter landing pages had significantly higher conversion rates across all locations and times. Findings demonstrate a negative correlation between the content amount and consumer behavior, suggesting that users who had less information were more inclined to provide their data. Recommendations for Practitioners: At a practical level, results can empirically support business practices, design considerations, and content strategy by informing practitioners on the role of content in online commerce. Recommendation for Researchers: Findings suggest that the amount of content plays a significant role in online decision making and effective informing. They also contradict prior research on trust, persuasion, and security. This study advances research on the paradoxical relationship between the increased level of information and online decision-making and indicates that contrary to earlier work, not all persuasion theories‎ are ‎effective online. Impact on Society: Understanding how information drives behavior has implications in many domains (civic engagement, health, education, and more). This has relevance to system design and public communication in both online and offline contexts. Future Research: Using this research as a starting point, future research can examine the impact of content in other contexts, as well as other behavioral drivers (such as demographic data). This can lead to theoretical, methodological, and practical recommendations.

Aim/Purpose: This paper aimed to explore the impact of compassion fatigue, compassion satisfaction, and burn-out among the general population during the pandemic. Background: The paper has attempted to explore compassion fatigue, compassion satisfaction, and burn-out among the population at large, especially during the pandemic. This area has not been explored as yet. Methodology: A simple random sample of 98 males and 88 females was collected anonymously through a Google form survey. Part A collected demographic data and Part B comprised of 15 statements with 5 each for compassion fatigue, compassion satisfaction, and burn-out, adapted from a Compassion Fatigue/Satisfaction Self-Test. ANOVA single factor was employed for the three variables of compassion fatigue, compassion satisfaction, and burn-out using a 0.05 significance level. Correlations among the variables were also analyzed. Contribution: The present paper contributes to covering the research gap of investigating the presence of compassion fatigue, compassion satisfaction, and burn-out among the population at large comprising the age group of 18 to 60+ and from different professions. Findings: The findings revealed significant differences in the levels of compassion fatigue, compassion satisfaction, and burn-out in the population at large during the pandemic. Future Research: The findings can be further strengthened by extending it to a larger sample size across different nations and, specifically, studying gender differences during such adverse pandemic situations.

Aim/Purpose: The Covid-19 pandemic has created many adverse effects. It overloads the healthcare system, causes deaths, and angers some at anti-covid restrictions. This study examines the feasibility of using technical and technological ideas to overcome these effects. The solution is based on new knowledge about the virus, its nature, formation, and activation in the environment. Background: The rapid spread of a new coronavirus infection is taking place against the background of a lack of time required to create new treatment scenarios for the disease, development, production, and vaccine safety research. In such a situation, it became necessary to gain this time for organizing and conducting events that could reduce the burden on the healthcare system. Methodology: The science that studies the morphology, physiology, genetics, ecology, and evolution of viruses is virology. The modern development of virology is moving towards a more accurate and comprehensive description of the mechanisms of interaction of viruses with the host organism. This contributed to the emergence of genomics, transcriptomics, proteomics, and immunomics. However, in virology, there is no particular discipline that sets itself three fundamental goals: to substantiate a single concept of the emergence of viruses; to study the natural mechanisms of formation of virus molecules in the environment; to describe the natural mechanisms of activation of certain viruses in the environment that cause viral pandemics. As a result, there are many articles among the published scientific articles on viruses dealing with the mechanisms of interaction of viruses with the host organism. However, there are no articles on the natural mechanisms of formation and activation of certain viruses in the environment. In the absence of such specialized articles, we were forced to use the method of systems transdisciplinary generalization of disciplinary knowledge to achieve our article’s purpose. Generalization created new knowledge about the nature of viruses, about the mechanisms of their formation and activation in the environment and cells of biological organisms. It is logical to assume that to synchronize the state of biological objects of all functional ensembles on the planet, it is necessary to create and activate appropriate “technological tools.” We have suggested and proved that RNA viruses play the role of such tools. Piezoelectricity activates viruses. It occurs during the compression and stretching of sedimentary rocks and bases of continental plates in different territories. Contribution: The systems transdisciplinary generalization of the knowledge of scientific disciplines made it possible to edit the concept of viruses, to eliminate stereotypes that arose due to the use of unsuccessful analogies. As a result of this generalization, it was possible to prove that viruses are not intracellular parasites. The virus is a “technological tool” of the planetary organizing component. This “tool” aims to correct the genetic programs of organisms of all functional ensembles (plants, animals, people), which will maintain the state of organisms and the parameters of their metabolism in changing environmental conditions. Findings: The viruses that triggered pandemics in the 20th century and early 21st century are RNA viruses. RNA molecules play the role of “technological tools” that the planetary organizing component uses to carry out short-term and long-term adjustments and constant support of the genetic programs of biological organisms. Therefore, in such a situation, it is advisable to talk not about the fight against the virus but only about eliminating the negative manifestations of the Covid-19 pandemic: reducing the number of people in need of emergency hospitalization, eliminating cases of the acute course of the disease and deaths. It is proposed to use certain technical and technological ideas and solutions to eliminate these negative manifestations. Recommendation for Researchers: This paper recommends that researchers use new interdisciplinary and transdisciplinary approaches. They challenge assumptions and conclusions about the nature of viruses, and the mechanisms of their formation and activation in the environment can initiate. Such new research might describe the mechanisms that form and activate viruses in the environment and the body’s cells. They also might provide practical use of this knowledge to eliminate the multiple speculations and fears that arise against the background of reports of the likely appearance of more deadly viruses and viral infections. Future Research: The results of a systems transdisciplinary generalization of disciplinary knowledge about the nature and purpose of viruses are essential for expanding the horizon of the scientific worldview. Future fundamental research on the mechanisms of objective organizing constituents, a general description given in this article, will contribute to a deeper understanding of chemical and biological evolution mechanisms in which modern humanity is involved. In due time, such an understanding will allow a new look at the existing scenarios of the world socio-economic order, explore and describe new principles of sustainable development of society.

