This research study explores peer teaching and learning without a domain expert teacher, within the context of an activity where teams of second level students (~16 years old) are required to create a learning experience for their peers. The study looks at how participants would like to be taught and how they would teach their peers if given the opportunity and examines the support they require, their motivation levels, and if they actually learn curriculum content using this approach. An exploratory case study methodology was used, and the findings suggest that students want varied learning experiences that include many of the elements which would fall under the heading of 21st century learning, that with some support and encouragement they can create innovative learning experiences for their peers, and that they can learn curriculum content from the process.

The ways people connect, interact, share, and communicate have changed due to recent developments in information technology. These developments, categorized as social media, have captured the attention of business executives, technologists, and education professionals alike, and have altered many business models. Additionally, the concept of social media impacts numerous sub-disciplines within business and has become an important issue with operational, tactical, and strategic considerations. Despite this interest, many business schools do not have courses involving social media technologies and applications. In those that do, the placement and focus of the course varies considerably. This article provides motivation and insight into the process of developing an approach for effectively teaching social media use in business. Additionally, it offers implementation examples of courses taught at three major universities. The article concludes with lessons-learned that will give instructors practical guidance and ensure that social media courses taught in a business school provide students with a solid basis for integrating social media into business practice.

Aim/Purpose: This study investigates the variations in student participation patterns across different types of instructional activities, learning modes, and with different instructional guidance approaches. In the current study, different variables, modes of learning (guided versus unguided), and types of guidance (social versus cognitive) were manipulated in a series of microblogging-supported collaborative learning tasks to examine to what extent and in which aspects instructional guidance affects the effectiveness and student perception of microblogging-supported learning. Background: Despite the overwhelming agreement on the importance of instructional guidance in microblogging-supported learning environments, very few studies have been done to examine the specificity of guidance, such as how to structure and support microblogging activities, as well as what types of guidance are appropriate in what learning contexts. Methodology: This semester-long study utilized a case-study research design via a multi-dimensional approach in a hybrid classroom with both face-to-face and online environments. Tweets were collected from four types of activities and coded based on content within their contextual setting. Twenty-four college students participated in the study. Contribution: In response to the call to improve social media learning environments under-scored in contemporary education, the current case study took an initial step aiming at deepening the understanding of the role of instructional guidance in microblogging-supported learning environments. Findings: This study showcases that with instructor facilitation, students succeeded in being engaged in a highly participatory and interactive learning experience across a variety of tasks and activities. This study indicates that students’ perspectives of social media tools rely heavily on what instructors do with the tool and how the instructional activities are structured and supported. Instructors’ scaffolding and support is instrumental in keeping students on task and engaging students with meaningful events, thus ensuring the success of microblogging-based learning activities. Meanwhile, students’ perception of usefulness of instructional guidance is closely related to their own pre-perception and experience. Recommendations for Practitioners: When incorporating social media tools, it is important to examine learner’s prior knowledge and comfort level with these tools and tailor the design of instructional activities to their attributes. It is also vital to monitor student progress, adjust the type and amount of guidance and scaffolding provided as they progress, and eventually remove the scaffolding until students can demonstrate that they can perform the task successfully without assistance. Recommendation for Researchers: Due to many other potential factors in place that could potentially influence student learning, no conclusive remarks can be made regarding the superiority of either one type of guidance approach. Future researchers should continue to develop robust research methodologies to seek ways to better operationalize this variable and strive to understand its effect. Future Research: Future replication studies in other settings, with a larger sample size, and different populations will certainly provide further insights on the effects of instructional guidance in microblogging-based learning. Alternative coding methods may also shed light on differences in student interaction in terms of content diversity and depth of learning when analyzing the tweets. Advanced data collection techniques may be explored to ascertain the completeness of data collection.

