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This chapter delves into the transformative potential of Artificial Intelligence (AI) in spearheading the smart city vision by 2040 in the Gulf Region. It emphasizes the integration of AI with smart city governance, strategy, and infrastructure, underlining the pivotal role AI plays in digital transformation. The discourse navigates through strategic areas such as the exploitation of Large Language Models for enhancing smart city services, the utilization of AI in analyzing social network content and crowdsourcing applications, and leveraging AI’s reasoning capabilities to bolster predictive analytics and smart city services innovation. The chapter also showcases how AI-driven innovation fosters the development of new markets and industries within the smart city ecosystem. Highlighting the Gulf Region’s strategic investment in human capital and technology, the authors present a vision where smart cities serve as hubs of sustainability, innovation, and economic growth. Through the lens of the Smart City GSUTI Framework, the narrative illustrates the comprehensive integration of governance, strategy, utilization frameworks, technologies, and infrastructural capabilities. This holistic approach ensures that AI not only enhances the quality of life and economic prosperity in the Gulf Region but also positions it as a global leader in smart city development. The chapter is a call to action for leveraging AI’s potential to transform the Gulf Region into a model for sustainable, innovative, and smart urban development by 2040.

This introductory chapter lays the foundation for a Holistic Smart City framework organized around five pillars namely: Smart City Governance – Strategy – Utilization – Technology – Infrastructure (GSUTI) model. Smart cities governance establishes the overarching structure for effective management, emphasizing data integrity, regulatory resilience, and civic engagement. The strategy pillar integrates vision, mission, and strategic initiatives aligned with sustainable development goals, ensuring resource allocation and innovation resilience. The Utilization Framework encompasses diverse domains like transportation, energy, and healthcare, thus prioritizing initiatives and monitoring impact, enabling technologies that drive smart city innovation, leveraging artificial intelligence (AI) (Chui et al., 2018), Internet of Things, blockchain, and cybersecurity to enhance efficiency and service delivery. Infrastructures and capabilities encompass physical and data ecosystems, from buildings and transportation hubs to big data streams and social media content. The chapter sets the context for the scientific discussion, offering practical insights into strategic consultation, performance monitoring, technology management, and data governance. It outlines directions for smart city development in the Gulf Region, including bold visions, megaprojects, national-level initiatives, data-driven services, and AI-driven excellence. This holistic framework provides a roadmap for smart city development, fostering sustainability, innovation, and societal well-being across the Gulf Region and beyond.

In this chapter, we present an introductory and definitive discussion of transformative leadership as a holistic and bold approach for the next generation of higher education. We integrate this concept with the idea of sustainable innovation. The chapter is divided into four sections, each addressing essential aspects of transformative leadership in higher education. In Section 1, we introduce a high-level integrated approach to transformative leadership in higher education institutions. We define and discuss the diverse pillars that form the foundation of this leadership style. In Section 2, we propose a contextual framework for transformative leadership as a value space. This framework provides guidelines and principles for crafting a transformative leadership strategy, and we offer indicative actions and initiatives for its deployment in higher education. To support the documentation of the transformative leadership strategy, Section 3 outlines simple designs for tools and instruments, including the transformative leadership scorecard and the systematic overview of the portfolio of transformative educational programs. We also emphasize the significance of social impact, research, innovation, and sustainability aspects within the strategy. In Section 4, we summarize the key takeaways from this chapter. Our contribution is manifold, as this chapter can serve as a valuable reference for administrators seeking to design and execute transformative leadership in universities and colleges. Additionally, it offers guiding principles for researchers interested in making further contributions in this domain.

The purpose of this paper is to rethink the focus of the smart cities debate and to open it to policymaking and strategy considerations. To this end, the origins of what is termed normative bias in smart cities research are identified and a case made for a holistic, scalable and human-centred smart cities research agenda. Applicable across the micro, mezzo and macro levels of the context in which smart cities develop, this research agenda remains sensitive to the limitations and enablers inherent in these contexts. Policymaking and strategy consideration are incorporated in the agenda this paper advances, thus creating the prospect of bridging the normative and the empirical in smart cities research.

