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.

The purpose of the study is to identify influential factors in the use of collaborative networks within the context of manufacturing. The study aims to investigate factors that influence employees’ learning, and to bridge the gap between theory and praxis in collaborative networks in manufacturing. The study further extends the boundary of a collaborative network beyond enterprises to include suppliers, customers, and external stakeholders. It provides a holistic perspective of collaborative networks within the complexity of the manufacturing environment, based on empirical evidence from a questionnaire survey of 246 respondents from diverse manufacturing industries. Drawing upon the socio-technical systems (STS) theory, the study presents the theoretical context and interpretations through the lens of manufacturing. The results show significant influences of organizational support, promotive interactions, positive interdependence, internal-external learning, perceived effectiveness, and perceived usefulness on the use of collaborative networks among manufacturing employees. The study offers a basis of empirical validity for measuring collaborative networks in organizational learning and knowledge/information sharing in manufacturing.

Aim/Purpose: This paper aims to present an example of the application of a Knowledge Man-agement (KM) framework to automotive original component manufacturers (OEMs). The objective is to explore KM according to the four pillars of a selected KM framework. Background: This research demonstrates how a framework, namely the George Washington University’s Four Pillar Framework, can be used to determine the KM status of the automotive OEM industry, where knowledge is complex and can influence the complexity of the KM system (KMS) used. Methodology: An empirical study was undertaken using a questionnaire to gather quantitative data. There were 38 respondents from the National Association of Automotive Component and Allied Manufacturers (NAACAM) and suppliers from three major automotive OEMs. The respondents were required to be familiar with the company’s KMS. Contribution: Currently there is a limited body of research available on the KM implementation frameworks for the automotive industry. This study presents a novel approach to the use of a KM framework to reveal the status of KM in automotive OEMs. At the time of writing, the relationship between the four pillars and the complexity of KMS had not yet been determined. Findings: The results indicate that there is a need to improve KM in the automotive OEM industry. According to the relationships investigated, the four pillars, namely leadership, organization, technology and learning, are considered important for KM, regardless of the level of KMS complexity, Recommendations for Practitioners: Automotive OEMs need to ensure that the KM aspects are established and should be periodically evaluated by using a KM framework such as the George Washington University’s Four Pillar Framework to identify KM weaknesses. Recommendation for Researchers: The establishment and upkeep of a successful KM environment is challenging due to the complexity involved with various influencing aspects. To ensure that all aspects are considered in KM environments, comprehensive KM frameworks, such as the George Washington University’s Four Pillar Framework, need to be applied. Impact on Society: The status of KM management and accessibility of knowledge in organizations needs to be periodically examined, in order to improve supplier and OEM knowledge sharing. Future Research: Although the framework used provides a process for KM status determination, this study could be extended by investigating a methodology that includes KMS best practice and tools. This study could be repeated at a national and international level to provide an indication of KM practice within the entire automotive industry.

Aim/Purpose: This study aims to develop a descriptive typology to better identify leadership toward creativity in virtual work in different types of companies. Background: The study empirically explores how leadership toward creativity occurs in virtual work and uses the theoretical lenses of creativity-conducive leadership and heterarchy to generate a typology. Methodology : A multiple qualitative case study design, interpretivist approach, and abductive analysis are applied. Data is collected by interviewing 21 leaders and employees face-to-face in four companies in the ICT sector and one business advisor company. Contribution: The empirical evidence of this study enriches the understanding of leadership toward creativity in virtual work and contributes to the limited empirical knowledge on leadership that stimulates a virtual workforce to achieve creativity. Findings: The four different types of companies in the typology utilize various transitions toward leadership creativity in virtual work. The trend in leadership in the existing virtually networked business environment is toward the “collective mind” company, which is characterized by shared values, meaningful work, collective intelligence, conscious reflection, transparency, coaching, empowering leadership by example, effective multichannel interaction, and assertiveness. The findings empirically support applying a heterarchy perspective to lead a virtual workforce toward creativity and promote leaders who are genuinely interested in people, their development, collaboration, and technology. Recommendations for Practitioners: The typology helps professionals realize the need to develop leadership, communication, interaction, learning, and growth to foster creative interaction and improve productivity and competitiveness. Recommendation for Researchers: This study enables researchers to more rigorously and creatively conceptualize the conditions and relationships in leadership that facilitate creativity in virtual work. Impact on Society : The findings highlight humanistic values for developing leadership. The study strengthens the view that collective creativity in virtual work cannot emerge without virtual and physical interaction in appropriate spaces and caring for each other. Future Research: Future studies may focus on other fields, industries, networks, roles of materialities, and employees in fostering creativity and on theory development. Longitudinal studies are advisable.

Aim/Purpose: Unified Communications (UC) is touted as a technology that will transform business communication. While positive claims abound, the factors of UC attributable to its success have yet to be identified. By examining how users perceive UC impacts productivity, this study aids organizations in making better decisions regarding investments in and usage of communications technologies. Background: Unified Communications integrates disparate communications and information sharing applications into a single platform. The promise of UC is that it will revolutionize the workplace by providing a more synchronized fit between the way people communicate and the technology they use. Methodology: Through case study research conducted within a large multinational corporation (the Hewlett Packard Company), this study investigated the impact of UC on productivity. Interview narratives were examined using an open coding technique to capture individual perceptions of productivity. Further, to assess the role UC plays in facilitating relationship building and its connection to productivity, participant responses were mapped to the key factors of technology that influence relationships within an organization as identified by Dillon and Montano (2005). Contribution: This research contributes to studies on the impact of UC on productivity in the workplace. Findings UC was found to increase personal productivity, remove communication barriers, and create a more positive work environment. Recommendations for Practitioners : The findings of this study will aid organizations in making investment decisions as they evolve their business communications strategy. Impact on Society: Unified Communications will play an increasingly important role as people adapt to the evolving digital world through which they communicate and collaborate. Future Research: Little research exists that examines the impact of UC within an organization. Additional research investigating the use of UC in a variety of business sectors is needed.

Aim/Purpose: This work aims to present a knowledge modeling technique that supports the representation of the student learning process and that is capable of providing a means for self-assessment and evaluating newly acquired knowledge. The objective is to propose a means to address the pedagogical challenges in m-learning by aiding students’ metacognition through a model of a student with the target domain and pedagogy. Background: This research proposes a framework for social and meta-cognitive support to tackle the challenges raised. Two algorithms are introduced: the meta-cognition algorithm for representing the student’s learning process, which is capable of providing a means for self-assessment, and the social group mapping algorithm for classifying students according to social groups. Methodology : Based on the characteristics of knowledge in an m-learning system, the cognitive knowledge base is proposed for knowledge elicitation and representation. The proposed technique allows a proper categorization of students to support collaborative learning in a social platform by utilizing the strength of m-learning in a social context. The social group mapping and metacognition algorithms are presented. Contribution: The proposed model is envisaged to serve as a guide for developers in implementing suitable m-learning applications. Furthermore, educationists and instructors can devise new pedagogical practices based on the possibilities provided by the proposed m-learning framework. Findings: The effectiveness of any knowledge management system is grounded in the technique used in representing the knowledge. The CKB proposed manipulates knowledge as a dynamic concept network, similar to human knowledge processing, thus, providing a rich semantic capability, which provides various relationships between concepts. Recommendations for Practitioners: Educationist and instructors need to develop new pedagogical practices in line with m-learning. Recommendation for Researchers: The design and implementation of an effective m-learning application are challenging due to the reliance on both pedagogical and technological elements. To tackle this challenge, frameworks which describe the conceptual interaction between the various components of pedagogy and technology need to be proposed. Impact on Society: The creation of an educational platform that provides instant access to relevant knowledge. Future Research: In the future, the proposed framework will be evaluated against some set of criteria for its effectiveness in acquiring and presenting knowledge in a real-life scenario. By analyzing real student interaction in m-learning, the algorithms will be tested to show their applicability in eliciting student metacognition and support for social interactivity.

