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This chapter uses the case of Tay as a basis for exploring Artificial Intelligence (AI) and its implications for corporate social responsibility (CSR). We explore issues with AI in relation to two of Roome's (2005) CSR agendas – responsible business practices and consumer responsibility. Then, we build a framework for approaching AI that connects user and designer perspectives, pointing out key concepts and opportunities for public relations (PR) professionals to engage with both uses for and development of AI in the workplace. We point out the ability of AI, as a technology, to turn the action of connecting to publics from a front office to a back office endeavor. Also, we advocate for PR's need, as a field, to rethink potential changes resulting from the integration of AI into organizations which can affect PR practice at a fundamental level. Finally, we propose ways for PR practitioners, educators, and researchers to consider integrating this understanding of AI into their work.

The aim of this article is to map the use of AI in the user experience (UX) design process. Disrupting the UX process by introducing novel digital tools such as artificial intelligence (AI) has the potential to improve efficiency and accuracy, while creating more innovative and creative solutions. Thus, understanding how AI can be leveraged for UX has important research and practical implications.

This article builds on a systematic literature review approach and aims to understand how AI is used in UX design today, as well as uncover some prominent themes for future research. Through a process of selection and filtering, 46 research articles are analysed, with findings synthesized based on a user-centred design and development process.

The authors’ analysis shows how AI is leveraged in the UX design process at different key areas. Namely, these include understanding the context of use, uncovering user requirements, aiding solution design, and evaluating design, and for assisting development of solutions. The authors also highlight the ways in which AI is changing the UX design process through illustrative examples.

While there is increased interest in the use of AI in organizations, there is still limited work on how AI can be introduced into processes that depend heavily on human creativity and input. Thus, the authors show the ways in which AI can enhance such activities and assume tasks that have been typically performed by humans.

Artificial intelligence (AI) is revolutionizing product recommendations, but little is known about consumer acceptance of AI recommendations. This study examines how to improve consumers' acceptance of AI recommendations from the perspective of product type (material vs experiential).

Four studies, including a field experiment and three online experiments, tested how consumers' preference for AI-based (vs human) recommendations differs between material and experiential product purchases.

Results show that people perceive AI recommendations as more competent than human recommendations for material products, whereas they believe human recommendations are more competent than AI recommendations for experiential products. Therefore, people are more (less) likely to choose AI recommendations when buying material (vs experiential) products. However, this effect is eliminated when is used as an assistant to rather than a replacement for a human recommendation.

This study is the first to focus on how products' material and experiential attributes influence people's attitudes toward AI recommendations. The authors also identify under what circumstances resistance to algorithmic advice is attenuated. These findings contribute to the research on the psychology of artificial intelligence and on human–technology interaction by investigating how experiential and material attributes influence preference for or resistance to AI recommenders.

Human resource managers are adopting AI technology for conducting various tasks of human resource management, starting from manpower planning till employee exit. AI technology is prominently used for talent acquisition in organizations. This research investigates the adoption of AI technology for talent acquisition.

This study employs Technology-Organization-Environment (TOE) and Task-Technology-Fit (TTF) framework and proposes a model to explore the adoption of AI technology for talent acquisition. The survey was conducted among the 562 human resource managers and talent acquisition managers with a structured questionnaire. The analysis of data was completed using PLS-SEM.

This research reveals that cost-effectiveness, relative advantage, top management support, HR readiness, competitive pressure and support from AI vendors positively affect AI technology adoption for talent acquisition. Security and privacy issues negatively influence the adoption of AI technology. It is found that task and technology characteristics influence the task technology fit of AI technology for talent acquisition. Adoption and task technology fit of AI technology influence the actual usage of AI technology for talent acquisition. It is revealed that stickiness to traditional talent acquisition methods negatively moderates the association between adoption and actual usage of AI technology for talent acquisition. The proposed model was empirically validated and revealed the predictors of adoption and actual usage of AI technology for talent acquisition.

This paper provides the predictors of the adoption of AI technology for talent acquisition, which is emerging extensively in the human resource domain. It provides vital insights to the human resource managers to benchmark AI technology required for talent acquisition. Marketers can develop their marketing plan considering the factors of adoption. It would help designers to understand the factors of adoption and design the AI technology algorithms and applications for talent acquisition. It contributes to advance the literature of technology adoption by interweaving it with the human resource domain literature on talent acquisition.

This research uniquely validates the model for the adoption of AI technology for talent acquisition using the TOE and TTF framework. It reveals the factors influencing the adoption and actual usage of AI technology for talent acquisition.

Advances in Artificial Intelligence (AI) technologies and Autonomous Unmanned Vehicles are shaping our daily lives, society, and will continue to transform how we will fight future wars. Advances in AI technologies have fueled an explosion of interest in the military and political domain. As AI technologies evolve, there will be increased reliance on these systems to maintain global security. For the individual and society, AI presents challenges related to surveillance, personal freedom, and privacy. For the military, we will need to exploit advances in AI technologies to support the warfighter and ensure global security. The integration of AI technologies in the battlespace presents advantages, costs, and risks in the future battlespace. This chapter will examine the issues related to advances in AI technologies, as we examine the benefits, costs, and risks associated with integrating AI and autonomous systems in society and in the future battlespace.

