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.

Efficient knowledge and information management is essential for companies to prosper in the rapidly changing global environment. This article presents challenges of a large Finnish multinational company relating to their current knowledge and information management practices and systems. The focus is on New Product Introduction (NPI) process. The study is based on interviews and facilitated workshops in the Research and Development (R&D) and Production departments. Furthermore, the identified challenges are reflected to the findings presented in knowledge and information management literature. The results gained from the company case study were well in line with the findings in the literature. Three main topics, which can be generalized to cause challenges for knowledge and information management in most companies, were recognized: 1) Issues related to human behavior, individual characteristics and capabilities, different backgrounds, and professional vocabulary; 2) Codifying tacit knowledge into explicit information, which can be saved to company information system; 3) Lack of interoperability between different information systems. The study provides the management of the case company, and other similar organizations, focus points while seeking for better knowledge and information management. From a scientific perspective, the main contribution of this article is to give practical examples of how the theoretical findings presented in literature manifest themselves in real industrial practices.

Background/Aim/Purpose: A commonly implemented software process improvement framework is the capability maturity model integrated (CMMI). Existing literature indicates higher levels of CMMI maturity could result in a loss of agility due to its organizational focus. To maintain agility, research has focussed attention on agile maturity models. The objective of this paper is to find the common research themes and conclusions in agile maturity model research. Methodology: This research adopts a systematic approach to agile maturity model research, using Google Scholar, Science Direct, and IEEE Xplore as sources. In total 531 articles were initially found matching the search criteria, which was filtered to 39 articles by applying specific exclusion criteria. Contribution:: The article highlights the trends in agile maturity model research, specifically bringing to light the lack of research providing validation of such models. Findings: Two major themes emerge, being the coexistence of agile and CMMI and the development of agile principle based maturity models. The research trend indicates an increase in agile maturity model articles, particularly in the latter half of the last decade, with concentrations of research coinciding with version updates of CMMI. While there is general consensus around higher CMMI maturity levels being incompatible with true agility, there is evidence of the two coexisting when agile is introduced into already highly matured environments. Future Research: Future research direction for this topic should include how to attain higher levels of CMMI maturity using only agile methods, how governance is addressed in agile environments, and whether existing agile maturity models relate to improved project success.

Aim/Purpose: Violations of Information Systems (IS) security policies continue to generate great anxiety amongst many organizations that use information systems, partly because these violations are carried out by internal employees. This article addresses IS security policy violations in organizational settings, and conceptualizes and problematizes IS security violations by employees of organizations from a paradox perspective. Background: The paradox is that internal employees are increasingly being perceived as more of a threat to the security of organizational systems than outsiders. The notion of paradox is exemplified in four organizational contexts of belonging paradox, learning paradox, organizing paradox and performing paradox. Methodology : A qualitative conceptual framework exemplifying how IS security violations occur as paradoxes in context to these four areas is presented at the end of this article. Contribution: The article contributes to IS security management practice and suggests how IS security managers should be positioned to understand violations in light of this paradox perspective. Findings: The employee generally in the process of carrying out ordinary activities using computing technology exemplifies unique tensions (or paradoxes in belonging, learning, organizing and performing) and these tensions would generally tend to lead to policy violations when an imbalance occurs. Recommendations for Practitioners: IS security managers must be sensitive to employees tensions. Future Research: A quantitative study, where statistical analysis could be applied to generalize findings, could be useful.

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: Despite the fact that the plethora of studies demonstrate the positive impact of information technology (IT) capabilities on SMEs performance, the understanding of underlying mechanisms through which IT capabilities affect the firm performance is not yet clear. This study fills these gaps by explaining the roles of absorptive capacity and corporate entrepreneurship. The study also elaborates the effect of IT capability dimensions (IT integration and IT alignment) upon the SMEs performance outcomes through the mediating sequential process of absorptive capacity and corporate entrepreneurship. Methodology: This study empirically tests a theoretical model based on the Dynamic Capability View (DCV), by using the partial least square (PLS) technique with a sample of 489 manufacturing SMEs in Pakistan. A survey is employed for the data collection by following the cluster sampling approach. Contribution: This research contributes to the literature of IT by bifurcating the IT capability into two dimensions, IT integration and IT alignment, which allows us to distinguish between different sources of IT capabilities. Additionally, our findings shed the light on the dynamic capability view by theoretically and empirically demonstrating how absorptive capacity and corporate entrepreneurship sequentially affect the firms' performance outcomes. At last, this study contributes to the literature of SMEs by measuring the two levels of performance: innovation performance and firm performance. Findings: The results of the analysis show that the absorptive capacity and the corporate entrepreneurship significantly mediate the relationship between both dimensions of IT capability and performance outcomes.

Aim/Purpose: This paper examines the behavior of financial firm employees with regard to information security procedures instituted within their organization. Furthermore, the effect of information security awareness and its importance within a firm is explored. Background: The study focuses on employees’ attitude toward compliance with information security policies (ISP), combined with various norms and personal abilities. Methodology: A self-reported questionnaire was distributed among 202 employees of a large financial Corporation Contribution: As far as we know, this is the first paper to thoroughly explore employees’ awareness of information system procedures, among financial organizations in Israel, and also the first to develop operative recommendations for these organizations aimed at increasing ISP compliance behavior. The main contribution of this study is that it investigates compliance with information security practices among employees of a defined financial corporation operating under rigid regulatory governance, confidentiality and privacy of data, and stringent requirements for compliance with information security procedures. Findings: Our results indicate that employees’ attitudes, normative beliefs and personal capabilities to comply with firm’s ISP, have positive effects on the firm’s ISP compliance. Also, employees’ general awareness of IS, as well as awareness to ISP within the firm, positively affect employees’ ISP compliance. Recommendations for Practitioners: This study can help information security managers identify the motivating factors for employee behavior to maintain information security procedures, properly channel information security resources, and manage appropriate information security behavior. Recommendation for Researchers: Researchers can see that corporate rewards and sanctions have significant effects on employee security behavior, but other motivational factors also reinforce the ISP’s compliance behavior. Distinguishing between types of corporations and organizations is essential to understanding employee compliance with information security procedures. Impact on Society: This study offers another level of understanding of employee behavior with regard to information security in organizations and comprises a significant contribution to the growing knowledge in this area. The research results form an important basis for IS policymakers, culture designers, managers, and those directly responsible for IS in the organization. Future Research: Future work should sample employees from another type of corporation from other fields and should apply qualitative analysis to explore other aspects of behavioral patterns related to the subject matter.