Aim/Purpose: This research aims to develop an information technology (IT) maturity model for incident management (IM) process that merges the most known IT frameworks’ practices. Our proposal intends to help organizations overcome the current limitations of multiframework implementation by informing organizations about frameworks’ overlap before their implementation. Background: By previously identifying frameworks’ overlaps it will assist organizations during the multi-framework implementation in order to save resources (human and/or financial). Methodology: The research methodology used is design science research (DSR). Plus, the authors applied semi-structured interviews in seven different organizations to demonstrate and evaluate the proposal. Contribution: This research adds a new and innovative artefact to the body of knowledge. Findings: The proposed maturity model is seen by the practitioners as complete and useful. Plus, this research also reinforces the frameworks’ overlap issue and concludes that some organizations are unaware of their actual IM maturity level; some organizations are unaware that they have implemented practices of other frameworks besides the one that was officially adopted. Recommendations for Practitioners: Practitioners may use this maturity model to assess their IM maturity level before multi-framework implementation. Moreover, practitioners are also incentivized to communicate further requirements to academics regarding multi-framework assessment maturity models. Recommendation for Researchers: Researchers may explore and develop multi-frameworks maturity models for the remaining processes of the main IT frameworks. Impact on Society: This research findings and outcomes are a step forward in the development of a unique overlapless maturity model covering the most known IT frameworks in the market thus helping organizations dealing with the increasing frameworks’ complexity and overlap. Future Research: Overlapless maturity models for the remaining IT framework processes should be explored.

Aim/Purpose: This study aimed to examine the relationships among compatibility, relative advantage, complexity, IT Infrastructure, security, top Management Support, financial Support, information Policies, employee engagement, customer pressure, competitive pressure, information integrity, information sharing, attitude toward adopting technology factors, and CRM adoption Background: Customer relationship management (CRM) refers to the use of the process, information, technology, and people for the management of the interactions between the organization and its customers. Therefore, there is a need for SMEs to implement CRM practices in their businesses for competitive advantage. However, in developing nations, the adoption rate of such practices remains low. This low rate may be attributed to the lack of important factors that guide CRM adoption, and as such, the present study attempts to investigate the factors affecting CRM adoption in Palestinian SMEs. This paper used the Diffusion of Innovation Theory (DOI), Resource-Based View (RBV), and Technology, Organization, and Environment Framework (TOE) framework to identify the determinant factors from the technological, organizational, environmental, and information culture perspectives. Methodology: This study uses a quantitative approach to investigate the relationships between the variables. A questionnaire was designed to collect data from 420 SMEs in Palestine. 331respondents completed and returned the survey. The Partial Least Square-Structural Equation Model (PLS-SEM) approach was used to assess both the measurement and structural models. Contribution: This study contributes to both theory and practitioners by providing insights into factors that affect CRM adoption in Palestinian SMEs, which did not explore before. Future research suggestions are also provided. Findings: The results of the study prove that the adoption of CRM depends on compatibility (CMP), security (SEC), top management support (TMS), information policies (INP), financial resources (FR), employee engagement (EEN), competitive pressure (COP), customers pressure (CUP), attitude toward adopting technology (ATA), information integrity (INI), and information sharing (INS). Surprisingly, complexity (CMX), IT infrastructure (ITI), and relative advantage (RLA) do not play any role in CRM adoption in Palestine. Recommendations for Practitioners: This study provides practitioners with the important factors for CRM adoption upon its successful implementation in the context of Palestinian SMEs. Recommendation for Researchers: Our findings may be used to conduct further studies about compatibility, security, top management support, information policies, financial resources, employee engagement, competitive pressure, customers pressure, attitude toward adopting technology, information integrity, information sharing factors, and CRM adoption by using different countries, procedure, and context. Impact on Society: The proposed framework provides insights for SMEs which have significant effects for research and practice to help facilitate the adoption of CRM Future Research: The findings may also be compared to other studies conducted in different contexts and provide deeper insights into the influence of the examined contexts on the employees’ intention toward CRM adoption in banking and universities. It would be fruitful to test whether the results hold true in developed and developing countries.

Aim/Purpose: This study seeks to investigate if participation in business association’s programs through the traditional and new media platforms influences family businesses in South Eastern Nigeria to diversify into similar or different businesses. Background: Before the advances in information and communication technology, businesses were carried on via the traditional media. The application of these advances has changed the way business communications and transactions are conducted globally in both family and non-family businesses. Businesses are adapting to today’s turbulent environment by opening similar or different businesses in the same or different locations that are hinged on the traditional and new media platforms. Nigerians are largely involved in social network through the traditional (face-to-face contact) and new media (e.g., Facebook, WhatsApp, Twitter, YouTube and Instagram). Moreover, in spite of the commonplaceness of family businesses in Nigeria, these businesses still experience weak diversification, bankruptcy and loss of socio-emotional wealth. Consequent upon the foregoing, this paper specifically investigates if involvement in social network via the traditional media (i.e., participation in business association’s meetings, workshops, seminars) and the new media (i.e., participation in the business association’s interactive sessions on trending business issues through the association’s online social platform like WhatsApp, Twitter), influence family businesses in South Eastern Nigeria to diversify into similar or different businesses. Methodology: The study adopted a qualitative methodology. The qualitative data were generated via interview involving 30 purposively selected businesses from South Eastern Nigeria. This comprises 15 family businesses each that have respectively adopted related and unrelated diversification strategies. Two respondents (i.e., the business owner and a top level manager) each were drawn from the selected businesses. In all, 60 respondents were interviewed. Since the unit of analysis is the family business, the interview transcriptions from all the respondents were subjected to thematic content analysis on the basis of the family businesses. Contribution: Active involvement and participation in all the meetings, discussions, workshops and seminars of the social network via the traditional and new media platforms facilitates the adoption of related or unrelated diversification in family businesses. Moreover, the adoption of similar social network platforms like WhatsApp and Twitter in all the relationships among and between employees and managers, and the transactions of the businesses is one of the key factors for achieving successful related or unrelated diversification in family businesses. Findings: In spite of the risky nature of the business environment, the adoption of related diversification strategies is significantly influenced by resources such as business consultancy services garnered through the traditional and new media platforms of the social network. Also, family businesses that are actively involved in a social network where the actors interact through the traditional and new media are influenced by the resources acquired to consider adopting unrelated diversification. These resources include: better understanding of the nature of business challenges, environments and experiences; and different lines of businesses. Thus, the traditional and new media platforms are complementary in their roles. Recommendations for Practitioners: Family business owner-managers could use the findings to develop related or unrelated strategies for diversifying into existing or new markets. This can be through the localization of manufacturing plant, improvement of product packaging, sitting of sales outlet closer to the consumers, introduction of lower prices for products/services, introduction of new and better ways of service delivery, or development of more compelling promotion strategies. Recommendation for Researchers: As a veritable guide, this study could guide future researchers in the formulation of their objectives, selection of instrument for data collection and respondents, and adoption of method of data analysis. Impact on Society: Successful diversification suggests the establishment of new or more businesses. Consequently, these new or more family businesses are expected to translate to more employment opportunities and by extension reduction in unemployment and poverty rates in the society. Future Research: Further studies should be carried out to enhance the development of family businesses, contribute to the existing literature and ensure the generalization of the findings.

