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Appreciative inquiry is an approach to action research that intends to create knowledge for social innovation. Such knowledge has the generative capacity to interrupt habitual practice and to create an inspiring sense of possibility that energizes novel action. How can appreciative inquiry live up to this promise? The premise of this chapter is that we need to better understand the generative qualities of inquiry in the appreciative/inquiry equation. What is the nature of inquiry that has generativity at its core? The chapter describes five distinct, yet interrelated approaches that enhance the generative process of inquiry. They depict generativity as a dynamic interplay of open-endedness and connectedness. How can the five dimensions of generativity advance appreciative inquiry as a scholarship of transformation? The last section of the chapter gives some suggestions for such possible enrichment. We need audacious forms of scholarship for the creation of a more just and sustainable global society. Appreciative inquiry as a generative process is well positioned to take on that role.

This chapter introduces two core notions from Niklas Luhmann’s social systems theory to paradox studies. Specifically, it offers the notions of decision paradox and deparadoxization as potential generative theoretical devices for paradox scholars. Drawing on these devices, the paper shifts focus to the everyday and mundane nature of decision paradox and the important role of deparadoxization (i.e., generating latency) in working through paradox. This contribution comes at a critical juncture for paradox scholarship, which has begun to converge around core theories, by opening up additional and possibly alternative theoretical pathways for understanding paradox. These ideas respond to recent calls in the literature to widen our theoretical repertoire and align scholarship more closely with the rich, pluralistic traditions of paradox studies.

Following the recent rise in generative artificial intelligence (GenAI) tools, fundamental questions about their wider impacts have started to reverberate around various disciplines. This study aims to track the unfolding landscape of general issues surrounding GenAI tools and to elucidate the specific opportunities and limitations of these tools as part of the technology-assisted enhancement of mechanical engineering education and professional practices.

As part of the investigation, the authors conduct and present a brief scientometric analysis of recently published studies to unravel the emerging trend on the subject matter. Furthermore, experimentation was done with selected GenAI tools (Bard, ChatGPT, DALL.E and 3DGPT) for mechanical engineering-related tasks.

The study identified several pedagogical and professional opportunities and guidelines for deploying GenAI tools in mechanical engineering. Besides, the study highlights some pitfalls of GenAI tools for analytical reasoning tasks (e.g., subtle errors in computation involving unit conversions) and sketching/image generation tasks (e.g., poor demonstration of symmetry).

To the best of the authors’ knowledge, this study presents the first thorough assessment of the potential of GenAI from the lens of the mechanical engineering field. Combining scientometric analysis, experimentation and pedagogical insights, the study provides a unique focus on the implications of GenAI tools for material selection/discovery in product design, manufacturing troubleshooting, technical documentation and product positioning, among others.

The development of capacities for sustainability thinking by large-scale social systems requires of the skilled practitioner complex abstract logics. There is an intricate complexity of relationships to consider in the observation of the landscape of thought. This paper aims to introduce a matrix as an orientating heuristic to guide this form of praxis and provides reflections on its use in the enactment of social learning.

The signifiers for four different orientations to learning and four abstractions in levels of learning are described. The configurations that result from their conjunction generate a matrix of 16 alternatives. These are combined into a heuristic to guide and inform reflexive praxis.

The conjunction of these two dimensions enables observations of the cybernetic interactions between levels of learning and orientations to learning. This depiction prompts considerations of the ethics and aesthetics of large-scale collaborative learning. In reflecting on the use of the heuristic device in practice, four primary observations are offered as possible considerations to inform ethical practice.

The value of this research is in enabling awareness of the relationship between levels of learning and orientations to learning. The originality is the application of apithology principles to the multi-dimensional learning landscapes found in complex thought-ecologies.

This chapter reviews three analytical perspectives – ‘structural’, ‘network’ and ‘cultural’ – on the study of power and their implications for theorizing elites. It builds on this initial theoretical review by developing a critical realist approach to the study of organizational elites out of the structurally based perspective identified in the first section of the chapter. The explanatory potential of this critical realist approach is then illustrated through two case studies of ruling elites embedded in contrasting historical, political and social contexts. The final section of the chapter provides a discussion of the wider implications of these case study analyses for understanding and explaining the ‘new feudalism’ which is emerging in advanced political economies and societies.

This paper aims to offer an approach to cyborg composing with artificial intelligence (AI). The author posits that the hybridity of the cyborg, which amalgamates human and artificial elements, invites a cascade of creative and emancipatory possibilities. The author critically examines the biases embedded in AI systems while gesturing toward the generative potential of AI–human entanglements. Drawing on Bakhtinian theories of dialogism, the author contends that crafting found poetry with AI could inspire writers to problematize the ideologies embedded into the corpus while teasing apart its elisions or contradictions, sparking new forms of expression at the interface of the organic and the artificial.