Aim/Purpose: This study aimed to investigate the impact of innovation management and learning orientation as the mechanisms playing the role of an intermediate relationship between entrepreneurial leadership and organisational performance of small and medium enterprises (SMEs) in Kuwait. Background: SMEs are currently among the principal economic instruments in most industrialised and developing countries. The contribution of SMEs can be viewed from various perspectives primarily related to the crucial role they play in developing entrepreneurial activities, employment generation, and improving innovativeness. Developing countries, including Kuwait and other countries, in the Gulf Cooperation Council (GCC), have recognised the key role played by SMEs as a strong pillar of growth. Consequently, many governments have formulated policies and programmes to facilitate the growth and success of SMEs. Unfortunately, the organisational performance of SMEs in developing countries, particularly in Kuwait, remains below expectations. The lagged growth could be due to a lack of good managerial practices and increasing competition that negatively impact their performance. Numerous researchers discovered the positive effect of entrepreneurial leadership on SMEs’ performance. However, a lack of clarity remains regarding the direct impact of entrepreneurial leadership on SMEs’ performance, especially in developing countries. Therefore, the nexus between entrepreneurial leadership and organisational performance is still indecisive and requires further studies. Methodology: This study adopted a quantitative approach based on a cross-sectional survey and descriptive design to gather data within a specific period. The data were collected by distributing a survey questionnaire to Kuwaiti SMEs’ owners and Chief Executive Officers (CEOs) via online and on-hand instruments. A total of 384 useable questionnaires were obtained. Moreover, the partial least square-structural equation modelling (PLS-SEM) analysis was performed to test the hypotheses. Contribution: The current study contributed to the existing literature by developing a moderated mediation model integrating entrepreneurial leadership, innovation management, and learning orientation. The study also investigated their effect on the organisational performance of SMEs. The study findings also bridged the existing significant literature gap regarding the role of these variables on SMEs’ performance in developing countries, particularly in Kuwait, due to the dearth of studies linking these variables in this context. Furthermore, this study empirically confirmed the significant effect of innovation management and learning orientation as intermediate variables in strengthening the relationship between entrepreneurial leadership and organisational performance in the settings of Kuwait SMEs, which has not been verified previously. Findings: The study findings showed the beneficial and significant impact of entrepreneurial leadership and innovation management on SME’s organisational performance. The relationship between entrepreneurial leadership and SMEs’ organisational performance is fundamentally mediated by innovation management and moderated by learning orientation. Recommendations for Practitioners: The present study provides valuable insights and information regarding the factors considered by the government, policymakers, SMEs’ stakeholders, and other authorities in the effort to increase the organisational performance level and facilitate the growth of SMEs in Kuwait. SMEs’ owners or CEOs should improve their awareness and knowledge of the importance of entrepreneurial leadership, innovation management, and learning orientation. These variables will have beneficial effects on the performance and assets to achieve success and sustainability if adopted and managed systematically. This study also recommends that SMEs’ entrepreneurs and top management should facilitate supportive culture by creating and maintaining an organisational climate and structure that encourages learning behaviour and innovation mindset among individuals. The initiative will motivate them towards acquiring, sharing, and utilising knowledge and increasing their ability to manage innovation systemically in all production processes to adapt to new technologies, practices, methods, and different circumstances. Recommendation for Researchers: The study findings highlighted the mediating effect of innovation management on the relationship between entrepreneurial leadership (the independent variable) and SMEs’ organisational performance (the dependent variable) and the moderating effect of learning orientation in the same nexus. These relationships were not extensively addressed in SMEs of developing countries and require further validation. Impact on Society: This study aims to influence the management strategies and practices adopted by entrepreneurs and policymakers who work in SMEs in developing countries. The effect will be reflected in the development of their firms and the national economy in general. Future Research: Future research should investigate the conceptual research framework against the backdrop of other developing economies and in other business settings to generalise the results. Future investigation should seek to establish the effect of entrepreneurial leadership style on other mechanisms, such as knowledge management processes, which could function with entrepreneurial leadership to improve SMEs’ performance efficiently. In addition, future studies may include middle and lower-level managers and employees, leading to more positive outcomes.

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: The need to explore leaders’ role as a mediating factor between knowledge sharing and firms’ competitiveness was the focus of this paper. Further, gaps related to knowledge sharing influence on firms’ competitiveness from an emerging economy perspective was a major driver of this study. Background: The relevance of knowledge sharing is today crucial for firms that seek to harness internal resource innovation towards ensuring increased competitiveness. The link between the actions of leaders and outcomes from sharing knowledge towards increased competitiveness would further advance theory on knowledge sharing and provide managerial implication that is instrumental for an improved organisational outcome. Methodology: The study sample was 282 participants and Partial least square structural equation model was used for the analysis of the data obtained through a questionnaire survey with the aid of SmartPLSv3.9. Contribution: The study contributes to knowledge management literature through advancing leadership as a mediating factor that accounts for the link between knowledge sharing and firms’ competitiveness, most especially from an emerging economy perspective. Findings: Knowledge sharing was found to have a positive effect on firms’ competitiveness. The study found that leadership behaviour mediates the relationship between knowledge sharing and a firm’s competitiveness. Recommendations for Practitioners: The study recommends that, when supported with the right attitude from leaders in the organisation, knowledge sharing will be beneficial towards the firm gaining competitiveness most especially. Future Research: Future studies should be carried out in other sectors aside from the manufacturing sector using the same measures used to measure knowledge sharing. Also, a comparative analysis of knowledge sharing and firms’ competitiveness using leaders’ behaviour as a mediator should be researched in other developing economies.