Aim/Purpose: The primary purpose of this study is to provide a cost-effective and artificial intelligence enabled security solution for IoT enabled healthcare ecosystem. It helps to implement, improve, and add new attributes to healthcare services. The paper aims to develop a method based on an artificial neural network technique to predict suspicious devices based on bandwidth usage. Background: COVID has made it mandatory to make medical services available online to every remote place. However, services in the healthcare ecosystem require fast, uninterrupted facilities while securing the data flowing through them. The solution in this paper addresses both the security and uninterrupted services issue. This paper proposes a neural network based solution to detect and disable suspicious devices without interrupting critical and life-saving services. Methodology: This paper is an advancement on our previous research, where we performed manual knowledge-based intrusion detection. In this research, all the experiments were executed in the healthcare domain. The mobility pattern of the devices was divided into six parts, and each one is assigned a dedicated slice. The security module regularly monitored all the clients connected to slices, and machine learning was used to detect and disable the problematic or suspicious devices. We have used MATLAB’s neural network to train the dataset and automatically detect and disable suspicious devices. The different network architectures and different training algorithms (Levenberg–Marquardt and Bayesian Framework) in MATLAB software have attempted to achieve more precise values with different properties. Five iterations of training were executed and compared to get the best result of R=99971. We configured the application to handle the four most applicable use cases. We also performed an experimental application simulation for the assessment and validation of predictions. Contribution: This paper provides a security solution for the IoT enabled healthcare system. The architectures discussed suggest an end-to-end solution on the sliced network. Efficient use of artificial neural networks detects and block suspicious devices. Moreover, the solution can be modified, configured and deployed in many other ecosystems like home automation. Findings: This simulation is a subset of the more extensive simulation previously performed on the sliced network to enhance its security. This paper trained the data using a neural network to make the application intelligent and robust. This enhancement helps detect suspicious devices and isolate them before any harm is caused on the network. The solution works both for an intrusion detection and prevention system by detecting and blocking them from using network resources. The result concludes that using multiple hidden layers and a non-linear transfer function, logsig improved the learning and results. Recommendations for Practitioners: Everything from offices, schools, colleges, and e-consultation is currently happening remotely. It has caused extensive pressure on the network where the data flowing through it has increased multifold. Therefore, it becomes our joint responsibility to provide a cost-effective and sustainable security solution for IoT enabled healthcare services. Practitioners can efficiently use this affordable solution compared to the expensive security options available in the commercial market and deploy it over a sliced network. The solution can be implemented by NGOs and federal governments to provide secure and affordable healthcare monitoring services to patients in remote locations. Recommendation for Researchers: Research can take this solution to the next level by integrating artificial intelligence into all the modules. They can augment this solution by making it compatible with the federal government’s data privacy laws. Authentication and encryption modules can be integrated to enhance it further. Impact on Society: COVID has given massive exposure to the healthcare sector since last year. With everything online, data security and privacy is the next most significant concern. This research can be of great support to those working for the security of health care services. This paper provides “Security as a Solution”, which can enhance the security of an otherwise less secure ecosystem. The healthcare use cases discussed in this paper address the most common security issues in the IoT enabled healthcare ecosystem. Future Research: We can enhance this application by including data privacy modules like authentication and authorisation, data encryption and help to abide by the federal privacy laws. In addition, machine learning and artificial intelligence can be extended to other modules of this application. Moreover, this experiment can be easily applicable to many other domains like e-homes, e-offices and many others. For example, e-homes can have devices like kitchen equipment, rooms, dining, cars, bicycles, and smartwatches. Therefore, one can use this application to monitor these devices and detect any suspicious activity.

Aim/Purpose: The aim of this paper is to propose a new information security controls framework for blockchain technology, which is currently absent from the National and International Information Security Standards. Background: Blockchain technology is a secure and relatively new technology of distributed digital ledgers, which is based on inter-linked blocks of transactions, providing great benefits such as decentralization, transparency, immutability, and automation. There is a rapid growth in the adoption of blockchain technology in different solutions and applications and within different industries throughout the world, such as finance, supply chain, digital identity, energy, healthcare, real estate, and the government sector. Methodology: Risk assessment and treatments were performed on five blockchain use cases to determine their associated risks with respect to security controls. Contribution: The significance of the proposed security controls is manifested in complementing the frameworks that were already established by the International and National Information Security Standards in order to keep pace with the emerging blockchain technology and prevent/reduce its associated information security risks. Findings: The analysis results showed that the proposed security controls herein can mitigate relevant information security risks in blockchain-based solutions and applications and, consequently, protect information and assets from unauthorized disclosure, modification, and destruction. Recommendations for Practitioners: The performed risk assessment on the blockchain use cases herein demonstrates that blockchain can involve security risks that require the establishment of certain measures in order to avoid them. As such, practitioners should not blindly assume that through the use of blockchain all security threats are mitigated. Recommendation for Researchers: The results from our study show that some security risks not covered by existing Standards can be mitigated and reduced when applying our proposed security controls. In addition, researchers should further justify the need for such additional controls and encourage the standardization bodies to incorporate them in their future editions. Impact on Society: Similar to any other emerging technology, blockchain has several drawbacks that, in turn, could have negative impacts on society (e.g., individuals, entities and/or countries). This is mainly due to the lack of a solid national and international standards for managing and mitigating risks associated with such technology. Future Research: The majority of the blockchain use cases in this study are publicly published papers. Therefore, one limitation of this study is the lack of technical details about these respective solutions, resulting in the inability to perform a comprehensive risk identification properly. Hence, this area will be expanded upon in our future work. In addition, covering other standardization bodies in the area of distributed ledger in blockchain technology would also prove fruitful, along with respective future design of relevant security architectures.

