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The construction sector has significantly evolved in recent decades, in parallel with a huge increase in the amount of data generated and exchanged in any construction project. These data need to be managed in order to complete a successful project in terms of quality, cost and schedule in the the context of a safe project environment while appropriately organising many construction documents.

However, the origin of these data is very diverse, mainly due to the sector’s characteristics. Moreover, these data are affected by uncertainty, complexity and diversity due to the imprecise nature of the many factors involved in construction projects. As a result, construction project data are associated with large, irregular and scattered datasets.

The objective of this chapter is to introduce an approach based on a fuzzy multi-dimensional model and on line analytical processing (OLAP) operations in order to manage construction data and support the decision-making process based on previous experiences. On one hand, the proposal allows for the integration of data in a common repository which is accessible to users along the whole project’s life cycle. On the other hand, it allows for the establishment of more flexible structures for representing the data of the main tasks in the construction project management domain. The incorporation of this fuzzy framework allows for the management of imprecision in construction data and provides easy and intuitive access to users so that they can make more reliable decisions.

Current global business challenges and circumstances are responsible for the need for global talent management (GTM) within multinational corporations (MNCs). Social media and networks are becoming key channels for global communication and collaboration. For GTM in MNCs, an effective usage of social media can potentially result in a competitive edge and create value. The purpose of this study is to answer the question of how.

By means of the Delphi method, three groups of experts were interviewed with questionnaires in two rounds: HR managers, HR researchers, and students following a Master of Science program in Business Administration.

The findings show that all groups assessed the necessity of GTM in MNCs. The HR managers and HR researchers agreed on the areas of focus and instruments that are essential for a successful GTM system. But concerning the application of social networks, the groups have different views. The graduate students are especially open-minded about social networks, and therefore they advocate the use of this tool. The HR managers, however, are skeptical toward this new media and even now have not integrated social networks into GTM to a great extent.

This study presents a GTM model for MNCs based on the combined findings from the literature review and the Delphi study. To our knowledge, this is a new approach. The model helps researchers and practitioners to align GTM in MNCs with the support of social media.

This chapter qualitatively explored the impact of including parent liaisons (i.e., parents with lived experience caring for a child with complex needs, who support other caregivers in navigating child and family needs) in a case conferencing model for children with complex medical/social needs. Case conferences are used to address fragmented care, shared decision-making, and set patient-centered goals. Seventeen semi-structured interviews were conducted with clinicians and parent liaisons to assess the involvement of parent liaisons in case conferencing. Two main themes included benefits of parent liaison involvement (10 subthemes) and challenges to parent liaison involvement (5 subthemes). Clinicians reported that liaison participation and support of patients reduced stress for clinicians as well as family members. Challenges to liaison involvement included clinical team/parent liaison communication delays, which were further exacerbated by the COVID-19 pandemic. Parent liaison involvement in case conferences is perceived to be beneficial to children with complex needs, their families, and the clinical team. Integration of liaisons ensures the familial perspective is included in clinical goal setting.

The recent debate on smart cities research is challenged by the arrival of brand-new technologies and new ideas on their social impact. Beyond the hype and the expectations, the next generation smart cities research has to be grounded on the lessons learnt and the experience of the current extensive implementations of smart cities projects worldwide. Additionally, it is required to revisit the basic assumptions for the added value of smart cities research to the strategic blueprints around the world. This chapter is aiming to communicate a new agenda for future smart cities research including social, economic, technological, and community factors. The main contribution is organized around a framework that intends to integrate the technology sophistication, the human and social dynamics, and the strategy orientation of smart cities.

Purpose: In recent times, ‘artificial intelligence (AI)’ has been pervasive even in organisations or at home. AI is defined as programming computers or other technological devices to act, react, respond, or assist the same way humans do. AI has undeniably made people’s lives easier. In organisations, the impact of AI is even more visible. The main aim of this chapter is to examine the significant role of future work skill’s (FWS) each component in the field of on-growing automation. The focus will be especially on emotional and social intelligence (ESI) (a key component of FWS) while adopting AI.

Need of the Study: In terms of human resource management (HRM), AI is useful for people management, payroll services, staff monitoring and improving the recruiting network, among other things. Even managers put their organisation’s job openings on the web and get applicant resumes electronically. People and employees in the organisation have become more advanced and innovative due to AI. A device obtains employee attendance, and human resource (HR) can track their employees and their organisation’s workforce data. HR has now been awarded more authority to manage and fix their employee’s problems because of AI. In a rapidly changing world, AI is affecting all aspects. AI is yearning to automate all of the jobs.

Methodology: Now a question arises how we can stay relevant in AI economic development? As humans, we learned that every issue is a problem of optimisation because we simply require human skills to develop, create and innovate new things. Therefore, researchers recognised that adopting sustainable growth skills encourages people to continue learning throughout their lives. Moreover, AI has enabled machines the ability to learn over time. Still, they will never be able to develop new ideas like human intelligence. A machine can use only one fixed data algorithm. Now humans have made significant progress in various fields with the help of FWS; without integrated computer sciences, brain science would not make such an outstanding achievement. On the other hand, human minds are masters of their intelligence, such as creativity, complex problem-solving, cognitive thinking, ESI and communication. Breakthrough human mind are masters of algorithms represented people have to understand new trends of technology around us, and the best way to move forward is to be aware, adapt and update skills.

Practical Implications: However, AI is required because, regardless of technological advancements, AI is leading Industry 4.0. The industry’s transformation is in 4.0, and hopefully, 5.0 will jump on board soon. Undoubtedly, AI should streamline the process and eliminate redundancy or administrative tasks.

Finding: AI can be more effective in organisations if they incorporate other FWS, particularly the soft human ESI skills, whereas AI is present everywhere, we can still not neglect FWS, especially ESI. So, this chapter highlights the important role of soft skills, that is, ESI and FWS, while adapting AI for an effective HRM.