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: Acquisitions play a pivotal role in the growth strategy of a firm. Extensive resources and time are dedicated by a firm toward the identification of prospective acquisition candidates. The Indian manufacturing sector is currently experiencing significant growth, organically and inorganically, through acquisitions. The principal aim of this study is to explore models that can predict acquisitions and compare their performance in the Indian manufacturing sector. Background: Mergers and Acquisitions (M&A) have been integral to a firm’s growth strategy. Over the years, academic research has investigated multiple models for predicting acquisitions. In the context of the Indian manufacturing industry, the research is limited to prediction models. This research paper explores three models, namely Logistic Regression, Decision Tree, and Multilayer Perceptron, to predict acquisitions. Methodology: The methodology includes defining the accounting variables to be used in the model which have been selected based on strong theoretical foundations. The Indian manufacturing industry was selected as the focus, specifically, data for firms listed in the Bombay Stock Exchange (BSE) between 2010 and 2022 from the Prowess database. There were multiple techniques, such as data transformation and data scrubbing, that were used to mitigate bias and enhance the data reliability. The dataset was split into 70% training and 30% test data. The performance of the three models was compared using standard metrics. Contribution: The research contributes to the existing body of knowledge in multiple dimensions. First, a prediction model customized to the Indian manufacturing sector has been developed. Second, there are accounting variables identified specific to the Indian manufacturing sector. Third, the paper contributes to prediction modeling in the Indian manufacturing sector where there is limited research. Findings: The study found significant supporting evidence for four of the proposed hypotheses indicating that accounting variables can be used to predict acquisitions. It has been ascertained that statistically significant variables influence acquisition likelihood: Quick Ratio, Equity Turnover, Pretax Margin, and Total Sales. These variables are intrinsically linked with the theories of liquidity, growth-resource mismatch, profitability, and firm size. Furthermore, comparing performance metrics reveals that the Decision Tree model exhibits the highest accuracy rate of 62.3%, specificity rate of 66.4%, and the lowest false positive ratio of 33.6%. In contrast, the Multilayer Perceptron model exhibits the highest precision rate of 61.4% and recall rate of 64.3%. Recommendations for Practitioners: The study findings can help practitioners build custom prediction models for their firms. The model can be developed as a live reference model, which is continually updated based on a firm’s results. In addition, there is an opportunity for industry practitioners to establish a benchmark score that provides a reference for acquisitions. Recommendation for Researchers: Researchers can expand the scope of research by including additional classification modeling techniques. The data quality can be enhanced by cross-validation with other databases. Textual commentary about the target firms, including management and analyst quotes, provides additional insight that can enhance the predictive power of the models. Impact on Society: The research provides insights into leveraging emerging technologies to predict acquisitions. The theoretical basis and modeling attributes provide a foundation that can be further expanded to suit specific industries and firms. Future Research: There are opportunities to expand the scope of research in various dimensions by comparing acquisition prediction models across industries and cross-border and domestic acquisitions. Additionally, it is plausible to explore further research by incorporating non-financial data, such as management commentary, to augment the acquisition prediction model.

Aim/Purpose: This study analyzes health professionals’ information security behavior (ISB) as health information system (HIS) users concerning associated information security controls and risks established in a public hospital. This work measures ISB using a complete measuring scale and explains the relevant influential factors from the perspectives of Protection Motivation Theory (PMT) and General Deterrence Theory (GDT) Background: Internal users are the primary source of security concerns in hospitals, with malware and social engineering becoming common attack vectors in the health industry. This study focuses on HIS user behavior in developing countries with limited information security policies and resources. Methodology: The research was carried out in three stages. First, a semi-structured interview was conducted with three hospital administrators in charge of HIS implementation to investigate information security controls and threats. Second, a survey of 144 HIS users to determine ISB based on hospital security risk. Third, a semi-structured interview was conducted with 11 HIS users to discuss the elements influencing behavior and current information security implementation. Contribution: This study contributes to ISB practices in hospitals. It discusses how HIS managers could build information security programs to enhance health professionals’ behavior by considering PMT and GDT elements. Findings: According to the findings of this study, the hospital has implemented particular information security management system (ISMS) controls based on international standards, but there is still room for improvement. Insiders are the most prevalent information security dangers discovered, with certain working practices requiring HIS users to disclose passwords with others. The top three most common ISBs HIS users practice include appropriately disposing of printouts, validating link sources, and using a password to unlock the device. Meanwhile, the top three least commonly seen ISBs include transferring sensitive information online, leaving a password in an unsupervised area, and revealing sensitive information via social media. Recommendations for Practitioners: Hospital managers should create work practices that align with information security requirements. HIS managers should provide incentives to improve workers’ perceptions of the benefit of robust information security measures. Recommendation for Researchers: This study suggests more research into the components that influence ISB utilizing diverse theoretical foundations such as Regulatory Focus Theory to compare preventive and promotion motivation to enhance ISB. Impact on Society: This study can potentially improve information security in the healthcare industry, which has substantial risks to human life but still lags behind other vital sector implementations. Future Research: Future research could look into the best content and format for an information security education and training program to promote the behaviors of healthcare professionals that need to be improved based on this ISB measurement and other influential factors.