Aim/Purpose: 5G and IoT are two path-breaking technologies, and they are like wall and climbers, where IoT as a climber is growing tremendously, taking the support of 5G as a wall. The main challenge that emerges here is to secure the ecosystem created by the collaboration of 5G and IoT, which consists of a network, users, endpoints, devices, and data. Other than underlying and hereditary security issues, they bring many Zero-day vulnerabilities, which always pose a risk. This paper proposes a security solution using network slicing, where each slice serves customers with different problems. Background: 5G and IoT are a combination of technology that will enhance the user experience and add many security issues to existing ones like DDoS, DoS. This paper aims to solve some of these problems by using network slicing and implementing an Intrusion Detection System to identify and isolate the compromised resources. Methodology: This paper proposes a 5G-IoT architecture using network slicing. Research here is an advancement to our previous implementation, a Python-based software divided into five different modules. This paper’s amplification includes induction of security using pattern matching intrusion detection methods and conducting tests in five different scenarios, with 1000 up to 5000 devices in different security modes. This enhancement in security helps differentiate and isolate attacks on IoT endpoints, base stations, and slices. Contribution: Network slicing is a known security technique; we have used it as a platform and developed a solution to host IoT devices with peculiar requirements and enhance their security by identifying intruders. This paper gives a different solution for implementing security while using slicing technology. Findings: The study entails and simulates how the IoT ecosystem can be variedly deployed on 5G networks using network slicing for different types of IoT devices and users. Simulation done in this research proves that the suggested architecture can be successfully implemented on IoT users with peculiar requirements in a network slicing environment. Recommendations for Practitioners: Practitioners can implement this solution in any live or production IoT environment to enhance security. This solution helps them get a cost-effective method for deploying IoT devices on a 5G network, which would otherwise have been an expensive technology to implement. Recommendation for Researchers: Researchers can enhance the simulations by amplifying the different types of IoT devices on varied hardware. They can even perform the simulation on a real network to unearth the actual impact. Impact on Society: This research provides an affordable and modest solution for securing the IoT ecosystem on a 5G network using network slicing technology, which will eventually benefit society as an end-user. This research can be of great assistance to all those working towards implementing security in IoT ecosystems. Future Research: All the configuration and slicing resources allocation done in this research was performed manually; it can be automated to improve accuracy and results. Our future direction will include machine learning techniques to make this application and intrusion detection more intelligent and advanced. This simulation can be combined and performed with smart network devices to obtain more varied results. A proof-of-concept system can be implemented on a real 5G network to amplify the concept further.

Aim/Purpose: The aim of this study is to identify Critical Success Factors (CSF) of Business Intelligence (BI) and provide a framework to classify CSF into layers or perspectives using an enterprise architecture approach, then rank CSF within each perspective and evaluate the importance of each perspective at different BI maturity levels as well. Background: Although the implementation of the BI project has a significant impact on creating analytical and competitive capabilities, the lack of evaluation of CSF holistically is still a challenge. Moreover, the BI maturity level of the organization has not been considered in the BI implementation project. Identifying BI critical success factors and their importance can help the project team to move to a higher maturity level in the organization. Methodology: First, a list of distinct CSF is identified through a literature review. Second, a framework is provided for categorizing these CSF using enterprise architecture. Interviewing is the research method used to evaluate the importance of CSF and framework layers with two questionnaires among experts. The first questionnaire was done by Analytical Hierarchy Process (AHP), a quantitative method of decision-making to calculate the weight of the CSF according to the importance of CSF in each of the framework layers. The second one was conducted to evaluate framework layers at different BI maturity levels using a Likert scale. Contribution: This paper contributes to the implementation of BI projects by identifying a comprehensive list of CSF in the form of a holistic multi-layered framework and ranking the importance of CSF and layers at BI maturity levels. Findings: The most important CSF in BI implementation projects include senior management support, process identification, data quality, analytics quality, hardware quality, security standards, scope management, documentation, project team skills, and customer needs transformation, which received the highest scores in framework layers. In addition, it was observed that as the organization moves to higher levels of maturity, the average importance of strategic business and security perspectives or layers increases. But the average importance of data, applications, infrastructure, and network, the project management layers in the proposed framework is the same regardless of the level of business intelligence maturity. Recommendations for Practitioners: The results of this paper can be used by academicians and practitioners to improve BI project implementation through understanding a comprehensive list of CSF and their importance. This awareness causes us to focus on the most important CSF and have better planning to reach higher levels of maturity according to the maturity level of the organization. Future Research: For future research, the interaction of critical success factors of business intelligence and framework layers can be examined with different methods.

Aim/Purpose: The main goal of this systematic literature review was to look for studies that provide information relevant to business intelligence’s (BI) framework development and implementation in the tourism sector. This paper tries to classify the tourism sectors where BI is implemented, group various BI functionalities, and identify common problems encountered by previous research. Background: There has been an increased need for BI implementation to support decision-making in the tourism sector. Tourism stakeholders such as management of destination, accommodation, transportation, and public administration need a guideline to understand functional requirements before implementation. This paper addresses the problem by comprehensively reviewing the functionalities and issues that need to be considered based on previous business intelligence framework development and implementation in tourism sectors. Methodology: We have conducted a systematic literature review using the Preferred Reporting Items for Systematic Reviews and Guidelines for Meta-Analysis (PRISMA) method. The search is conducted using online academic database platforms, resulting in 543 initial articles published from 2002 to 2022. Contribution: The paper could be of interest to relevant stakeholders in the tourism industry because it provides an overview of the capabilities and limitations of business intelligence for tourism. To our knowledge, this is the first study to identify and classify the BI functionalities needed for tourism sectors and implementation issues related to organizations, people, and technologies that need to be considered. Findings: BI functionalities identified in this study include basic functions such as data analysis, reports, dashboards, data visualization, performance metrics, and key performance indicator, and advanced functions such as predictive analytics, trend indicators, strategic planning tools, profitability analysis, benchmarking, budgeting, and forecasting. When implementing BI, the issues that need to be considered include organizational, people and process, and technological issues. Recommendations for Practitioners: As data is a major issue in BI implementation, tourism stakeholders, especially in developing countries, may need to build a tourism data center or centralized coordination regulated by the government. They can implement basic functions first before implementing more advanced features later. Recommendation for Researchers: We recommend further studying the BI implementation barriers by employing a perspective of an adoption framework such as the technology, organization, and environment (TOE) framework. Impact on Society: This research has a potential impact on improving the tourism industry’s performance by providing insight to stakeholders about what is needed to help them make more accurate decisions using business intelligence. Future Research: Future research may involve collaboration between practitioners and academics in developing various BI architectures specific to each tourism industry, such as destination management, hospitality, or transportation.

Aim/Purpose: The aim of the study is to explore factors that affect how healthcare clients in rural areas use infomediaries in maternal mHealth interventions. The study focuses on maternal healthcare clients who do not own mobile phones but use the mHealth intervention. Background: Maternal mHealth interventions in poor-resource settings are bedevilled by inequalities in mobile phone ownership. Clients who do not own mobile phones risk being excluded from benefiting from the interventions. Some maternal mHealth providers facilitate the access of mobile phones for those who do not own them using “infomediaries”. Infomediaries, in this case, refer to individuals who have custody of mobile phones that other potential beneficiaries may use. However, the use of infomediaries to offer access to the “have nots” may be influenced by a number of factors. Methodology: The study uses a case of a maternal mHealth intervention project in Malawi, as well as a qualitative research method and interpretive paradigm. Data was collected using secondary data from the implementing agency, semi-structured interviews, and focus group discussions. Empirical data was collected from maternal healthcare clients who do not own mobile phones and infomediaries. Data were analysed inductively using thematic analysis. Contribution: The study proposed a theoretical framework for studying infomediaries in ICT4D. The study may inform mHealth designers, implementers, and policymakers on how infomediaries could be implemented in a rural setting. Consequently, understanding the factors that affect the use of infomediaries may inform mHealth intervention implementers on how they could overcome the challenges by implementing mHealth interventions that reduce the challenges on the mHealth infomediaries side, and the maternal healthcare clients’ side. Findings: Characteristics of the maternal healthcare client, characteristics of the mHealth infomediary, perceived value of mHealth intervention, and socio-environmental factors affect maternal healthcare clients’ use of mHealth infomediaries. Recommendations for Practitioners: Implementers of interventions ought to manage the use of infomediaries to avoid volunteer fatigue and infomediaries who may not be compatible with the potential users of the intervention. Implementers could leverage traditional systems of identifying and using infomediaries instead of reinventing the wheel. Recommendation for Researchers: This research adopted a single case study to develop the theoretical framework for mHealth infomediary use. We recommend future studies are conducted in order to test and develop this framework further, not only in ICT4D, but also in other areas of application. Impact on Society: People still lack access. The lack of ownership of technology may still exclude them from participating in an information society. The use of infomediaries may help to provide access to technologies to those who do not have them thereby bridging the digital divide gap. Future Research: We propose herein that traditional systems may offer a good starting point for designing a system that would work for communities. We, therefore, recommend that future research may explore these possibilities.