This paper queries the smart cities debate and, by reference to megacities research, argues that the smart city remains an overly normatively laden concept frequently discussed in separation from the broader socio-political and economic contexts in which it is embedded. By focusing on what is termed the normative bias of smart cities research, this paper introduces the nested clusters model. By advocating the inclusion of policymaking and strategy considerations in the smart cities debate, a case is made for a holistic, scalable and human-centred smart cities agenda focused, on the one hand, on individuals and citizens inhabiting smart cities and, on the other hand, on interdependencies that unfold between a given smart city and the context in which it is embedded.

This paper delineates the research focus and scope of the megacities and smart cities debates respectively. It locates the origins of normative bias inherent in smart cities research and, by making a case for holistic, scalable and human-centred smart cities research, suggests ways of bypassing that bias. It is argued that smart cities research has the potential of contributing to research on megacities (smart megacities and clusters), cities (smart cities) and villages (smart villages). The notions of policymaking and strategy, and ultimately of governance, are brought into the spotlight. Against this backdrop, it is argued that smart cities research needs to be based on real tangible experiences of individuals inhabiting rural and urban space and that it also needs to mirror and feed into policy-design and policymaking processes.

The paper stresses the need to explore the question of how the specific contexts in which cities/urban areas are located influence those cities/urban areas’ growth and development strategies. It also postulates new avenues of inter and multidisciplinary research geared toward building bridges between the normative and the empirical in the smart cities debate. More research is needed to advance these imperatives at the micro, mezzo and macro levels.

By highlighting the connection, relatively under-represented in the literature, between the normative and the empirical in smart cities research, this paper encourages a more structured debate between academia and policymakers focused on the sustainable development of cities/urban areas. In doing so, it also advocates policies and strategies conducive to strengthening individuals’/citizens’ ability to benefit from and contribute to smart cities development, thereby making them sustainable.

This paper makes a case for pragmatic and demand-driven smart cities research, i.e. based on the frequently very basic needs of individuals and citizens inhabiting not only urban but also rural areas. It highlights the role of basic infrastructure as the key enabler/inhibitor of information and communication technology-enhanced services. The nested clusters model introduced in this paper suggests that an intimate connection exists between individuals’ well-being, their active civic engagement and smart cities sustainability.

This paper delineates the relationship between megacities and smart cities research. It identifies the sources of what is termed normative bias in smart cities research. To address the implications of that bias, a nested clusters model for smart cities is introduced, i.e. a conceptual framework that allows us to redraw the debate on smart cities and establish a functional connection between the array of normatively laden ideas of what a smart city could be and what is feasible, and under which conditions at the policymaking level.

The purpose of this paper is to contextualise and examine critically the collection of the papers dealing with the broad and multi-faceted question of risk, threats and challenges governments are exposed to in the 21st century. To this end, the concept of ‘distributed risks and threats’ is introduced to account of challenges spread across the context, in which governments are embedded.

This paper offers a critical insight into the content of the issue.

The key argument that this paper advances is that while the nature of risks, threats and challenges that governments are exposed to today is qualitatively new and their scope unprecedented, a lot of governments’ capacity remains idle, i.e. ready to be deployed to address these risks, threats and challenges.

As a review paper, the points conveyed in this paper sketch and highlight, rather than explore in-depth, the possible and new research avenues that the collection of papers prompts.

This paper highlights that the – developed over the centuries – capacity of the government to act and address risks and threats is incommensurate with the agility of challenges borne in the 21st century.

This paper introduces the concept of ‘distributed risks and threats’ to account of the qualitatively new and hybrid challenges spread across the context, in which governments are embedded. This conceptualization of risks and threats, or challenges, offers a handy way to contextualize the variety of ways in which the government is challenged today.

The purpose of this paper is to explore the integration of serious games (SGs) in the area of special educational needs in the last ten years (2007-2017).

SGs indicate positive effects on students with special educational needs and promote a multi-sensory style of learning.

Research showed that SGs are able to keep K-12 education students with attention, memory and developmental disabilities engaged in classroom facilities scaffolding their learning through increased motivation, independence, autonomy and resultant self-esteem.