Aim/Purpose: The main aim of this study was to investigate the impact of knowledge-based Human Resources Management (HRM) practices on inbound and outbound open innovation in Jordanian small and medium enterprises (SMEs). Background: SMEs in Jordan lack tangible resources. This insufficiency can be remedied by using knowledge as a resource. According to the Knowledge-Based View (KBV) theory, which posits knowledge as the most valuable resource, SMEs can achieve open innovation by implementing knowledge-based HRM practices that enhance the utilization of knowledge and yield competitiveness. Methodology: This study adopted the quantitative method employing descriptive and exploratory approaches. A total of 500 Jordanian manufacturing SMEs were selected from 2,310 manufacturing SMEs registered lists, according to the Jordan Social Security, by using random sampling. The study’s instrument was a questionnaire that was applied to these SMEs. There were 335 responses that were deemed useful for analysis after filtering out the replies with missing values; this corresponded to a response rate of 67%. The paper utilized structural equation modeling and cross-sectional design to test hypotheses in the proposed research model. Contribution: This study advocates the assumption of the role of KBV in improving innovation practices. This study contributes to the existing strategic HRM research by extending the understanding of knowledge-based HRM practices in the context of SMEs. Thus, this study contributes to the understanding of innovation management by demonstrating the role of knowledge-based HRM practices in boosting inbound and outbound OI practices, thereby enhancing innovation as an essential component of firm competitiveness. Findings: The findings revealed the positive impact of four knowledge-based HRM practices on inbound and outbound open innovation in Jordanian manufacturing SMEs. These practices were knowledge-based recruitment and selection, knowledge-based training and development, knowledge-based compensation and reward, as well as knowledge-based performance assessment. Recommendations for Practitioners: This study is expected to help the stakeholders of SMEs to re-shape the traditional HRM practices into knowledge-based practices which improve managerial skills, innovation practices, and the level of the firm’s competitiveness. Recommendation for Researchers: This study serves as a significant contribution to the research field of innovation practices by building a new association between knowledge-based HRM practices and inbound and outbound open innovation. Impact on Society: The study emphasizes the vital role of knowledge-based HRM practices in enhancing the knowledge and social skills of the human capital in SMEs in Jordan, thus improving the country’s social and economic development. Future Research: Future research could build on this study to include service SMEs. It could also employ a longitudinal study over the long run which would allow for a deeper analysis of the relationships of causality, offering a more comprehensive view of the effect of knowledge-based HRM on open innovation. Furthermore, future research could examine the sample of investigation before and after implementing the knowledge-based HRM practices to provide stronger evidence of their influence on inbound and outbound innovation.

Aim/Purpose: The UAE and other Middle Eastern countries suffer from various cybersecurity vulnerabilities that are widespread and go undetected. Still, many UAE government organizations rely on human-centric approaches to combat the growing cybersecurity threats. These approaches are ineffective due to the rapid increase in the amount of data in cyberspace, hence necessitating the employment of intelligent technologies such as AI cybersecurity systems. In this regard, this study investigates factors influencing users’ intention to adopt AI-based cybersecurity systems in the UAE. Background: Even though UAE is ranked among the top countries in embracing emerging technologies such as digital identity, robotic process automation (RPA), intelligent automation, and blockchain technologies, among others, it has experienced sluggish adoption of AI cybersecurity systems. This selectiveness in adopting technology begs the question of what factors could make the UAE embrace or accept new technologies, including AI-based cybersecurity systems. One of the probable reasons for the slow adoption and use of AI in cybersecurity systems in UAE organizations is the employee’s perception and attitudes towards such intelligent technologies. Methodology: The study utilized a quantitative approach whereby web-based questionnaires were used to collect data from 370 participants working in UAE government organizations considering or intending to adopt AI-based cybersecurity systems. The data was analyzed using the PLS-SEM approach. Contribution: The study is based on the Protection Motivation Theory (PMT) framework, widely used in information security research. However, it extends this model by including two more variables, job insecurity and resistance to change, to enhance its predictive/exploratory power. Thus, this research improves PMT and contributes to the body of knowledge on technology acceptance, especially in intelligent cybersecurity technology. Findings: This paper’s findings provide the basis from which further studies can be conducted while at the same time offering critical insights into the measures that can boost the acceptability and use of cybersecurity systems in the UAE. All the hypotheses were accepted. The relationship between the six constructs (perceived vulnerability (PV), perceived severity (PS), perceived response efficacy (PRE), perceived self-efficacy (PSE), job insecurity (JI), and resistance to change (RC)) and the intention to adopt AI cybersecurity systems in the UAE was found to be statistically significant. This paper’s findings provide the basis from which further studies can be conducted while at the same time offering critical insights into the measures that can boost the acceptability and use of cybersecurity systems in the UAE. Recommendations for Practitioners: All practitioners must be able to take steps and strategies that focus on factors that have a significant impact on increasing usage intentions. PSE and PRE were found to be positively related to the intention to adopt AI-based cybersecurity systems, suggesting the need for practitioners to focus on them. The government can enact legislation that emphasizes the simplicity and awareness of the benefits of cybersecurity systems in organizations. Recommendation for Researchers: Further research is needed to include other variables such as facilitating conditions, AI knowledge, social influence, and effort efficacy as well as other frameworks such as UTAUT, to better explain individuals’ behavioral intentions to use cybersecurity systems in the UAE. Impact on Society: This study can help all stakeholders understand what factors can increase users’ interest in investing in the applications that are embedded with security. As a result, they have an impact on economic recovery following the COVID-19 pandemic. Future Research: Future research is expected to investigate additional factors that can influence individuals’ behavioral intention to use cybersecurity systems such as facilitating conditions, AI knowledge, social influence, effort efficacy, as well other variables from UTAUT. International research across nations is also required to build a larger sample size to examine the behavior of users.