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

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

Aim/Purpose: This study investigates the relationship between dual network embedding, dual entrepreneurial bricolage, and knowledge-creation performance. Background: The importance of new ventures for innovation and economic growth has been fully endorsed. Establishing incubation organizations to help new startups overcome constraints and dilemmas has become the consensus of various countries. In particular, the number of Chinese makerspaces has rapidly increased. Startups in the makerspaces form a loosely coupled dual network to cooperate and share resources, especially knowledge. Methodology: By convenience sampling, 400 startups in the makerspaces in Fujian Province, China were selected for the questionnaire survey study. In total, 307 valid responses were collected, yielding a response rate of 76.8%. The survey data were analyzed for hypothesis testing, using the PL-SEM technique with the AMOS20.0 software. Contribution: At the theoretical level, this research supplements the exploration of the influencing factors of the entrepreneurial bricolage of startups at the network level. It deepens the research on the internal mechanism of the dual network embeddedness affecting the knowledge-creation performance. In practice, it provides a theoretical basis and management inspiration for startups in makerspaces to overcome the inherent disadvantage of being too small and weak to explore innovative paths. Findings: First, relational embedding of startups in makerspaces directly affects knowledge-creation performance. Second, dual entrepreneurial bricolage plays a mediating role in diversity. Selective entrepreneurial bricolage plays a partial mediating role between relationship embedding and knowledge-creation performance. Parallel entrepreneurial bricolage plays a complete intermediary role between structural embedding and knowledge-creation performance. Dual entrepreneurial bricolage plays a complete intermediary role between knowledge embedding and knowledge-creation performance. Recommendations for Practitioners: Enterprises in the makerspaces should make dynamic adjustments to the network embedded state and dual entrepreneurial bricolage to improve knowledge-creation performance. When startups conduct selective entrepreneurship bricolage, they should strengthen relational and knowledge embeddedness to improve their relationship strength and tacit knowledge acquisition. When startups conduct parallel entrepreneurship bricolage, structural and knowledge embedding should be strengthened to improve the position of enterprises in the network to acquire diversified knowledge to explore and discover new business opportunities and project resources. Recommendation for Researchers: The heterogeneity of industries and regions may impact the dual network embedding mechanism of startups. Researchers can choose a wider range of regions and industries for sampling. Impact on Society: This study provides a theoretical basis and management inspiration for startups to overcome the inherent disadvantage of being too small and weak to explore innovative paths. It provides a basis to support startups in unleashing innovation vitality and achieving healthy growth. Future Research: Previous studies have shown that network relationships and bricolage behavior have a certain relationship with the enterprise life cycle. Future research can adopt a longitudinal research design across time points, which will increase the explanatory power of research conclusions.

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 aims to develop a theoretical model based on the SOR (Stimulus – Organism – Response) framework and GETAMEL, which cover environmental, personal, and learning quality aspects to identify factors influencing students’ acceptance of the use of LMS in high schools, especially after COVID-19 pandemic. Background: After the COVID-19 pandemic, many high schools reopened for in-person classes, which led to a decreased reliance on e-learning. The shift from online to traditional face-to-face learning has influenced students’ perceptions of the importance of e-learning in their academic activities. Consequently, high schools are facing the challenge of ensuring that LMS can still be integrated into the teaching-learning process even after the pandemic ends. Therefore, this study proposes a model to investigate the factors that affect students’ actual use of LMS in the high school environment. Methodology: This study used 890 high school students to validate the theoretical model using Structural Equation Modeling (SEM) analysis to deliver direct, indirect, and moderating effect analysis. Contribution: This study combines SOR and acceptance theory to provide a model to explain high school students’ intention to use technology. The involvement of direct, indirect, and moderating effects analysis offers an alternative result and discussion and is considered another contribution of this study from a technical perspective. Findings: The findings show that perceived satisfaction is the most influential factor affecting the use of LMS, followed by perceived usefulness. Meanwhile, from indirect effect analysis, subjective norms and computer self-efficacy were found to indirectly affect actual use through perceived usefulness as a mediator. Content quality was also an indirect predictor of the actual use of LMS through perceived satisfaction. Further, the moderating effect of age influenced perceived satisfaction’s direct effect on actual use. Recommendations for Practitioners: This study provides practical recommendations that can be useful to high schools and other stakeholders in improving the use of LMS in educational environments. Specifically, exploring the implementation of LMS in high schools prior to and following the COVID-19 outbreak can offer valuable insights into the changing educational environment. Recommendation for Researchers: The results of this study present a significant theoretical contribution by employing a comprehensive approach to explain the adoption of LMS among high school students after the COVID-19 pandemic. This contribution extends the GETAMEL framework by incorporating environmental, personal, and learning quality aspects while also analyzing both direct and indirect effects, which have not been previously explored in this context. Impact on Society: This study provides knowledge to high schools for improving the use of LMS in educational environments post-COVID-19, leading to an enhanced teaching-learning process. Future Research: This study, however, is limited to collecting responses exclusively from Indonesian respondents. Therefore, the replication of the finding needs to consider the characteristics and culture similar to Indonesian students, which is regarded as the limitation of this study.

Aim/Purpose: This conceptual review analyzes the designs of global health virtual communities of practice (VCoPs) programming reported in the empirical literature and proposes a new typology of their functioning. The purpose of this review is to provide clarity on VCoP learning stages of (co)evolution and insight into VCoP (re)development efforts to best meet member, organization, and network needs against an ever-evolving landscape of complexity in global health. Background: Since the COVID-19 pandemic, the field of global health has seen an uptick in the use of VCoPs to support continuous learning and improve health outcomes. However, evidence of how different combinations of programmatic designs impact opportunities for learning and development is lacking, and how VCoPs evolve as learning networks has yet to be explored. Methodology: Following an extensive search for literature in six databases, thematic analysis was conducted on 13 articles meeting the inclusion criteria. This led to the development and discussion of a new typology of VCoP phases of learning (co)evolution. Contribution: Knowledge gained from this review and the new categorization of VCoPs can support the functioning and evaluation of global health training programs. It can also provide a foundation for future research on how VCoPs influence the culture of learning organizations and networks. Findings: Synthesis of findings resulted in the categorization of global health VCoPs into five stages (slightly evolving, somewhat revolving, moderately revolving, highly revolving, and coevolving) across four design domains (network development, general member engagement before/after sessions, general member engagement during sessions, and session leadership). All global health VCoPs reviewed showed signs of adaptation and recommended future evolution. Recommendations for Practitioners: VCoP practitioners should pay close attention to how the structured flexibility of partnerships, design, and relationship development/accountability may promote or hinder VcoP’s continued evolution. Practitioners should shift perspective from short to mid- and long-term VCoP planning. Recommendation for Researchers: The new typology can stimulate further research to strengthen the clarity of language and findings related to VCoP functioning. Impact on Society: VCoPs are utilized by academic institutions, the private sector, non-profit organizations, the government, and other entities to fill gaps in adult learning at scale. The contextual implementation of findings from this study may impact VCoP design and drive improvements in opportunities for learning, global health, and well-being. Future Research: Moving forward, future research could explore how VCoP evaluations relate to different stages of learning, consider evaluation stages across the totality of VCoP programming design, and explore how best to capture VCoP (long-term) impact attributed to health outcomes and the culture of learning organizations and networks.