Aim/Purpose: The main aim of this study was modeling a collaborative process for knowledge visualization, via the creation of infographics. Background: As an effective method for visualizing complex information, creating infographics requires learners to generate and cultivate a deep knowledge of content and enables them to concisely visualize and share this knowledge. This study investigates creating infographics as a knowledge visualization process for collaborative learning situations by integrating the infographic design model into the team-based and technology-mediated Bridge21 learning model. Methodology: This study was carried out using an educational design perspective by conducting three main cycles comprised of three micro cycles: analysis and exploration; design and construction; evaluation and reflection. The process and the scaffolding were developed and enhanced from cycle to cycle based on both qualitative and quantitative methods by using the infographic design rubric and researcher observations acquired during implementation. Respectively, twenty-three, twenty-four, and twenty-four secondary school students participated in the infographic creation process cycles. Contribution: This research proposes an extensive step-by-step process model for creating infographics as a method of visualization for learning. It is particularly relevant for working with complex information, in that it enables collaborative knowledge construction and sharing of condensed knowledge. Findings: Creating infographics can be an effective method for collaborative learning situations by enabling knowledge construction, visualization and sharing. The Bridge21 activity model constituted the spine of the infographic creation process. The content generation, draft generation, and visual and digital design generation components of the infographic design model matched with the investigate, plan and create phases of the Bridge21 activity model respectively. Improvements on infographic design results from cycle to cycle suggest that the revisions on the process model succeeded in their aims. The rise in each category was found to be significant, but the advance in visual design generation was particularly large. Recommendations for Practitioners: The effectiveness of the creation process and the quality of the results can be boosted by using relevant activities based on learner prior knowledge and skills. While infographic creation can lead to a focus on visual elements, the importance of wording must be emphasized. Being a multidimensional process, groups need guidance to ensure effective collaboration. Recommendation for Researchers: The proposed collaborative infographic creation process could be structured and evaluated for online learning environments, which will improve interaction and achievement by enhancing collaborative knowledge creation. Impact on Society: In order to be knowledge constructors, innovative designers, creative communicators and global collaborators, learners need to be surrounded by adequate learning environments. The infographic creation process offers them a multidimensional learning situation. They must understand the problem, find an effective way to collect information, investigate their data, develop creative and innovative perspectives for visual design and be comfortable for using digital creation tools. Future Research: The infographic creation process could be investigated in terms of required learner prior knowledge and skills, and could be enhanced by developing pre-practices and scaffolding.

Aim/Purpose: One of the main objectives of higher education institutions is to provide a high-quality education to their students and reduce dropout rates. This can be achieved by predicting students’ academic achievement early using Educational Data Mining (EDM). This study aims to predict students’ final grades and identify honorary students at an early stage. Background: EDM research has emerged as an exciting research area, which can unfold valuable knowledge from educational databases for many purposes, such as identifying the dropouts and students who need special attention and discovering honorary students for allocating scholarships. Methodology: In this work, we have collected 300 undergraduate students’ records from three departments of a Computer and Information Science College at a university located in Saudi Arabia. We compared the performance of six data mining methods in predicting academic achievement. Those methods are C4.5, Simple CART, LADTree, Naïve Bayes, Bayes Net with ADTree, and Random Forest. Contribution: We tested the significance of correlation attribute predictors using four different methods. We found 9 out of 18 proposed features with a significant correlation for predicting students’ academic achievement after their 4th semester. Those features are student GPA during the first four semesters, the number of failed courses during the first four semesters, and the grades of three core courses, i.e., database fundamentals, programming language (1), and computer network fundamentals. Findings: The empirical results show the following: (i) the main features that can predict students’ academic achievement are the student GPA during the first four semesters, the number of failed courses during the first four semesters, and the grades of three core courses; (ii) Naïve Bayes classifier performed better than Tree-based Models in predicting students’ academic achievement in general, however, Random Forest outperformed Naïve Bayes in predicting honorary students; (iii) English language skills do not play an essential role in students’ success at the college of Computer and Information Sciences; and (iv) studying an orientation year does not contribute to students’ success. Recommendations for Practitioners: We would recommend instructors to consider using EDM in predicting students’ academic achievement and benefit from that in customizing students’ learning experience based on their different needs. Recommendation for Researchers: We would highly endorse that researchers apply more EDM studies across various universities and compare between them. For example, future research could investigate the effects of offering tutoring sessions for students who fail core courses in their first semesters, examine the role of language skills in social science programs, and examine the role of the orientation year in other programs. Impact on Society: The prediction of academic performance can help both teachers and students in many ways. It also enables the early discovery of honorary students. Thus, well-deserved opportunities can be offered; for example, scholarships, internships, and workshops. It can also help identify students who require special attention to take an appropriate intervention at the earliest stage possible. Moreover, instructors can be aware of each student’s capability and customize the teaching tasks based on students’ needs. Future Research: For future work, the experiment can be repeated with a larger dataset. It could also be extended with more distinctive attributes to reach more accurate results that are useful for improving the students’ learning outcomes. Moreover, experiments could be done using other data mining algorithms to get a broader approach and more valuable and accurate outputs.