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: In this study, the research proposes and experiments with a new model of collecting, storing, and analyzing big data on customer feedback in the tourism industry. The research focused on the Vietnam market. Background: Big Data describes large databases that have been “silently” built by businesses, which include product information, customer information, customer feedback, etc. This information is valuable, and the volume increases rapidly over time, but businesses often pay little attention or store it discretely, not centrally, thereby wasting an extremely large resource and partly causing limitations for business analysis as well as data. Methodology: The study conducted an experiment by collecting customer feedback data in the field of tourism, especially tourism in Vietnam, from 2007 to 2022. After that, the research proceeded to store and mine latent topics based on the data collected using the Topic Model. The study applied cloud computing technology to build a collection and storage model to solve difficulties, including scalability, system stability, and system cost optimization, as well as ease of access to technology. Contribution: The research has four main contributions: (1) Building a model for Big Data collection, storage, and analysis; (2) Experimenting with the solution by collecting customer feedback data from huge platforms such as Booking.com, Agoda.com, and Phuot.vn based on cloud computing, focusing mainly on tourism Vietnam; (3) A Data Lake that stores customer feedback and discussion in the field of tourism was built, supporting researchers in the field of natural language processing; (4) Experimental research on the latent topic mining model from the collected Big Data based on the topic model. Findings: Experimental results show that the Data Lake has helped users easily extract information, thereby supporting administrators in making quick and timely decisions. Next, PySpark big data processing technology and cloud computing help speed up processing, save costs, and make model building easier when moving to SaaS. Finally, the topic model helps identify customer discussion trends and identify latent topics that customers are interested in so business owners have a better picture of their potential customers and business. Recommendations for Practitioners: Empirical results show that facilities are the factor that customers in the Vietnamese market complain about the most in the tourism/hospitality sector. This information also recommends that practitioners reduce their expectations about facilities because the overall level of physical facilities in the Vietnamese market is still weak and cannot be compared with other countries in the world. However, this is also information to support administrators in planning to upgrade facilities in the long term. Recommendation for Researchers: The value of Data Lake has been proven by research. The study also formed a model for big data collection, storage, and analysis. Researchers can use the same model for other fields or use the model and algorithm proposed by this study to collect and store big data in other platforms and areas. Impact on Society: Collecting, storing, and analyzing big data in the tourism sector helps government strategists to identify tourism trends and communication crises. Based on that information, government managers will be able to make decisions and strategies to develop regional tourism, propose price levels, and support innovative programs. That is the great social value that this research brings. Future Research: With each different platform or website, the study had to build a query scenario and choose a different technology approach, which limits the ability of the solution’s scalability to multiple platforms. Research will continue to build and standardize query scenarios and processing technologies to make scalability to other platforms easier.

Aim/Purpose: Using the Agile Adoption Framework (AAF), this study aims to examine the agile potential of software development companies in Nepal based on their agile maturity level. In addition, this study also examines the impact of various basic agile practices in determining the maturity level of the agile processes being implemented in the software industry of Nepal. Background: Even if most organizations in the software sector utilize agile development strategies, it is essential to evaluate their performance. Nepal’s software industry did not adopt agile techniques till 2014. The Nepalese industry must always adapt to new developments and discover ways to make software development more efficient and beneficial. The population of the study consists of 1,500 and 2,000 employees of software companies in Nepal implementing agile techniques. Methodology: The sample size considered was 150 employees working in software companies in Nepal. However, only 106 respondents responded after three follow-ups. The sample was collected with purposive sampling. A questionnaire was developed to gain information on Customer Adaptive, Customer Collaboration, Continuous Delivery, Human Centric, and Technical Excellence related to agile practices along with the Agile Maturity Level. Contribution: This research contributes to the understanding of agile practices adopted in software companies in developing countries like Nepal. It also reveals the determinants of the agility of software companies in developing countries. Findings: The results suggest that some of the basic principles of agile have a very significant role in Agile Maturity Level in the Nepali context. In the context of Nepal, human-centered practices have a very high level of correlation, which plays a vital role as a major predictor of the agile maturity level. In addition, Technical Excellence is the variable that has the highest level of association with the Agile Maturity Level, making it the most significant predictor of this quality. Recommendations for Practitioners: As Nepali software companies are mostly offshore or serve outsourcing companies, there is a very thin probability of Nepali developers being able to interact with actual clients and this might be one of the reasons for the Nepali industry not relying on Customer Adaptation and Collaboration as major factors of the Agile methodologies. Continuous Delivery, on the other hand, has a significant degree of correlation with Agile Maturity Level. Human-centric practices have a very high level of correlation as well as being a major predictor in determining the Agile Maturity Level in the context of Nepal. Technical Excellence is the most significant predictor and the variable which has the highest level of correlation with Agile Maturity Level. Practitioners should mainly focus on technical excellence as well as human-centric practices to achieve a higher level of Agile Maturity. Recommendation for Researchers: There has not been any such research in the Nepali context that anyone could rely on, to deep dive into their organizational concerns regarding agile strategies and plans. Researchers will need to focus on a more statistical approach with data-driven solutions to the issues related to people and processes. Researchers will need to cover freelancers as well as academics to get a different perspective on what can be the better practices to achieve a higher level of agile maturity. Impact on Society: This study on Agile work is accessible not only to the software industry but also to the general public. The Agile technique has had a huge impact on society’s project management. It has revolutionized how teams approach project planning, development, and execution. The paper’s findings will further information regarding the Agile methodology, which emphasizes collaboration and communication, fosters teamwork and higher quality work, and promotes the exchange of knowledge, ideas, and the pursuit of common goals. Future Research: Owing to the limitations of this study, it is necessary to analyze agile practices in the Nepalese software sector using additional factors that influence agile maturity. The conclusion that years of agile experience do not serve as a balancing factor for both agile practices and the Agile Maturity Level requires additional research. Whether a software outsourcing firm or not, the organization type had no bearing on the degree of maturity of agile methods; this leaves space for further research.