Aim/Purpose: This study aims to develop a solution for personalized tourism recommendations that addresses information overload, data sparsity, and the cold-start problem. It focuses on enabling tourists to choose the most suitable tourism-related facilities, such as restaurants and hotels, that match their individual needs and preferences. Background: The tourism industry is experiencing a significant shift towards digitalization due to the increasing use of online platforms and the abundance of user data. Travelers now heavily rely on online resources to explore destinations and associated options like hotels, restaurants, attractions, transportation, and events. In this dynamic landscape, personalized recommendation systems play a crucial role in enhancing user experience and ensuring customer satisfaction. However, existing recommendation systems encounter major challenges in precisely understanding the complexities of user preferences within the tourism domain. Traditional approaches often rely solely on user ratings, neglecting the complex nature of travel choices. Data sparsity further complicates the issue, as users might have limited interactions with the system or incomplete preference profiles. This sparsity can hinder the effectiveness of these systems, leading to inaccurate or irrelevant recommendations. The cold-start problem presents another challenge, particularly with new users who lack a substantial interaction history within the system, thereby complicating the task of recommending relevant options. These limitations can greatly hinder the performance of recommendation systems and ultimately reduce user satisfaction with the overall experience. Methodology: The proposed User-based Multi-Criteria Trust-aware Collaborative Filtering (UMCTCF) approach exploits two key aspects to enhance both the accuracy and coverage of recommendations within tourism recommender systems: multi-criteria user preferences and implicit trust networks. Multi-criteria ratings capture the various factors that influence user preferences for specific tourism items, such as restaurants or hotels. These factors surpass a simple one-star rating and take into account the complex nature of travel choices. Implicit trust relationships refer to connections between users that are established through shared interests and past interactions without the need for explicit trust declarations. By integrating these elements, UMCTCF aims to provide more accurate and reliable recommendations, especially when data sparsity limits the ability to accurately predict user preferences, particularly for new users. Furthermore, the approach employs a switch hybridization scheme, which combines predictions from different components within UMCTCF. This scheme leads to a more robust recommendation strategy by leveraging diverse sources of information. Extensive experiments were conducted using real-world tourism datasets encompassing restaurants and hotels to evaluate the effectiveness of UMCTCF. The performance of UMCTCF was then compared against baseline methods to assess its prediction accuracy and coverage. Contribution: This study introduces a novel and effective recommendation approach, UMCTCF, which addresses the limitations of existing methods in personalized tourism recommendations by offering several key contributions. First, it transcends simple item preferences by incorporating multi-criteria user preferences. This allows UMCTCF to consider the various factors that users prioritize when making tourism decisions, leading to a more comprehensive understanding of user choices and, ultimately, more accurate recommendations. Second, UMCTCF leverages the collective wisdom of users by incorporating an implicit trust network into the recommendation process. By incorporating these trust relationships into the recommendation process, UMCTCF enhances its effectiveness, particularly in scenarios with data sparsity or new users with limited interaction history. Finally, UMCTCF demonstrates robustness towards data sparsity and the cold-start problem. This resilience in situations with limited data or incomplete user profiles makes UMCTCF particularly suitable for real-world applications in the tourism domain. Findings: The results consistently demonstrated UMCTCF’s superiority in key metrics, effectively addressing the challenges of data sparsity and new users while enhancing both prediction accuracy and coverage. In terms of prediction accuracy, UMCTCF yielded significantly more accurate predictions of user preferences for tourism items compared to baseline methods. Furthermore, UMCTCF achieved superior coverage compared to baseline methods, signifying its ability to recommend a wider range of tourism items, particularly for new users who might have limited interaction history within the system. This increased coverage has the potential to enhance user satisfaction by offering a more diverse and enriching set of recommendations. These findings collectively highlight the effectiveness of UMCTCF in addressing the challenges of personalized tourism recommendations, paving the way for improved user satisfaction and decision-making within the tourism domain. Recommendations for Practitioners: The proposed UMCTCF approach offers a potential opportunity for tourism recommendation systems, enabling practitioners to create solutions that prioritize the needs and preferences of users. By incorporating UMCTCF into online tourism platforms, tourists can utilize its capabilities to make well-informed decisions when selecting tourism-related facilities. Furthermore, UMCTCF’s robust design allows it to function effectively even in scenarios with data sparsity or new users with limited interaction history. This characteristic makes UMCTCF particularly valuable for real-world applications, especially in scenarios where these limitations are common obstacles. Recommendation for Researchers: The success of UMCTCF can open up new avenues in personalized recommendation research. One promising direction lies in exploring the integration of additional contextual information, such as temporal (time-based) or location-based information. By incorporating these elements, the model could be further improved, allowing for even more personalized recommendations. Furthermore, exploring the potential of UMCTCF in domains other than tourism has considerable significance. By exploring its effectiveness in other e-commerce domains, researchers can broaden the impact of UMCTCF and contribute to the advancement of personalized recommendation systems across various industries. Impact on Society: UMCTCF has the potential to make a positive impact on society in various ways. By delivering accurate and diverse recommendations that are tailored to individual user preferences, UMCTCF fosters a more positive and rewarding user experience with tourism recommendation systems. This can lead to increased user engagement with tourism platforms, ultimately enhancing overall satisfaction with travel planning. Furthermore, UMCTCF enables users to make more informed decisions through broader and more accurate recommendations, potentially reducing planning stress and leading to more fulfilling travel experiences. Future Research: Expanding upon the success of UMCTCF, future research activities can explore several promising paths. Enriching UMCTCF with various contextual data, such as spatial or location-based data, to enhance recommendation accuracy and relevance. Leveraging user-generated content, like reviews and social media posts, could provide deeper insights into user preferences and sentiments, improving personalization. Additionally, applying UMCTCF in various e-commerce domains beyond tourism, such as online shopping, entertainment, and healthcare, could yield valuable insights and enhance recommendation systems. Finally, exploring the integration of optimization algorithms could improve both recommendation accuracy and efficiency.