Monetary policy is essential for sustainable growth where effective monetary policies can improve investment, employment, and consumption by fostering a balanced and resilient economy. However, sustainable development is vital for harmonising economic growth, social equity, and environmental preservation. A number of factors have been discussed in the literature that impact sustainable development. However, this study explicitly tries to investigate the nexus among the monetary policy (MP) toward sustainable development (SD) with the mediation of e-banking services (e-BS) and moderation of financial risk management (FRM) from China drawing on stakeholder theory. It discovered a significant connection between monetary policy and sustainable development along with sub-dimensions of SD. Likewise, this study confirmed a positive mediating influence of e-BS between monetary policy and sustainable development. Finally, the study additionally ensured a positive moderation of financial risk between monetary policy and sustainable development, respectively. These outcomes bestow several interesting insights into monetary policy, e-banking services, financial risk management, and sustainable development.

Green entrepreneurial intention (GEI) in the agriculture sector signifies agricultural businesses' strong determination to embrace environmentally sustainable practices and innovative eco-friendly approaches. To understand farmers' GEI, the research applied theories of entrepreneurial events and institutional theory. A model was developed and empirically validated through structural equation modelling (SEM). A questionnaire survey was used to collect data from 211 farmers from the southern region of India. Findings revealed that perceived desirability, perceived feasibility, mimetic pressure, and entrepreneurial mindset positively influenced GEI. Entrepreneurial mindset played a mediating role in strengthening the farmers GEI. This study contributes to understanding GEI in agriculture and informs strategies for promoting sustainable farming practices.

In India, 70% of the Indian population is dependent on agriculture, yet agriculture generates only 13% of the country's gross domestic product. Several factors contribute to high levels of stress among farmers in India, such as increased input costs, draughts, and reduced revenues. The problem lies in the absence of an integrated farm advisory system. A farmer needs help to bridge this information gap, and they need it early in the crop's lifecycle to prevent it from being destroyed by pests or diseases. This research involves developing deep learning algorithms such as <i>ResNet18</i> and <i>DenseNet121</i> to help farmers diagnose crop diseases earlier and take corrective actions. By using deep learning techniques to detect these crop diseases with images farmers can scan or click with their smartphones, we can fill in the knowledge gap. To facilitate the use of the models by farmers, they are deployed in Android-based smartphones.

This paper proposes a method for predicting college students' performance based on learning behaviour data mining. The method addresses the issue of limited sample size affecting prediction accuracy. It utilises the K-means clustering algorithm to mine learning behaviour data and employs a density-based approach to determine optimal clustering centres, which are then output as the results of the clustering process. These clustering results are used as input for an attention encoder-decoder model to extract features from the learning behaviour sequence, incorporating an attention mechanism, sequence feature generator, and decoder. The characteristics derived from the learning behaviour sequence are then used to establish a prediction model for college students' performance, employing support vector regression. Experimental results demonstrate that this method accurately predicts students' performance with a relative error of less than 4% by leveraging the results obtained from learning behaviour data mining.

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.

Using information criteria is a common method for making a decision about which model to use for forecasting. There are many different methods for evaluating forecasting models, such as MAE, RMSE, MAPE, and Theil-U, among others. After the creation of AIC, AICc, HQ, BIC, and BICc, the two criteria that have become the most popular and commonly utilised are Bayesian IC and Akaike's IC. In this investigation, we are innovative in our use of exponential weighting to get the log-likelihood of the information criteria for model selection, which means that we propose assigning greater weight to more recent data in order to reflect their increased precision. All research data is from the major stock markets' daily observations, which include the USA (GSPC, DJI), Europe (FTSE 100, AEX, and FCHI), and Asia (Nikkei).

The patient must follow a good diet to lessen the risk of health conditions. The body needs vitamins, minerals, and nutrients for illness prevention. When the human body does not receive the right amount of nutrients, nutritional disorders can develop, which can cause a number of different health issues. Chronic diseases like diabetes and hypertension can be brought on by dietary deficiencies. The human body receives the nutrients from a balanced diet to function properly. This research has a prototype that enables patients to find nutritious food according to their health preferences. It suggests meals based on their preferences for nutrients such as protein, fibre, high-fibre, low-fat, etc., and diseases such as pregnancy and diabetes. The process implements the recommendation based on the patient's profile (content-relied, K-NN), recommendation relied on patients with similar profiles, and recommendation based on the patient's past or recent activity.

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.