This paper aims to define hybrid reality (HyR) as an ongoing process in which artificial intelligence (AI) technology is gradually introduced as an active stakeholder by using reasoning to execute real-life activities. Also, to examine the implications of social responsibility (SR) concepts as featured in the HyR underlying common framework to progress towards the redefinition of global society.

A combination of systemic tools is used to examine and assess the development of HyR. The research is based on evolutionary and learning concepts, leading to the new meta-system development. It also builds upon the viable system model and AI, invoking SR as a conceptual framework. The research is conducted by using a new approach: using system dynamics based interactions modelling, the following two models have been proposed. The state-of-the-art HyR interactions model, examined using SR concepts; and a SR concept-based HyR model, examined using a smart vehicle case.

In the HyR model, interaction asymmetry between stakeholders is identified, possibly leading to pathological behaviour and AI technology learning corruption. To resolve these asymmetry issues, an interaction model based on SR concepts is proposed and examined on the example of an autonomous vehicle transport service. The examination results display significant changes in the conceptual understanding of transport services, their utilisation and data-sharing concepts.

As the research proposal is theoretical in nature, the projection may not display a fully holistic perspective and can/should be complemented with empirical research results.

For researchers, HyR provides a new paradigm and can thereby articulate potential research frameworks. HyR designers can recognise projected development paths and the resources required for the implication of SR concepts. Individuals and organisations should be aware of their not necessarily passive role in HyR and can therefore use the necessary social force to activate their status.

For the first time, to the best of the author’s knowledge, the term HyR is openly elaborated and systemically examined by invoking concepts of SR. The proposed model provides an overview of the current and potential states of HyR and examines the gap between them.

Inscrutable machine learning (ML) models are part of increasingly many information systems. Understanding how these models behave, and what their output is based on, is a challenge for developers let alone non-technical end users.

The authors investigate how AI systems and their decisions ought to be explained for end users through a systematic literature review.

The authors’ synthesis of the literature suggests that AI system communication for end users has five high-level goals: (1) understandability, (2) trustworthiness, (3) transparency, (4) controllability and (5) fairness. The authors identified several design recommendations, such as offering personalized and on-demand explanations and focusing on the explainability of key functionalities instead of aiming to explain the whole system. There exists multiple trade-offs in AI system explanations, and there is no single best solution that fits all cases.

Based on the synthesis, the authors provide a design framework for explaining AI systems to end users. The study contributes to the work on AI governance by suggesting guidelines on how to make AI systems more understandable, fair, trustworthy, controllable and transparent.

This literature review brings together the literature on AI system communication and explainable AI (XAI) for end users. Building on previous academic literature on the topic, it provides synthesized insights, design recommendations and future research agenda.

This paper aims to examine the influence of accounting information system (AIS) success on planning process effectiveness (PPE) in small- and medium-sized enterprises (SMEs) of Yemen, a less developed nation (LDN).

This study developed a theoretical model based on IS success model (DMISS2003). The model’s components were tested using structural equation modeling via SmartPLS on a sample of 325 SMEs.

The empirical results imply that AIS success positively affects PPE if SMEs focus on AIS information quality, system quality, user satisfaction and usage.

This study focused on SMEs in Yemen. Thus, it can be expanded to include different other countries.

The results of this study could be considered by owners and managers of SMEs, policymakers and AIS designers/vendors. This study could provide them insight into the role of AIS success in promoting PPE in SMEs. This study could assist policymakers in analyzing the work of SMEs and assessing their success, referring to AIS. Moreover, knowing the most critical determinants of AIS success could direct designers’/vendors’ efforts toward upgrading and improving the present AIS.

Government policymakers in LDNs considering how to motivate SME development in their nation should be aware of the significance of AIS success and implementation among SMEs and its role in the nation’s economic development.

This research is one of the first that investigates the impact of AIS success on PPE in SMEs. Current literature largely lacks the investigation of such an impact, especially among SMEs in LDNs such as Yemen. Most AIS’s prior research focused on SMEs in developed nations, which may not fully apply to LDNs such as Yemen. Indeed, no existing literature is available where AIS success impacts PPE in SMEs of LDNs. In this respect, this study claims its uniqueness.

The lack of conceptual approaches for organizing and expressing capabilities, usage and impact of intelligent machines (IMs) in work settings is an obstacle to moving beyond isolated case examples, domain-specific studies, 2 × 2 frameworks and expert opinion in discussions of IMs and work. This paper's purpose is to illuminate many issues that often are not addressed directly in research, practice or punditry related to IMs. It pursues that purpose by presenting an integrated approach for identifying and organizing important aspects of analysis and evaluation related to IMs in work settings. 

This paper integrates previously published ideas related to work systems (WSs), smart devices and systems, facets of work, roles and responsibilities of information systems, interactions between people and machines and a range of criteria for evaluating system performance.

Eight principles outline a straightforward and flexible approach for analyzing and evaluating IMs and the WSs that use them. Those principles are based on the above ideas.

This paper provides a novel approach for identifying design choices for situated use of IMs. The breadth, depth and integration of this approach address a gap in existing literature, which rarely aspires to this paper’s thoroughness in combining ideas that support the description, analysis, design and evaluation of situated uses of IMs.