Aim/Purpose: This study investigated factors influencing the intention to engage in green innovation among small and medium enterprises (SMEs) in the tourism sector, using an integrated approach from the pro-environmental planned behavior (PEPB) and technology organization environment (TOE) models. Background: Green innovation is a long-term strategy aimed at addressing environmental challenges in the Indonesian tourism sector, especially those related to SMEs in culinary, accommodation, transportation, and creative industries. While prior research primarily focused on innovation characteristics and various behavioral intentions towards new technologies, this study pioneered an approach to understanding green innovation practices among SMEs by examining behavioral intention and the influence of internal organizational and external environmental factors. This was achieved through the PEPB model, which extends the theory of planned behavior (TPB) by incorporating perceived authority support and perceived environmental concern and integrating it with the TOE model. This comprehensive approach was crucial for understanding SME motivations, needs, and challenges in adopting green innovation, thereby supporting environmental sustainability. Methodology: Data were collected through offline and online questionnaires and interviews with 405 SMEs that had implemented green innovation as respondents. The theoretical model was tested using partial least squares structural equation modeling (PLS-SEM) with top-level constructs. Contribution: This research contributed to the development and validation of an integrated model for green innovation in SMEs, offering insights and recommendations for all stakeholders in the tourism sector to formulate effective green innovation strategies. Findings: This research revealed that the integrated model of pro-environmental planned behavior and technology organization environment successfully explained 71% of the factors influencing the intention to engage in green innovation for SMEs in the tourism sector. Perceived authority support emerged as the strongest factor, while perceived behavioral control was identified as a weaker factor. Recommendations for Practitioners: The research findings recommended that SMEs in the tourism sector focus on customer satisfaction and operational efficiency and optimize the recruitment and training processes of resources to maximize success in adopting environmentally friendly innovations. Meanwhile, for the government, providing support, incentives, and stringent environmental regulations could encourage sustainable business practices. Recommendation for Researchers: The research findings recommended that SMEs in the tourism sector focus on customer satisfaction and operational efficiency and optimize the recruitment and training processes of resources to maximize success in adopting environmentally friendly innovations. Meanwhile, for the government, providing support, incentives, and stringent environmental regulations could encourage sustainable business practices. Impact on Society: Examining the factors influencing the intention to engage in green innovation among SMEs in the tourism sector carried significant social implications. The findings contributed to recommending strategies for businesses and stakeholders such as the government, investors, and tourists to collectively strive to minimize environmental damage in tourist areas through the implementation of green innovation. Future Research: There are several promising avenues to explore to enhance future research. Expanding the scope to include diverse regions and industries and using additional approaches, such as leadership theory and management commitment theories, can increase the R-squared value. Additionally, broadening the profile of interviewees to obtain a more comprehensive understanding of the intention to engage in green innovation should be considered.

Aim/Purpose: While existing literature has extensively explored factors influencing the success of big data projects and proposed big data maturity models, no study has harnessed machine learning to predict project success and identify the critical features contributing significantly to that success. The purpose of this paper is to offer fresh insights into the realm of big data projects by leveraging machine-learning algorithms. Background: Previously, we introduced the Global Big Data Maturity Model (GBDMM), which encompassed various domains inspired by the success factors of big data projects. In this paper, we transformed these maturity domains into a survey and collected feedback from 90 big data experts across the Middle East, Gulf, Africa, and Turkey regions regarding their own projects. This approach aims to gather firsthand insights from practitioners and experts in the field. Methodology: To analyze the feedback obtained from the survey, we applied several algorithms suitable for small datasets and categorical features. Our approach included cross-validation and feature selection techniques to mitigate overfitting and enhance model performance. Notably, the best-performing algorithms in our study were the Decision Tree (achieving an F1 score of 67%) and the Cat Boost classifier (also achieving an F1 score of 67%). Contribution: This research makes a significant contribution to the field of big data projects. By utilizing machine-learning techniques, we predict the success or failure of such projects and identify the key features that significantly contribute to their success. This provides companies with a valuable model for predicting their own big data project outcomes. Findings: Our analysis revealed that the domains of strategy and data have the most influential impact on the success of big data projects. Therefore, companies should prioritize these domains when undertaking such projects. Furthermore, we now have an initial model capable of predicting project success or failure, which can be invaluable for companies. Recommendations for Practitioners: Based on our findings, we recommend that practitioners concentrate on developing robust strategies and prioritize data management to enhance the outcomes of their big data projects. Additionally, practitioners can leverage machine-learning techniques to predict the success rate of these projects. Recommendation for Researchers: For further research in this field, we suggest exploring additional algorithms and techniques and refining existing models to enhance the accuracy and reliability of predicting the success of big data projects. Researchers may also investigate further into the interplay between strategy, data, and the success of such projects. Impact on Society: By improving the success rate of big data projects, our findings enable organizations to create more efficient and impactful data-driven solutions across various sectors. This, in turn, facilitates informed decision-making, effective resource allocation, improved operational efficiency, and overall performance enhancement. Future Research: In the future, gathering additional feedback from a broader range of big data experts will be valuable and help refine the prediction algorithm. Conducting longitudinal studies to analyze the long-term success and outcomes of Big Data projects would be beneficial. Furthermore, exploring the applicability of our model across different regions and industries will provide further insights into the field.

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.

In recent years, schools (as well as universities) have added cyber security to their computer science curricula. This topic is still new for most of the current teachers, who would normally have a standard computer science background. Therefore the teachers are trained and then teaching their students what they have just learned. In order to explore differences in both populations’ learning, we compared measures of software quality and security between high-school teachers and students. We collected 109 source files, written in Python by 18 teachers and 31 students, and engineered 32 features, based on common standards for software quality (PEP 8) and security (derived from CERT Secure Coding Standards). We use a multi-view, data-driven approach, by (a) using hierarchical clustering to bottom-up partition the population into groups based on their code-related features and (b) building a decision tree model that predicts whether a student or a teacher wrote a given code (resulting with a LOOCV kappa of 0.751). Overall, our findings suggest that the teachers’ codes have a better quality than the students’ – with a sub-group of the teachers, mostly males, demonstrate better coding than their peers and the students – and that the students’ codes are slightly better secured than the teachers’ codes (although both populations show very low security levels). The findings imply that teachers might benefit from their prior knowledge and experience, but also emphasize the lack of continuous involvement of some of the teachers with code-writing. Therefore, findings shed light on computer science teachers as lifelong learners. Findings also highlight the difference between quality and security in today’s programming paradigms. Implications for these findings are discussed.