This study examines diverse information media in order to identify those formats that are most suitable for consumption via handheld mobile devices, namely, smartphones and tablets. The preferences of the users are measured objectively by analyzing actual data of their relative use of handheld mobile devices and personal computing (PC) desktop devices, including laptops and notebooks, for consumption of information presented in various formats. Our findings are based on Google Analytics pageview data of five course Websites during a period of three semesters, by 11,557 undergraduate students. M-learning contexts were chosen, since in a learning environment the interests of information providers (i.e., the instructors) are in accord with those of the information consumers (i.e., the students), whereas in commercial settings there may be conflicts of interests. Our findings demonstrate that although about 90% of the pageviews were via PC devices, the rate of smartphone use for consuming learning content in diverse information media is gradually increasing as time goes by, whereas the rate of tablet use for these purposes is stagnant. The most promising direction for smartphone development, emanating from the findings, is online video content.

Aim/Purpose: This study was designed to examine the effectiveness of mentor’s work with immigrant children and adolescents at risk, using the Elements Way. Background: The New Media offers our “screen kids” a lot of information, many behavioral models, and a new type of social communication. The Elements Way is an educational method designed to enhance openness, development, breakthroughs, goal achievement, and transformation in the age of media and social networks. Methodology: The Elements Way was developed following research on communication in the diversified media, especially new media such as Facebook, WhatsApp, and television reality shows, and the study is an examination of the effectiveness of mentors’ work with immigrant children and adolescents at risk, using the Elements Way. All mentors had been trained in the Elements Way. The study population included 640 mentors working with immigrants’ children in Israel. The work was conducted in 2010-2013. The mixed-methods approach was selected to validate findings. Contribution: Empowering children and enhancing their ability to cope; Creating openness and sharing, making children more attentive to the significant adults in their lives; Supporting children who face the complex reality that characterizes our age. Findings: Significant differences were found in the mentors’ conduct with the children. Work programs were designed and implemented with care and consistency, and mentors succeeded in generating change within the children and achieving desired goals. Of the 640 participating mentors, 62 were not able to promote the child, and interviews with them revealed that their work with the children was not consistent with the Elements Way and began from a different vantage point. Recommendations for Practitioners: Success factors: Self-awareness and awareness of one’s surroundings. Empathy. Willingness to engage in significant interactions. Self-cleansing and self-reflection. Ability to engage in a personal and interpersonal dialogue. Ability to accept and contain the child. Cooperation with the child in creating a work program and assisting the child to achieve the goals that were set in the program. Recommendation for Researchers: Future studies should focus on analyzing the discussions of children and adolescents, to add depth to our insights regarding children and adolescents’ perception of the mentors’ work from their perspective. Impact on Society: Finding the “keys” to openness, development, goal achievement, and transformation in our work with “screen kids.” Future Research: Studies that are designed to examine the effectiveness of mentor’s work with immigrant children and adolescents at risk, using the Elements Way.

Aim/Purpose: Medical images are very sensitive data that can be transferred to medical laboratories, professionals, and specialist for referral cases or consultation. Strict security measures must be utilized to keep these data secured in computer networks when transferred to another party. On a daily basis, unauthorized users derive ways to gain access to sensitive patient medical information. Background: One of the best ways to which medical image could be kept secured is through the use of quantum cryptography Methodology : Applying the principles of quantum mechanics to cryptography has led to a remarkable new dimension in secured network communication infrastructure. This enables two legitimate users to produce a shared secret random bit string, which can be used as a key in cryptographic applications, such as message encryption and authentication. Contribution: This paper can make it possible for the healthcare and medical professions to construct cryptographic communication systems to keep patients’ transferred data safe and secured. Findings: This work has been able to provide a way for two authorized users who are in different locations to securely establish a secret network key and to detect if eavesdropping (a fraudulent or disruption in the network) has occurred Recommendations for Practitioners: This security mechanism is recommended for healthcare providers and practitioners to ensure the privacy of patients’ medical information. Recommendation for Researchers: This paper opens a new chapter in secured medical records Impact on Society Quantum key distribution promises network security based on the fundamental laws of quantum mechanics by solving the problems of secret-key cryptography . Future Research: The use of post-quantum cryptography can be further researched.