The objective of this study was to explore through the Model of Theory of Planned Behaviour the most important variables that influence the practice of physical and sports activity at home supported by virtual training in the context of the COVID-19 pandemic. A cross-study was proposed between countries from three continents, distributing the questionnaire in Spain (Europe), Pakistan (Asia), and Colombia (South America) to ensure a comprehensive study. The methodology of structural equations using partial least squares was used. The empirical exploratory study supported the hypotheses proposed, with the most important result that confinement due to the COVID-19 pandemic has been a factor causing the practice of physical and sports activity at home. This is one of the first studies to examine sports practice at home and the new context of sports practice that has generated disruptive technologies and the global crisis of the COVID-19 pandemic.

The interview questions deal with the means by which Estonia and other republics of the former Soviet Union managed to transform their educational systems and the impact of the Soviet heritage on this transformation. An interview was conducted with Professor Olav Aarna. In 1991 Professor Olav Aarna became the rector of TUT. From 2000 to 2003 he held the position of rector of the first private university in Estonia - Estonian Business School (EBS). From 2003 to 2007 Olav Aarna was member of the Estonian Parliament, serving also as Chairman of the Committee for Cultural Affairs responsible for education, research, culture and sports affairs. From 1998-2000 he was Vice Chairman of Estonian National Council for Research and Development. His experience in the field of educational legislation stems from his advisory position to the Minister of Education of Estonia from 1990 to1992. His competence in the field of the Bologna process results from the development of higher education legislation in Estonia (2002-...) and the development of a higher education quality assurance system for Estonia (2008-...). Olav Aarna has consulted third countries in the national qualifications framework (NQF) development as a European Training Foundation (ETF) expert.

In a series of qualitative and quantitative studies, we developed a model of the functions of positive work relationships, with an explicit focus on the role that these relationships play in employee flourishing. Stories that employees told about positive relationships at work revealed that relationships serve a broad range of functions, including the traditionally-studied functions of task assistance, career advancement, and emotional support, as well as less studied functions of personal growth, friendship, and the opportunity to give to others. Building on this taxonomy, we validated a scale - the Relationship Functions Inventory - and developed theory suggesting differential linkages between the relationship functions and outcomes indicative of employee flourishing. Results revealed unique associations between functions and outcomes, such that task assistance was most strongly associated with job satisfaction, giving to others was most strongly associated with meaningful work, friendship was most strongly associated with positive emotions at work, and personal growth was most strongly associated with life satisfaction. Our results suggest that work relationships play a key role in promoting employee flourishing, and that examining the differential effects of a taxonomy of relationship functions brings precision to our understand of how relationships impact individual flourishing.

We augment transaction cost economics' (TCE) bounded rationality assumption with heuristics (framing) and cognitive biases to expand the understanding of hierarchical governance in the theory. TCE traditionally puts opportunism in the frontseat, while primarily relegating bounded rationality to the support role of invoking incomplete contracts. The theory also suggests that hierarchical governance effectively mitigates opportunism-based transaction costs, making it difficult to explain why hierarchies are not always used. However, when an augmented bounded rationality assumption is incorporated into TCE, we argue, first, that bounded rationality is a separate source of transaction costs, and, second, that these costs are not equally mitigated by all forms of hierarchy. Instead, different hierarchical forms are associated with particular frames and social referents that naturally enhance specific bounded rationality-based conflicts, allowing certain hierarchical forms to mitigate bounded rationality-based transaction costs better than others. As a result, bounded rationality takes a frontseat in the theory, addressing prior critiques of TCE, expanding the governance questions addressed by the theory and creating a new moderating role for asset specificity in internal exchanges.

Improving equity in data science, edited by Colby Tofel-Grehl and Emmanuel Schanzer, is a thought-provoking exploration of how data science education can be transformed to foster equity, especially within K-16 classrooms. The editors advocate for redefining