This book chapter offers an examination of the transformative influence of Artificial Intelligence (AI) within the aviation sector, focusing specifically on its application in predictive maintenance for enhancing operational efficiency. Through the investigation of current research and industry trends, and the utilization of a literature review as its methodological framework, this chapter elucidates the transformative impact of AI-driven predictive maintenance strategies on aviation operations. It explores how AI algorithms analyze vast amounts of data to predict potential equipment failures, enabling proactive maintenance interventions that minimize downtime and optimize fleet performance, presenting an analysis of the implications of AI integration in aviation. Notably, the integration of AI-driven technologies in critical areas such as flight planning, predictive maintenance, and air traffic management is highlighted, showcasing the significant advancements that have reshaped the aviation landscape. Furthermore, the chapter adopts a user-centric perspective, offering a critical assessment of the challenges and considerations inherent in AI implementation in aviation. This includes examining issues pertaining to human-machine interaction, trust in AI systems, and the evolving dynamics of job roles within the industry. Overall, the chapter provides a comprehensive overview of AI's impact on aviation, offering valuable insights for aviation professionals, policymakers, and researchers seeking a deeper understanding of the profound changes driven by AI within the aviation domain.

Cloud computing (CC) represents a key digital transformation advancement, reshaping the ways in which businesses operate across diverse industries. The exponential growth in data production has created an unprecedented challenge in the ways in which data is created, processed, and managed. This study investigates the impact of cloud computing on the digital transformation of the enterprise, including issues concerning sustainability and long-term preservation and curation. While there has been a proliferation of studies concerning the adoption and implementation of cloud computing in the enterprise, there is still a gap in the literature concerning the use of cloud computing technology for long-term preservation, digital curation, and sustainability. The study employed a mixed-methods approach that utilized a systematic review of the literature and an Internet-based survey. The combination of the systematic review and survey was intended to provide insights into the key strategic factors impacting the use of cloud computing for long-term preservation and sustainability. The results of the study show that, despite the growing recognition of the benefits of cloud computing, most organizations are still concerned about issues such as security, privacy, accessibility, and cost. Concerns regarding the long-term preservation and sustainability of enterprise information are closely tied to the extent to which cloud computing services are deemed reliable and trustworthy. This study underscores the varying levels of satisfaction among users, with businesses acknowledging both the advantages and disadvantages of the current cloud solutions.

The purpose of this quantitative study is to investigate the factors influencing with the Knowledge Management (KM) use process in Information Technology (IT) enterprises in the Southern United States. This study aims to present an analysis of the use of information systems by IT managers, IT supervisors, and Chief Information Officers (CIOs) from several information technology enterprises. It utilizes the theoretical Knowledge Management Successful model developed by Kulkarni, Ravindran, and Freeze (2007)39, which investigated the use of Information Systems (IS) for successful KM practices in organizations through the examination of available knowledge systems built to the use and reuse of information, content quality, and determinants of users’ perceptions of usefulness, user satisfaction and organizational support structure for knowledge management. In this study the data was collected from a sample size of 166 individuals, per G*Power 3 statistical power analysis program, to determine the sample and effect between the 8 (eight) predictors variables for estimating change among scores depicting Knowledge Use. Statistical analysis used SPSS package to test the hypothesis. The relationships between the predictor and criterion variables were evaluated using simultaneous multiple regression modelling to support inferences related to the omnibus research questions. The 8 (eight) predictors variables (Explicit Knowledge, Knowledge Systems, Supervisor, Co-Worker, Leadership, Incentive, Perceived Usefulness, and User Satisfaction) in this model were regressed independently onto scores on the Knowledge Use scale. This study concludes that the model predicting knowledge use is both statistically significant and practically significant, and that scores associated with Explicit Knowledge, Leadership, Perceived Usefulness, and User Satisfaction yielded statistically strong predictive relationships

The exponential growth of digital data generation and consumption in the past decade has ignited new discussions about the relevance and impact of knowledge management (KM) on individuals and businesses. This chapter presents a literature review examining knowledge management and systems of learning as well as some of the critical factors to be considered in the design, implementation, and evaluation of metrics for KM implementation success. It highlights the role of leadership and the importance of valuing knowledge workers for effective KM and KMS practices, and the design of knowledge metrics focused on learning and growth within the scope of the balanced scorecard framework and the possibilities of a Web 4.0 data processing environment in a competitive globalized market.

The increasing digital transformation in all segments of society has been creating demand in information management and access. Consequently, local and federal governments must adapt and launch digital services that enable inclusion and citizenship participation initiatives through digitalization and content management to meet these demands. Nevertheless, the simple conversion of physical forms to digital is not enough to enable access. This chapter highlights the relevance of the use of plain language towards information access. This work conducted action research supported by the Toyota Kata approach demonstrating how the change in the semantical construct in the forms and service description is significantly altered post digitalization of some services provided by the State of Santa Catarina Government. The results demonstrate that plain language impacts the comprehension and consumption of information, incurring more inclusive e-government services.

This chapter examines information overload and its impact on organizational performance and productivity, the level of use of data analytics tools by organizations to address the information overload problem, and reports on the results from some of the data collected from an online survey about the use of visual analytics tools in organizations. The survey was aimed at gathering users' experiences in dealing with information overload and their level of exposure to data analytics tools. The results from the survey show that email is still the most time-consuming application, with a reported increase in remote access via handheld devices. A relevant percentage of respondents (65%) confirmed having knowledge and experience using some sort of data analytics tools, while 69.23% stated that the exposure to large amounts of information at work causes stress and anxiety. Request access from your librarian to read this chapter's full text.