Digital solutions have substantially contributed to the growth and dissemination of education. The distance education modality has been presented as an opportunity for worldwide students in many types of courses. However, projects of digital educational platforms require different expertise including knowledge areas such as pedagogy, psychology, computing, and digital technologies associated with education that allow the correct development and application of these solutions. To support the evolution of such solutions with satisfactory quality indicators, this research presents a model focused on quality of online educational solutions grounded in an approach aimed to continuous process improvement. The model considers of three maturity levels and six common entities that address the specific practices for planning and developing digital educational solutions, targeting quality standards that satisfy their users, such as students, teachers, tutors, and other people involved in development and use of these kinds of educational solutions.

In the fast changing global economic landscape, the cultivation of sustainable entrepreneurial ventures is seen as a vital mechanism that will enable businesses to introduce new innovative products to the market faster and more effectively than their competitors. This research paper investigated phenomena that may play a significant role when entrepreneurs implement creative ideas resulting in successful technology enabled start-ups within the South African market place. Constant and significant changes in technology provide several challenges for entrepreneurship. Various themes such as innovation, work experience, idea generation, education and partnership formation have been explored to assess their impact on entrepreneurship. Reflection and a design thinking approach underpinned a rigorous analysis process to distill themes from the data gathered through semi structured interviews. From the findings it was evident that the primary success influencers include the formation of partnership, iterative cycles, and certain types of education. The secondary influencers included the origination of an idea, the use of innovation. and organizational culture as well as work experience. This research illustrates how Informing Science as a transdisicpline can provide a philosophical underpinning to communicate and synthesise ideas from constituent disciplines in an attempt to create a more cohesive whole. This diverse environment, comprising people, technology, and business, requires blending different elements from across diverse fields to yield better science. With this backdrop, this preliminary study provides an important foundation for further research in the context of a developing country where entrepreneurial ventures may have a socio-economical impact. The themes that emerged through this study could provide avenues for further research.

Aim/Purpose: Although a literature review is the fundamental base for any research, it is often considered tedious and conducted with a lack of methodology and rigor. The paper presents a method for systematically searching and screening literature using modern search technologies. The method focuses on minimizing the amount of manual screening by employing the references among papers. Background: A method to select quality literature effectively using modern search technologies is presented and evaluated. Methodology: The method starts with a keywords search in which the most suitable keywords are identified. In the backward search, promising resources are collected based on the keywords and their reference sections are searched for duplicates to find often cited basic literature. Then, the forward search identifies current literature that cites the basic sources. Contribution: Modern search technologies have the potential to improve the effectiveness of the use of information channels significantly and thus of traditional literature searches. Findings: The selection method was applied to the field of literature review itself and to the field of functional modelling. In both cases, relevant literature was identified within a surprisingly short time. Recommendation for Researchers: Literature reviews should be done systematically by using modern search technologies. Future Research: The presented method may be adapted according to the evolution of search technologies. The tool support for the automated extraction of references should be improved and a quantitative evaluation of the method in comparison to traditional reviews may foster the findings.

Aim/Purpose: This article takes ‘tourism decision-making behavior’ as an entry point, and deeply analyzes the factors influencing the travel decision-making of Chinese ‘American Travel’ tourists and their degree of influence, so as to provide a reference for the development of Chinese outbound tourism. Background: With the development of China’s economy and the improvement in people’s level, the outbound tourism market of Chinese residents has developed rapidly. The United States has become an important tourism destination country for Chinese residents’ outbound tourism, and China has also become one of the important tourist source countries of American tourism. However, the rapid development of ‘American tourism’ has also caused competition problems in China’s tourism industry. For example, prices and tourism products have become a means of competition among tourism enterprises. As the main body of consumption, tourists’ decision-making behavior will be affected by various factors. Methodology: Drawing lessons from previous scholars’ research results on tourism decision-making behavior, the influencing factors of tourism decision-making behavior are summarized. A theoretical model and index system of factors influencing tourism decision-making behavior of Chinese residents ‘Travel in the United States’ are established, research hypotheses are put forward, questionnaire data are collected, and SPSS and Amos are used to analyze and verify the theoretical model. Contribution: This research expands the literature on topics related to tourism decision-making in research and practice. It establishes a theoretical model and index system for the factors that influence the decision-making behavior of Chinese residents’ ‘American Travel’ tourism. In addition, we propose countermeasures for tourism products, enterprises, and the government. Findings: Prior knowledge and external information have a positive influence on tourism perception and value perception, and a negative influence on risk perception. Risk perception value perception has a positive and negative influence on tourism decision-making and tourism motivation, respectively. Tourism motivation has a positive influence on tourism decision-making and has a positive impact. Recommendation for Researchers: According to the research conclusions of this article, the following counter-measures and suggestions are put forward from three aspects of tourism: products, enterprises, and governments. On the basis of existing tourism products, relevant operating companies should pay more attention to the upgrading and transformation of tourism, leisure and entertainment products in scenic spots to increase the willingness of tourists to travel. When considering corporate marketing and promotion plans, tourism companies operating related businesses should increase the weight of their marketing budgets in online marketing, increase investment in online marketing, and develop mobile applications that meet the preferences of Chinese residents in the United States. Do a good job in the timely publication of safety reminders and local information. Safety is an important foundation for tourism development and the core concern of many tourists. Future Research: Due to the important research on the impact of tourism activities, the influencing factors are many and complex, and the psychological process of tourism decision-making is carried out directly. There are still unconsidered factors that need to be studied in depth. In the future, it is possible to compare multiple resource-featured themes, and increase the characteristics of potential tourists, and the factors affecting the selection behavior of regional cultural tourists, and so forth, in order to make the research more applicable and practical instructive significance.