Aim/Purpose: Although cassava is one of the crops that can be grown during the dry season in Northeastern Thailand, most farmers in the region do not know whether the crop can grow in their specific areas because the available agriculture planning guideline provides only a generic list of dry-season crops that can be grown in the whole region. The purpose of this research is to develop a predictive model that can be used to predict suitable areas for growing cassava in Northeastern Thailand during the dry season. Background: This paper develops a decision support system that can be used by farmers to assist them determine if cassava can be successfully grown in their specific areas. Methodology: This study uses satellite imagery and data on land characteristics to develop a machine learning model for predicting suitable areas for growing cassava in Thailand’s Nakhon-Phanom province. Contribution: This research contributes to the body of knowledge by developing a novel model for predicting suitable areas for growing cassava. Findings: This study identified elevation and Ferric Acrisols (Af) soil as the two most important features for predicting the best-suited areas for growing cassava in Nakhon-Phanom province, Thailand. The two-class boosted decision tree algorithm performs best when compared with other algorithms. The model achieved an accuracy of .886, and .746 F1-score. Recommendations for Practitioners: Farmers and agricultural extension agents will use the decision support system developed in this study to identify specific areas that are suitable for growing cassava in Nakhon-Phanom province, Thailand Recommendation for Researchers: To improve the predictive accuracy of the model developed in this study, more land and crop characteristics data should be incorporated during model development. The ground truth data for areas growing cassava should also be collected for a longer period to provide a more accurate sample of the areas that are suitable for cassava growing. Impact on Society: The use of machine learning for the development of new farming systems will enable farmers to produce more food throughout the year to feed the world’s growing population. Future Research: Further studies should be carried out to map other suitable areas for growing dry-season crops and to develop decision support systems for those crops.

Aim/Purpose: Any system that aims to address the task of modeling social media communication need to deal with the usage of emojis. Efficient prediction of the most likely emoji given the text of a message may help to improve different NLP tasks. Background: We explore two tasks: emoji identification and emoji prediction. While emoji prediction is a classification task of predicting the emojis that appear in a given text message, emoji identification is the complementary preceding task of determining if a given text message includes emojies. Methodology: We adopt a supervised Machine Learning (ML) approach. We compare two text representation approaches, i.e., n-grams and character n-grams and analyze the contribution of additional metadata features to the classification. Contribution: The task of emoji identification is novel. We extend the definition of the emoji prediction task by allowing to use not only the textual content but also meta-data analysis. Findings: Metadata improve the classification accuracy in the task of emoji identification. In the task of emoji prediction it is better to apply feature selection. Recommendations for Practitioners: In many of the cases the classifier decision seems fitter to the comment content than the emoji that was chosen by the commentator. The classifier may be useful for emoji suggestion. Recommendation for Researchers: Explore character-based representations rather than word-based representations in the case of morphologically rich languages. Impact on Society: Improve the modeling of social media communication. Future Research: We plan to address the multi-label setting of the emoji prediction task and to investigate the deep learning approach for both of our classification tasks

Aim/Purpose. This study examined the self-perception of adolescents and young people aged 17-21 – how they perceived their personal characteristics, self-image, vitality, belonging to a local and global (glocal) society, happiness index and activity, media usage habits in general and smartphones in particular – in other words, it sought to produce a sketch of their character. Background. Different age groups are influenced by various factors that shape them, including living environment, technological developments, experiences, common issues, events of glocal significance, and more. People belonging to Gen Z were born at the end of the previous century and the beginning of the 21st century (up to 2010). This generation was born into the digital technological age and is the first one born into the environment defined by smartphones, and social media. Its members are referred to as “digital natives” because they were born after the widespread adoption of digital technology in the Western world. They entered an environment characterized by the widespread daily use of smartphones, the Internet, and technology in general. Methodology. This was a quantitative study based on a sample of 418 Israeli adolescents and young people aged 17-21. The following questionnaires were administered anonymously and disseminated online to an audience of youths aged 17-21 across Israel: A demographic questionnaire; Self-esteem; Vitality; Belonging vs. alienation; Social-emotional aspects; Usage habits in digital environments; Usage habits of learning on a smartphone; Open questions. Contribution. The current study tried to define clusters to characterize adolescents and youth aged 17-21. Findings Results show that study participants had high self-esteem and vitality, felt be-longing, happy, and satisfied with their life, and perceived themselves as active and enterprising at an average level or above. The study identified two clusters. Participants in Cluster 1 were characterized by higher parameter averages than those in Cluster 2 on the self-image, vitality, belonging, happiness, and activism scales. Participants in Cluster 1 felt that using a smartphone made life easier, helped them solve everyday problems, made everyday conduct easier, and allowed them to express themselves, keep up to date with what is happening with their friends, disseminate information conveniently, be involved in social life, and establish relationships with those around them. They thought that it was easy to collaborate with others and to plan activities and events. Recommendations for Practitioners. When examining cluster correlations with data in relation to other variables, it is apparent that participants in Cluster 1 had more options to reach out for help, report more weekly hours spent talking and meeting with friends and feel that using a smartphone makes everyday life easier and facilitates their day-to-day conduct than did participants in Cluster 2. The smartphone allows them to express themselves, keep updated regarding what is happening with their friends and disseminate information easily, helps them be involved in social life and establish connections with those around them. They find it easy to communicate and cooperate with others and to plan activities and events. By contrast, participants in Cluster 2 felt that the smartphone complicates things for them and creates problems in their daily lives. They feel that the use of social networks burdens them and that the smartphone prevents them from being more involved in their social life, and from establishing relationships with those around them. They felt that communication by smartphone creates more problems in understanding messages. Recommendations for Researchers. One of the challenges of this generation is forming an independent identity and self-regulation in a digital, global, across-the-border era that offers a variety of possibilities and communities. They must examine the connection between the digital and personal spaces, to be able to enjoy virtual communities and a sense of togetherness, and at the same time maintain privacy, autonomy, and individuality. Many studies point to the blurring of boundaries between the private-personal and the public, at numerous problems in social networks, including social problems, shaming, and exclusion from various groups and activities. The fear of shaming and the desire to keep up with everything that is happening create a state of mental stress, and adolescents often feel that they urgently need to check their smartphones. Sharing with others can help them deal with negative content and experiences and avoid the dangers lurking in their web surfing. Yet sharing, especially with friends, often causes intimate content to become public and leads to shaming and invasion of privacy. Impact on Society. Gen Z was born into an environment where smartphones, the Internet, and technology in general, are widely used in everyday routine, and they make extensive use of technological means in all areas of life. One of the characteristics of this generation is “globalization.” The present study showed that about 84% of participants felt to a moderate degree or higher that they were citizens of the world. Future Research. The findings of this study revealed a significant difference in self-image between males and females. An attempt was made to explain the findings in light of previous studies, but the need arose for studies on the self-image of young people of Gen Z that would shed light on the subject.