To illustrate this approach to human–AI composing, the author shares a found poem that she wrote using ChatGPT alongside her reflection on the poem. The author reflects on her positionality as well as the positionality of her artificial interlocutor, interrogating the notion of subjectivity in relation to Bakhtinian dialogism and multivocality.

Weaving tales of resilience in harmony or tension with AI could unravel threads of possibility as human writers enrich, deepen or complicate AI-generated texts. By composing with AI, writers can resist closure, infiltrate illusions of objectivity and “speak back” to AI and the dominant voices replicated in its systems.

By encouraging students to critically engage with, question and complicate AI-generated texts, one can open avenues for alternative ways of thinking and writing, inspiring students to imagine and compose speculative futures. Ultimately, in animating assemblages of the organic and the artificial, one can invite transformative possibilities of being and becoming.

Most recent C-suite surveying suggests current applications of generative AI, although hyped, are fragmented and unlikely to yield major financial returns anticipated. Instead, business leaders expect major value from generative AI will be achieved through application of generative AI to innovation: operational innovation, product and service innovation, and most elusive of all, business model innovation.

Findings and analysis presented draws on data from several surveys of C-level executives conducted by IBM Institute for Business Value in collaboration with Oxford Economics during 2023. Each survey focused on the potential of generative AI in a particular business area. The n-count of each survey ranged from 100-3000.

1. Business leaders expect generative AI to build on returns achieved from investments in traditional AI, with 10 percent RoI expected on generative AI investments by 2025. 2. Executives anticipate that generative AI will have most impact when implemented to expand innovation. 3. Specific examples provided for operational innovation, product innovation, and business model innovation

We are still very early in the generative AI development cycle. We have made best efforts to project, but only time will tell for sure.

Business application of generative AI are extremely fragmented. Despite the desire to throw investments at the wall to see what sticks, it is important that leaders take a structured approach to generative AI, focusing on RoI from innovation investments.

To alleviate negative impacts of generative AI, focusing on innovation potential and value maximization is crucial.

This research is based on completely new surveying and data. This papers adds to the sum total of new knowledge in the generative AI domain.

Since the release of ChatGPT by OpenAI in November 2022 – with its ability to create compelling, relevant content, new large language model (LLM) technology – business leaders, especially CEOs, are being pressured to accelerate new generative AI investments. IBM IBV surveyed executives to assess their progress and concerns and their adoption strategies.

Adoption of generative AI is still in its very early stages. Most organizations are only beginning to figure out how and where to make use of it. In fact, as few as 6 percent of executives in new surveying conducted by the IBM Institute for Business Value say they are operating generative AI in their enterprise today.

In contrast to many peoples’ expectations about AI, automating tasks is not the top priority for executives looking to tap generative AI to grow business value. Looking at benefits by function, research and innovation is the primary area where organizations see opportunities for generative AI.

IBM IBV's recent survey of executives found that the key barriers to the effective deployment and use of generative AI are linked to security, privacy, ethics, regulations and economics – not access to the underlying technology itself.

Organizations will have to evaluate where in their enterprise the potential gains and cost efficiencies outweigh the risks of possible errors or unintended consequences from the use of generative AI along with broader ethical considerations. Ecosystems expand generative AI opportunities to harness data, insights and technology capabilities from across partners and stakeholders while enabling control over the capabilities that are most central to an organization’s value proposition.

This commentary discusses the value of generative artificial intelligence (AI) for qualitative research in phygital settings to understand the customer experience.

The critical and logical analysis is based on current knowledge of generative AI.

Generative AI seems very useful for qualitative research in phygital settings to understand the customer experience and should be used in qualitative research projects. Generative AI can provide much-needed validation of the subjective nature of qualitative research and can also generate insights beyond human intuition.

The study is based on current technology, which changes fast. In the future, the skills of qualitative researchers may become outdated, relegating them to the role of prompt engineers.

Technology, and especially generative AI, will be a key tool for practitioners as they conduct practical research.

Qualitative researchers should overcome potential anti-technology speciesism and embrace the potential of generative AI.

This commentary provides insights into the role of generative AI for qualitative research in phygital settings.

Generativity is defined in this chapter as the creation of new images, metaphors, physical representations, and so on that have two qualities: they change how people think so that new options for decisions and/or actions become available to them, and they are compelling images that people want to act on. Research and experiences that suggest “positivity,” particularly positive emotion, is not sufficient for transformational change, but that generativity is a key change lever in cases of transformational change, are reviewed. A model of different characteristics of generativity is offered and ways in which appreciative inquiry can be a generative process, increase generative capacity, and lead to generative outcomes, are discussed. Ways to increase the generativity of appreciative inquiry through generative topics, generative questions, generative conversations, and generative action are offered.