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

Aim/Purpose: To explore the incidence of addiction potential within the Iranian public working population, describing how many Iranian public employees fall within the diagnostic categories of low, moderate, and high addiction potential. Also, to investigate the predicting role of occupational variables such as perceived stress, job security, and job satisfaction on addiction potential and belonging to low, moderate, and high addiction potential diagnostic categories. Background: Substance addiction among employees can lead to several negative consequences at the individual and organizational levels. Also, it is the fourth cause of death in Iran. However, few studies have been conducted on the topic among employees, and non among Iranian employees. Methodology: The study participants were 430 employees working in governmental offices of the North Khorasan province, Iran. Descriptive statistical analysis and multiple linear regression analysis were conducted to explore the incidence of addiction potential within the analyzed population and to investigate whether occupational variables such as perceived stress, job security, and job satisfaction predicted low, moderate, or high addiction potential. Contribution: This paper suggests that perceived stress might act as a risk factor for developing addiction, whereas job security and job satisfaction might be protective factors against the likelihood of addiction development. Findings: More than half of the sample showed moderate to high addiction potential. Perceived stress was positively related to addiction potential. Job security and job satisfaction were negatively related to addiction potential. Recommendation for Researchers: When addressing the topic of substance addiction, researchers should focus on the preventative side of investigating it; that is, addiction risk rather than already unfolded addiction. Also, researchers should be mindful of the cultural context in which studies are conducted. Future Research: Future research might investigate other relevant occupational predictors in relation to employee addiction potential, such as leadership style, work-life balance, and worktime schedule, or expand on the relevant causal chain by including personality traits such as neuroticism.

Aim/Purpose: This study aims to explore students’ digital entrepreneurial intention (DEI) in Kuwait. Specifically, the aim is twofold: (i) to identify and examine the factors influencing and predicting students’ DEI, and (ii) to validate a model of DEI. Background: The advent of modern digital technologies has provided entrepreneurs with many opportunities to establish and expand their firms through online platforms. Although the existing literature on DEI has explored various factors, certain factors that could be linked to DEI have been neglected, and others have not been given sufficient attention. Nonetheless, there has been little research on students’ DEI, particularly in Kuwait. Methodology: To fulfill the research’s aims, a study was conducted using a quantitative method (a survey of 305 students at a non-profit university in Kuwait). Contribution: This study aimed to fill the research gap on the limited DEI research among Kuwait’s students. Several recommendations were suggested to improve the DEI among students in Kuwait. Findings: The study identified five factors that could influence an individual’s intention to engage in digital entrepreneurship. These factors include self-perceived creativity, social media use, risk-taking and opportunity recognition, digital entrepreneurship knowledge, and entrepreneurial self-perceived confidence. Significant solid correlations were between all five identified factors and DEI. However, only self-perceived creativity and entrepreneurial self-perceived confidence were identified as significant positive predictors of DEI among undergraduates in Kuwait. Nevertheless, the main contributor to this intention was the students’ self-perceived confidence as entrepreneurs. Recommendation for Researchers: Researchers should conduct further longitudinal studies to understand better the dynamic nature of DEI and execution. Future Research: Additional research is required to utilize probability sampling approaches and increase the sample size for more generalizable findings.

Keitaro NAKASAI,Shin KOMEDA,Masateru TSUNODA,Masayuki KASHIMA, Vol.E107-D, No.11, pp.1444-1448
To automatically measure the mental workload of developers, existing studies have used biometric measures such as brain waves and the heart rate. However, developers are often required to equip certain devices when measuring them, and can therefore be physically burdened. In this study, we evaluated the feasibility of non-contact biometric measures based on the nasal skin temperature (NST). In the experiment, the proposed biometric measures were more accurate than non-biometric measures.
Publication Date: 2024/11/01

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

Recent innovations in additive manufacturing (AM) have proven its efficacy for not only the manufacturing industry but also the healthcare industry. Researchers from Cal Poly, San Luis Obispo, and California State University Long Beach are developing a model that will determine the optimal locations for additive manufacturing hubs that can effectively serve both the manufacturing and healthcare industries. This paper will focus on providing an overview of the healthcare industry's unique needs for an AM hub and summarise the specific inputs for the model. The methods used to gather information include extensive literature research on current practices of AM models in healthcare and an inclusive survey of healthcare practitioners. This includes findings on AM's use for surgical planning and training models, the workflow to generate them, sourcing methods, and the AM techniques and materials used. This paper seeks to utilise the information gathered through literature research and surveys to provide guidance for the initial development of an AM hub location model that locates optimal service locations.

Ph.D. Dissertation Defense Modeling and Extracting Information about Cybersecurity Events from Text Taneeya Satyapanich 9:30-11:30 Monday, 18 November, 2019, ITE346? People now rely on the Internet to carry out much of their daily activities such as banking, ordering food, and socializing with their family and friends. The technology facilitates our lives, but also comes with […]

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This paper how to apply dynamic topic models to a set of cybersecurity documents to understand how the concepts found in them are changing over time.

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We advance a model that highlights contingent linkages between overconfidence and narcissism, entrepreneurial entry, and the successful realization of venture opportunities. Overall, our proposals point to a paradox in which entrepreneurs high in overconfidence and narcissism are propelled toward more novel venture contexts—where these qualities are most detrimental to venture success, and are repelled from more familiar venture contexts—where these qualities are least harmful, and may even facilitate venture success. To illuminate these patterns of misalignment, we attend to the defining characteristics of alternative venture contexts and the focal mechanisms of overconfidence and narcissism.

This study looks at what happens to the collection of relationships (network) of service professionals during a role transition (promotion to a management role). Our setting is three professional service firms where we examine changes in relations of recently promoted service professionals (auditors, consultants, and lawyers). We take a comprehensive look at the drivers of two forms of network changes - tie loss and tie gain. Looking backward we examine the characteristics of the contact, the relationship, and social structure and identify which forces are at play in losing ties, revealing an overarching tendency for both cohesion and efficiency forces to play a role. Looking forward, we identify the effect of previous network structures that act as a "shadow of the past" and impact the quality of newly gained relations during the role transitions. Findings demonstrate that role transitions are not only influenced by a few key contacts but that the entire (extant) network of professional relationships shapes the way people reconfigure their workplace relations during a role transition.

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