Aim/Purpose: A vital business activity within organizations is tacit knowledge (TK) transfer. This work aims to propose a novel framework for TK transferability in multinational corporations (MNCs) from the information and communication technology (ICT) perspective. Background: In the past two decades, researchers have developed several frameworks for TK transfer based on humanistic, business, and educational perspectives. However, a review of the existing work revealed that TK transfer has seldom been examined from an ICT perspective. Methodology: A qualitative method was adopted because it was considered the most appropriate for the research requirements. A grounded theory approach was employed to generate the items that potentially affect the transferability of TK. Face-to-face semi-structured interviews were conducted to collect data, along with observations when possible. The study sample consisted of 20 expert interviewees in Malaysia. An inductive ongoing data analysis process based on grounded theory via open, axial, and selective coding was used as the primary analysis method. In addition, comparative and frequency count analyses were used to examine the collected data. Contribution: The main contribution of this study is its use of the grounded theory approach, which resulted in the generation of items that affect the transferability of TK, not all of which had been identified by previous researchers. This paper reports one of the few inductive studies conducted on TK transferability among MNCs and, therefore, makes distinct contributions to the literature on TK management, specifically bringing to the fore the intricacies involved in TK transferability. Findings: Information and communication technology plays a crucial role in and has a positive impact on TK transfer in MNCs. This study evaluated the potential of various ICT channels to facilitate TK transfer. The findings show that ICT tools cannot completely substitute for face-to-face contact. Tacit knowledge that has a high degree of complexity requires sophisticated channel features for its transfer. From this standpoint, virtual face-to-face communication is the richest communication medium in the ICT hierarchy. On the other hand, email and voicemail are less effective channels for TK transfer, while synchronous groupware is an intermediate communication medium. The findings highlight the importance of utilizing an appropriate mix of channels to improve TK transferability. Recommendations for Practitioners: It is recommended that practitioners consider the diverse aspects of TK and the potential of a range of ICT channels for the TK transfer process in order to increase the efficiency of TK transfer. The analytic categories developed in this study may give managers new insights into and a better understanding of how TK can be supported in a modern organization and how to overcome the barriers to TK transfer.