Aim/Purpose: This research aims to develop a smart agricultural knowledge management framework to empower emergent farmers and extension officers (advisors to farmers) in developing countries as part of a smart farming lab (SFL). The framework utilizes knowledge objects (KOs) to capture information and knowledge of different forms, including indigenous knowledge. It builds upon a foundation of established agricultural knowledge management (AKM) models and serves as the cornerstone for an envisioned SFL. This framework facilitates optimal decision support by fostering linkages between these KOs and relevant organizations, knowledge holders, and knowledge seekers within the SFL environment. Background: Emergent farmers and extension officers encounter numerous obstacles in their knowledge operations and decision-making. This includes limited access to agricultural information and difficulties in applying it effectively. Many lack reliable sources of support, and even when information is available, understanding and applying it to specific situations can be challenging. Additionally, extension offices struggle with operational decisions and knowledge management due to agricultural organizations operating isolated in silos, hindering their access to necessary knowledge. This research introduces an SFL with a proposed AKM process model aimed at transforming emergent farmers into smart, innovative entities by addressing these challenges. Methodology: This study is presented as a theory-concept paper and utilizes a literature review to evaluate and synthesize three distinct AKM models using several approaches. The results of the analysis are used to design a new AKM process model. Contribution: This research culminates in a new AKM process framework that incorporates the strengths of various existing AKM models and supports emergent farmers and extension officers to become smart, innovative entities. One main difference between the three models analyzed, and the one proposed in this research, is the deployment and use of knowledge assets in the form of KOs. The proposed framework also incorporates metadata and annotations to enhance knowledge discoverability and enable AI-powered applications to leverage captured knowledge effectively. In practical terms, it contributes by further motivating the use of KOs to enable the transfer and the capturing of organizational knowledge. Findings: A model for an SFL that incorporates the proposed agricultural knowledge management framework is presented. This model is part of a larger knowledge factory (KF). It includes feedback loops, KOs, and mechanisms to facilitate intelligent decision-making. The significance of fostering interconnected communities is emphasized through the creation of linkages. These communities consist of knowledge seekers and bearers, with information disseminated through social media and other communication integration platforms. Recommendations for Practitioners: Practitioners and other scholars should consider implementing the proposed AKM process model as part of a larger SFL to support emergent farmers and extension officers in making operational decisions and applying knowledge management strategies. Recommendation for Researchers: The AKM process model is only presented in conceptual form. Therefore, researchers can practically test and assess the new framework in an agricultural setting. They can also further explore the potential of social media integration platforms to connect knowledge seekers with knowledge holders. Impact on Society: The proposed AKM process model has the potential to support emergent farmers and extension officers in becoming smart, innovative entities, leading to improved agricultural practices and potentially contributing to food security. Future Research: This paper discusses the AKM process model in an agrarian setting, but it can also be applied in other domains, such as education and the healthcare sector. Future research can evaluate the model’s effectiveness and explore and further investigate the semantic web and social media integration.

Aim/Purpose: This paper intends to answer the question regarding the extent to which political postings with value differences/similarities will influence the level of implicit knowledge sharing (KS) among work colleagues in organizations. More specifically, the study assesses contributors’ responses to a workmate’s publicity about politics on social media platforms (SMP) and their eagerness to implement implicit KS to the co-worker. Background: Previously published articles have confirmed an association between publicity about politics and the reactions from workfellows in the organization. Moreover, prior work confirmed that workers’ social media postings about politics may create unfavorable responses, such as being disliked and distrusted by workfellows. This may obstruct the KS because interpersonal relations are among the KS’s essential components. Therefore, it is imperative to assess whether the workfellows’ relationship affected by political publicity would impede the KS in the office. Methodology: Data was gathered using the vignette technique and online survey. A total of 510 online and offline questionnaires were distributed to respondents in Indonesian Halal firms who have implemented knowledge-sharing practices and have been at work for no less than twelve months in the present role. Next, the 317 completed questionnaires were examined with partial least squares structural equation modeling (PLS-SEM). Contribution: Postings about politics on SMP can either facilitate or impede the level of KS in organizations, and this research topic is relatively scarce in the knowledge management discipline. While previously published articles have concentrated on public organizations, this research centers on private firms. Moreover, this work empirically examines private companies in Indonesia, which is also understudied in the existing literature. Findings: The outcomes confirm that perceived political value similarity (PPV) in a co-worker’s social-media publicity has a significant and indirect influence on contributors’ eagerness to perform implicit/tacit KS. Further, colleague likability and trustworthiness significantly influence the level of KS among respondents. As PPV significantly forms colleague likability, likability strongly and positively shapes trustworthiness. Recommendations for Practitioners: The study shows that political publicity significantly affects implicit knowledge sharing (KS). As a result, managers and leaders, particularly those in private firms, are strengthened to instruct their staff about the ramifications of publicity embedded in employees’ SMP postings, particularly about political topics, as it may result in either negative or positive perceptions amongst the staff towards the workmate who posts. Recommendation for Researchers: As this study focuses on examining KS behavior in a large context, i.e., Indonesia Halal firms that dominate the Indonesian economy, and the fact that much polarization research focuses on society at large and less on specific sectors of life, it is important and interesting for researchers to conduct similar studies in a specific workplace as political agreements and disagreements become so important and consequential in everyday lives. Impact on Society: This article makes the implication that a person’s personality can influence how they react to political posts on SMP. It is difficult for the exposers to know the personality of each viewer of publicity in daily life. Workers’ newfound knowledge can motivate them to use SMP responsibly and lessen the probability that they will disclose information that might make their co-workers feel or perceive anything unfavorably. Future Research: There is a need for further studies to examine if the results can be applied to different locations and organizations, as individuals’ behaviors may vary according to the cultures of society and firms. Furthermore, future research can take into account the individual characteristics of workers, such as hospitability, self-confidence, and psychological strength, which may be well-matched with future work models. Future research may potentially employ a qualitative technique to offer deeper insights into the same topic.