Time constraints, cost and availability of appropriate games as well as the small sample of the individuals being investigated are some of the research limitations the paper refers to.

Learning through SGs has educational values that are based on learning concepts intrinsically motivating.

Students with attention, memory and developmental disabilities demonstrate characteristics of engagement, creativity, control and communication.

SGs-based learning has proven its value added to students with attention, memory and executive control difficulties as well as mental or developmental disabilities engaging students better than when using traditional methods.

Although location-based augmented reality (AR) games are popular in recent years, the motivation of in-app purchases still needs further investigation. The purpose of this paper is to investigate the antecedents of the intention to make a purchase in location-based AR games.

This research develops a conceptual model and hypotheses based on the theory of perceived values and satisfaction to investigate the antecedents. An online questionnaire was developed and distributed on popular websites to collect data, and 662 usable responses were collected from the players of Pokémon Go in Taiwan.

In total, 22 hypotheses were validated by using partial least squares techniques. Among the antecedents of in-app purchases intention in the model, the perceived value and satisfaction were found to have strong direct effects. The antecedents of the perceived value (flow, design aesthetic, social self-expression and good price) have direct influences on the perceived value of all players. Design aesthetic, reward and the perceived value were found to have a direct impact on all players’ satisfaction. Moreover, the differences between paying users and non-paying users were discussed and verified.

The model demonstrated relatively good explanatory power for purchase intention in the context of location-based AR game. The proposed model can provide insights to location-based AR game developers to design their games and marketing strategies.

An ever-growing body of knowledge demonstrates the correlation among real-world phenomena and search query data issued on Google, as showed in the literature survey introduced in the following. The purpose of this paper is to introduce a pipeline, implemented as a web service, which, starting with recent Google Trends, allows a decision maker to monitor Twitter’s sentiment regarding these trends, enabling users to choose geographic areas for their monitors. In addition to the positive/negative sentiments about Google Trends, the pipeline offers the ability to view, on the same dashboard, the emotions that Google Trends triggers in the Twitter population. Such a set of tools, allows, as a whole, monitoring real-time on Twitter the feelings about Google Trends that would otherwise only fall into search statistics, even if useful. As a whole, the pipeline has no claim of prediction over the trends it tracks. Instead, it aims to provide a user with guidance about Google Trends, which, as the scientific literature demonstrates, is related to many real-world phenomena (e.g. epidemiology, economy, political science).

The proposed experimental framework allows the integration of Google search query data and Twitter social data. As new trends emerge in Google searches, the pipeline interrogates Twitter to track, also geographically, the feelings and emotions of Twitter users about new trends. The core of the pipeline is represented by a sentiment analysis framework that make use of a Bayesian machine learning device exploiting deep natural language processing modules to assign emotions and sentiment orientations to a collection of tweets geolocalized on the microblogging platform. The pipeline is accessible as a web service for any user authorized with credentials.

The employment of the pipeline for three different monitoring task (i.e. consumer electronics, healthcare, and politics) shows the plausibility of the proposed approach in order to measure social media sentiments and emotions concerning the trends emerged on Google searches.

The proposed approach aims to bridge the gap among Google search query data and sentiments that emerge on Twitter about these trends.

The purpose of this paper is to improve the classification of families having children with affective-behavioral maladies, and thus giving the families a suitable orientation.

The proposed methodology includes three steps. Step 1 addresses initial data preprocessing, by noise filtering or data condensation. Step 2 performs a multiple feature sets selection, by using genetic algorithms and rough sets. Finally, Step 3 merges the candidate solutions and obtains the selected features and instances.

The new proposal show very good results on the family data (with 100 percent of correct classifications). It also obtained accurate results over a variety of repository data sets. The proposed approach is suitable for dealing with non-symmetric similarity functions, as well as with high-dimensionality mixed and incomplete data.

Previous work in the state of the art only considers instance selection to preprocess the schools for children with affective-behavioral maladies data. This paper explores using a new combined instance and feature selection technique to select relevant instances and features, leading to better classification, and to a simplification of the data.