Aim/Purpose: The objective of this study was to assess the interrelationships among knowledge management infrastructure, knowledge management process, intellectual capital, and organization performance. Background: Although knowledge management capability is extensively used by organizations, reaching their maximum financial and non-financial performances has not been fully researched. Therefore, organizations need to optimize their performance by exploiting knowledge management capability through the accumulation of intellectual capital, where the new competitiveness is shifting from tangible to intangible resources. Methodology: This study adopted a positivist philosophy and deductive approach to accomplish the main goal of this research. Moreover, this research employed a quantitative approach since this study is concerned with causal relationship between variables. A questionnaire-based survey was designed to evaluate the research model using a convenience sample of 134 employees from the food industry sector in Jordan. Surveyed data was examined following the structural equation modeling procedures. Contribution: This study highlighted the potential benefits of applying the knowledge management capabilities, intellectual capital, and organizational performance to the food industrial sector in Jordan. Future research suggestions are also provided. Findings: Results indicated that knowledge management infrastructure had a positive effect on knowledge management process. In addition, knowledge management process impacted positively intellectual capital and organization performance and mediated the relationship between knowledge management infrastructure and intellectual capital. However, knowledge management infrastructure did not positively associate to organization performance. Recommendations for Practitioners: The current model is designed to help managers and decision makers to improve their management capabilities as well as their organization financial and non-financial performance through exploiting the organizational knowledge management infrastructure and intellectual capital approaches. Recommendation for Researchers: Our findings can be used as a base of knowledge to conduct further studies about knowledge management capabilities, intellectual capital, and organization performance following different criteria and research procedures. Impact on Society: The designed model highlights a significant organizational performance approach that can influence Jordanian food industrial sector positively. Future Research: The current designed research model can be applied and assessed further in other sectors including banking and industrial sectors across developed and developing countries. Also, we suggest that in addition to focusing on knowledge management process and intellectual capital as mediating variables, future research could test our findings in a longitudinal study and examine how to affect financial and non-financial performance.

Aim/Purpose: This study aims at (1) extending an existing theoretical framework to gain a deeper understanding of the technology acceptance process, notably of the Facebook social network in an unexplored Middle East context, (2) investigating the influence of social support theory on Facebook adoption outside the work context, (3) validating the effectiveness of the proposed research model for enhancing Facebook adoption, and (4) determining the effect of individual differences (gender, age, experience, and educational level) amongst Facebook users on the associated path between the proposed model constructs. Background: Social networking sites (SNSs) are widely adopted to facilitate social interaction in the Web-based medium. As such, this present work contends that there is a gap in the existing literature, particularly in the Middle East context, as regards an empirical investigation of the relationship between the social, psychological, individual, and cognitive constructs potentially affecting users’ intention to accept SNSs. The present research, therefore, attempts to address this deficit. The relevance of this work is also considered in light of the scarcity of empirical evidence and lack of detailed research on the effect of social support theory with regard to SNS adoption in a non-work context. Methodology: A quantitative research approach was adopted for this study. The corresponding analysis was carried out based on structural equation modelling (SEM), more specifically, partial least squares (PLS), using SmartPLS software. Earlier research recommended the PLS approach for exploratory studies when extending an existing model or developing a new theory. PLS is also a superior method of complex causal modelling. Moreover, a multi-group analysis technique was adopted to investigate the moderating influence of individual differences. This method divides the dataset into two groups and then computes the cause and effect relationships between the research model variables for each set. The analysis of an in-person survey with a sample of Facebook users (N=369) subsequently suggested four significant predictors of continuous Facebook use. Contribution: This study contributes to the body of knowledge relating to SNSs by providing empirical evidence of constructs that influence Facebook acceptance in the case of a developing country. It raises awareness of antecedents of Facebook acceptance at a time when SNSs are widely used in Arab nations and worldwide. It also contributes to previous literature on the effectiveness of the unified theory of acceptance and use of technology (UTAUT) in different cultural contexts. Another significant contribution of this study is that it has reported on the relevance of social support theory to Facebook adoption, with this theory demonstrating a significant and direct ability to predict Facebook acceptance. Finally, the present research identified the significant moderating effect of individual differences on the associated path between the proposed model constructs. This means that regardless of technological development, individual gaps still appeared to exist among users. Findings: The findings suggested four significant predictors of continuous Facebook use, namely, (a) performance expectancy, (b) peer support, (c) family support, and (d) perceived playfulness. Furthermore, behavioral intention and facilitating conditions were found to be significant determinants of actual Facebook use, while individual differences were shown to moderate the path strength between several variables in the proposed research model. Recommendations for Practitioners: The results of the present study make practical contributions to SNS organizations. For example, this research revealed that users do not adopt Facebook because of its usefulness alone; instead, users’ acceptance is developed through a sequence of variables such as individual differences, psychological factors, and social and organizational beliefs. Accordingly, social media organizations should not consider only strategies that apply to just one context, but also to other contexts characterized by different beliefs, perceptions, and cultures. Moreover, the evidence provided here is that social support theory has a significant influence on SNSs acceptance. This suggests that social media organizations should provide services to support this concept. Furthermore, the significant positive effect of perceived playfulness on the intention to use SNSs implied that designers and organizations should pay further attention to the entertainment services provided by social networks. Recommendation for Researchers: To validate the proposed conceptual framework, researchers from different countries and cultures are invited to apply the model. Moreover, a longitudinal research design could be implemented to gather data over a longer period, in order to investigate whether users have changed their attitudes, beliefs, perceptions, and intention by the end of the study period. Other constructs, such as individual experience, compatibility, and quality of working life could be included to improve the power of the proposed model. Impact on Society: Middle Eastern Facebook users regard the network as an important tool for interacting with others. The increasing number of Facebook users renders it a tool of universal communication and enjoyment, as well as a marketing network. However, knowledge of the constructs affecting the application of SNSs is valuable for ensuring that such sites have the various functions required to suit different types of user. Future Research: It is hoped that our future research will build on the results of this work and attempt to provide further explanation of why users accept SNSs. In this future research, the proposed research model could be adopted to explore SNSs acceptance in other developing countries. Researchers might also include other factors of potential influence on SNSs acceptance. The constructs influencing acceptance of other social networks could then be compared to the present research findings and thus, the differences and similarities would be highlighted.

