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The nature of technologies that are recognised as Artificial Intelligence (AI) has continually changed over time to be something more advanced than other technologies. Despite the fluidity of understanding of AI, the most common theme that has stuck with AI is ‘human-like decision making’. Advancements in processing power, coupled with big data technologies, gave rise to highly accurate prediction algorithms. Analytical techniques which use multi-layered neural networks such as machine learning and deep learning have emerged as the drivers of these AI-based applications. Due to easy access and growing information workforce, these algorithms are extensively used in a plethora of industries ranging from healthcare, transportation, finance, legal systems, to even military. AI-tools have the potential to transform industries and societies through automation. Conversely, the undesirable or negative consequences of AI-tools have harmed their respective organisations in social, financial and legal spheres. As the use of these algorithms propagates in the industry, the AI-based decisions have the potential to affect large portions of the population, sometimes involving vulnerable groups in society. This chapter presents an overview of AI’s use in organisations by discussing the following: first, it discusses the core components of AI. Second, the chapter discusses common goals organisations can achieve with AI. Third, it examines different types of AI. Fourth, it discusses unintended consequences that may take place in organisations due to the use of AI. Fifth, it discusses vulnerabilities that may arise from AI systems. Lastly, this chapter offers some recommendations for industries to consider regarding the development and implementation of AI systems.

This study aims at rereading public governance (PG) and public value management (PVM) in the light of viable systems approach (VSA). Starting from the common points and the dissimilarities between the two theories, an integrated framework for pinpointing the key drivers leading to the emersion of public value co-creation in a public system conception of governance is elaborated. An overview on the emersion of PVM and PG is conducted in order to identify the main features of the new mindset. Then, VSA’s assumptions also are analyzed (with particular focus on their managerial implications) and then subdivided into four macro-areas.

The combination of the two theories allows recognition of four levers (with relative postulates) for fostering public value co-creation: (1) strategic selection of actors; (2) establishment of system and relational boundaries; (3) pursuit of the fit strategy-tactics; (4) system governance diffusion. From a theoretical point of view, the study provides suggestions for the creation of a public system theory of governance. Regarding managerial standpoint, revealing the drivers for public value co-creation can aid managers to better elaborate strategies for stimulating actor’s engagement in order to challenge complexity and user’s demands variability.

The chapter aims to reflectively discuss a participatory research project concerning the future of higher education in Hungary. This project can be understood as an ongoing methodological experiment which attempts to engage teachers and students, in order to reveal how key stakeholders think about the future of higher education. In line with this, this methodologically oriented chapter shows how different participatory methodologies can be combined in a so-called backcasting framework. This approach starts by describing the present situation, then moves beyond the present conditions so as to identify the cornerstones of an ideal future state. On the one hand, the chapter gives a detailed introduction to how our participatory research process was set up and what particular methodologies we used during this process. On the other hand, it critically reflects on the methodological and ethical challenges involved.

Purpose: This chapter describes the design and implementation, at the computer hardware and software level, of the Greggg robot. Greggg is a scalable high performance, low cost hospitality robot constructed from off-the-shelf parts. Greggg has a robust architecture and acts as a tour guide on-campus, both indoors or outdoors. This research allows one to build a customized robot at a low cost, under U.S. $2,000, for accomplishing the desired hospitality tasks, and scale, and expand the capability of the robot as required.

Practical Implications: The practical implication of the research is the capability to build and program a robot for hospitality tasks. Greggg is a customizable robot capable of giving on-campus tours both indoors and outdoors. In its current architecture, Greggg can be trained to be a museum docent and give directions to visitors on-campus or at an airport and scaled up for other hospitality tasks using off-the-shelf components. Enhancing the robot by scaling it up and expanding it, in addition to testing it with a range of increasingly more difficult tasks using machine learning algorithms, is highly beneficial to advancing research on the use of robots in the hospitality sector. Greggg can also be used for Robot-as-a-service (Rass) applications.

Societal Implications: The economic implication of Greggg is the ease and low cost with which one, with minimal technology know-how, can construct an autonomous hospitality industry robot. This chapter details the hardware and software needed to build a low cost scalable and customizable autonomous robot for the hospitality industry without having to pay an exorbitant price.

Research/Limitations/Implications: This research allows one to build their own customized hospitality robot under U.S. $2,000. Given the cost of building the robot, it has limitations on the hospitality tasks it can perform. It can navigate on flat surfaces, has limited vision and speech processing capabilities and has a battery life not exceeding an hour. Furthermore, it does not have any robotic manipulators or tactile processing capabilities.

This chapter provides an overview of the potential use of Intelligent Transport Systems (ITS) and associated artificial intelligence (AI) techniques in the land transport sector in an attempt to achieve related United Nations Sustainable Development Goals (SDGs) targets. ITS applications that have now been extensively tested worldwide and have become part of the everyday transport toolkit available to practitioners have been discussed. AI techniques applied successfully in specific ITS applications such as automatic traffic control systems, real-time image processing, automatic incident detection, safety management, road condition assessment, asset management and traffic enforcement systems have been identified. These methods have helped to provide traffic engineers and transport planners with novel ways to improve safety, mobility, accessibility and efficiency in the sector and thus move closer to achieving the various SDG targets pertaining to transportation.