Aim/Purpose: This study investigates the drivers of user satisfaction in e-government services and its influence on continued use intention and citizen trust in government. It employs the integration of the Technology Acceptance Model (TAM) and the Information System Success Model (ISSM). Background: Electronic government, transforming citizen-state interactions, has gained momentum worldwide, including in India, where the aim is to leverage technology to improve citizen services, streamline administration, and engage the public. While prior research has explored factors influencing citizen satisfaction with e-government services globally, this area of study has been relatively unexplored in India, particularly in the post-COVID era. Challenges to widespread e-government adoption in India include a large and diverse population, limited digital infrastructure in rural areas, low digital literacy, and weak data protection regulations. Additionally, global declines in citizen trust, attributed to economic concerns, corruption, and information disclosures, further complicate the scenario. This study seeks to investigate the influence of various factors on user satisfaction and continuance usage of e-government services in India. It also aims to understand how these services contribute to building citizens’ trust in government. Methodology: The data were collected by utilizing survey items on drivers of e-government services, user satisfaction, citizen trust, and continuance use intention derived from existing literature on information systems and e-government. Responses from 501 Indian participants, collected using an online questionnaire, were analyzed using PLS-SEM. Contribution: This study makes a dual contribution to the e-government domain. First, it introduces a comprehensive research model that examines factors influencing users’ satisfaction and continuance intention with e-government services. The proposed model integrates the TAM and ISSM. Combining these models allows for a comprehensive examination of e-government satisfaction and continued intention. By analyzing the impact of user satisfaction on continuance intention and citizen trust through an integrated model, researchers and practitioners gain insights into the complex dynamics involved. Second, the study uncovers the effects of residential status on user satisfaction, trust, and continuance intention regarding e-government services. Findings reveal disparities in the influence of system and service quality on user satisfaction across different user segments. Researchers and policymakers should consider these insights when designing e-government services to ensure user satisfaction, continuance intention, and the building of citizen trust. Findings: The findings indicate that the quality of information, service, system, and perceived usefulness play important roles in user satisfaction with e-government services. All hypothesized paths were significant, except for perceived ease of use. Furthermore, the study highlights that user satisfaction significantly impacts citizen trust and continuance use intention. Recommendations for Practitioners: The findings suggest that government authorities should focus on delivering accurate, comprehensive, and timely information in a secure, glitch-free, and user-friendly digital environment. Implementing an interactive and accessible interface, ensuring compatibility across devices, and implementing swift query resolution mechanisms collectively contribute to improving users’ satisfaction. Conducting awareness and training initiatives, providing 24×7 access to online tutorials, helpdesks, technical support, clear FAQs, and integrating AI-driven customer service support can further ensure a seamless user experience. Government institutions should leverage social influence, community engagement, and social media campaigns to enhance user trust. Promotional campaigns, incentive programs, endorsements, and user testimonials should be used to improve users’ satisfaction and continuance intention. Recommendation for Researchers: An integrated model combining TAM and ISSM offers a robust approach for thoroughly analyzing the diverse factors influencing user satisfaction and continuance intention in the evolving digitalization landscape of e-government services. This expansion, aligning with ISSM’s perspective, enhances the literature by demonstrating how user satisfaction impacts continuance usage intention and citizen trust in e-government services in India and other emerging economies. Impact on Society: Examining the factors influencing user satisfaction and continuance intention in e-government services and their subsequent impact on citizen trust carries significant societal implications. The findings can contribute to the establishment of transparent and accountable governance practices, fostering a stronger connection between governments and their citizens. Future Research: There are several promising avenues to explore to enhance future research. Expanding the scope by incorporating a larger sample size could enable a more thorough analysis. Alternatively, delving into the performance of specific e-government services would offer greater precision, considering that this study treats e-government services generically. Additionally, incorporating in-depth interviews and longitudinal studies would yield a more comprehensive understanding of the dynamic evolution of digitalization.

In this paper, two hypothetical frameworks are proposed through the application of quality function deployment (QFD) to integrate the current institutional level and program level student learning focus areas with the relevant institutional and program specific stakeholder expectations. A generic skillset proficiency expected of all the graduating students at the institutional level by the stakeholders is considered in the first QFD application example and a program specific knowledge proficiency expected at the program level by the stakeholders is considered in the second QFD application example. Operations management major/option is considered for illustration purposes at the program level. In addition, an assurance of learning based approach rooted in continuous improvement philosophy is proposed to align the stakeholder expectations with the relevant student learning outcomes at different learning tiers.

Scientific experiments, simulations, and modern applications generate large amounts of data. Analysing resources required to process such big datasets is essential to identify application running costs for cloud or in-house deployments. Researchers have proposed keeping data in raw formats to avoid upfront utilisation of resources. However, it poses reparsing issues for frequently accessed data. The paper discusses detailed comparative analysis of resources required by in-situ engines and traditional database management systems to process a real-world scientific dataset. A resource monitoring framework has been developed and incorporated into the raw data query processing framework to achieve this goal. The work identified different query types best suited to a given data processing tool in terms of data to result time and resource requirements. The analysis of resource utilisation patterns has led to the development of query complexity aware (QCA) and resource utilisation aware (RUA) data partitioning techniques to process big raw data efficiently. Resource utilisation data have been analysed to estimate the data processing capacity of a given machine.

Students of Ethiopian origin belong to one of the weakest sectors in the Jewish population of Israel. During their studies they have to deal with social alienation, cultural gaps, economic hardships, and racial stereotypes which reduce their chances to successfully complete their academic degree. In this respect, the present research asks whether online social media could provide those youngsters with tools and resources for their better social integration and adaptation to the academic life. For this purpose, the study was conducted in one of Israel’s largest academic colleges while adopting a design-based research approach, which advanced gradually on a continuum between ‘ambient’ and ‘designed’ technology-enhanced learning communities. The interventions applied for this study aimed at examining how they may encourage students of Ethiopian origin to expand their activities in the online social learning groups. The findings indicate that the main pattern of students of Ethiopian origin online participation was peripheral and limited to viewing only. Nevertheless, the level of their online activity has been improved after a series of two interventions, which also led to a slight improvement in indicators of their social integration and in a change in their usage of online learning groups from social to academic uses.

In today’s job market, computer skills are part of the prerequisites for many jobs. In this paper, we report on a study of readiness to work with computers (the dependent variable) among unemployed women (N=54) after participating in a unique, web-supported training focused on computer skills and empowerment. Overall, the level of participants’ readiness to work with computers was much higher at the end of the course than it was at its begin-ning. During the analysis, we explored associations between this variable and variables from four categories: log-based (describing the online activity); computer literacy and experience; job-seeking motivation and practice; and training satisfaction. Only two variables were associated with the dependent variable: knowledge post-test duration and satisfaction with content. After building a prediction model for the dependent variable, another log-based variable was highlighted: total number of actions in the course website along the course. Overall, our analyses shed light on the predominance of log-based variables over variables from other categories. These findings might hint at the need of developing new assessment tools for learners and trainees that take into consideration human-computer interaction when measuring self-efficacy variables.

Aim/Purpose: The present study aims to describe existing peer-to-peer, social network-based sharing practices among adult students in teacher colleges. Background: Ubiquitous social network sites open up a wide array of possibilities for peer-to-peer information and knowledge sharing. College instructors are often unaware of such practices that happen behind the scenes. Methodology: An interpretative, qualitative research methodology was used. Thirty-seven Israeli students at a teacher college in Israel participated in either focus group discussions of (N = 29) or in-depth interviews (N = 8). Contribution: Whereas knowledge sharing has been a main focus of research in organizational and information sciences, its relevance to educational settings has thus far been underscored. Recent research shows that peer–to-peer knowledge sharing is wide-spread among teenage students. The current study extends that work to an adult student population. Findings: The findings show that knowledge sharing of this type is a common and even central feature of students’ college life and study behavior. It takes place through a variety of small and larger social network-based peer groups of different formations, including mostly college students but at time also practicing, experienced teachers. Sharing groups are formed on the spot for short term purposes or are stable, continuous over longer time periods. The contents shared are predominantly lesson summaries, material for exams, reading summaries, and lesson plans. They are used immediately or stored for future use, as students have access to vast data bases of stored materials that have been compiled throughout the years by students of previous cohorts. Teacher students mentioned a range of reasons for sharing, and overall regard it very positive. However, some downsides were also acknowledged (i.e., superficial learning, exclusion, attentional overload, and interruptions). Recommendations for Practitioners: College faculty and teaching staff should be cognizant and informed about these widespread peer-based knowledge sharing practices and consider whether perhaps changes in teaching formats and task assignments are required as a result. Future Research: Future research should extend this work to other higher education settings, cultures and countries, and should map the perceptions of higher education teaching staff about peer-to-peer, online knowledge sharing.