Aim/Purpose: Vis-à-vis management of crisis and disaster situations, this paper focuses on important use cases of social media functions, such as information collection & dissemination, disaster event identification & monitoring, collaborative problem-solving mechanism, and decision-making process. With the prolific utilization of disaster-based ontological framework, a strong disambiguation system is realized, which further enhances the searching capabilities of the user request and provides a solution of unambiguous in nature. Background: Even though social media is information-rich, it has created a challenge for deriving a decision in critical crisis-related cases. In order to make the whole process effective and avail quality decision making, sufficiently clear semantics of such information is necessary, which can be supplemented through employing semantic web technologies. Methodology: This paper evolves a disaster ontology-based system availing a framework model for monitoring uses of social media during risk and crisis-related events. The proposed system monitors a discussion thread discovering whether it has reached its peak or decline after its root in the social forum like Twitter. The content in social media can be accessed through two typical ways: Search Application Program Interfaces (APIs) and Streaming APIs. These two kinds of API processes can be used interchangeably. News content may be filtered by time, geographical region, keyword occurrence and availability ratio. With the support of disaster ontology, domain knowledge extraction and comparison against all possible concepts are availed. Besides, the proposed method makes use of SPARQL to disambiguate the query and yield the results which produce high precision. Contribution: The model provides for the collection of crisis-related temporal data and decision making through semantic mapping of entities over concepts in a disaster ontology we developed, thereby disambiguating potential named entities. Results of empirical testing and analysis indicate that the proposed model outperforms similar other models. Findings: Crucial findings of this research lie in three aspects: (1) Twitter streams and conventional news media tend to offer almost similar types of news coverage for a specified event, but the rate of distribution among topics/categories differs. (2) On specific events such as disaster, crisis or any emergency situations, the volume of information that has been accumulated between the two news media stands divergent and filtering the most potential information poses a challenging task. (3) Relational mapping/co-occurrence of terms has been well designed for conventional news media, but due to shortness and sparseness of tweets, there remains a bottleneck for researchers. Recommendations for Practitioners: Though metadata avails collaborative details of news content and it has been conventionally used in many areas like information retrieval, natural language processing, and pattern recognition, there is still a lack of fulfillment in semantic aspects of data. Hence, the pervasive use of ontology is highly suggested that build semantic-oriented metadata for concept-based modeling, information flow searching and knowledge exchange. Recommendation for Researchers: The strong recommendation for researchers is that instead of heavily relying on conventional Information Retrieval (IR) systems, one can focus more on ontology for improving the accuracy rate and thereby reducing ambiguous terms persisting in the result sets. In order to harness the potential information to derive the hidden facts, this research recommends clustering the information from diverse sources rather than pruning a single news source. It is advisable to use a domain ontology to segregate the entities which pose ambiguity over other candidate sets thus strengthening the outcome. Impact on Society: The objective of this research is to provide informative summarization of happenings such as crisis, disaster, emergency and havoc-based situations in the real world. A system is proposed which provides the summarized views of such happenings and corroborates the news by interrelating with one another. Its major task is to monitor the events which are very booming and deemed important from a crowd’s perspective. Future Research: In the future, one shall strive to help to summarize and to visualize the potential information which is ranked high by the model.