How Emerging Technologies Transform Value Creation: A Multi-Level Analysis of the Metaverse and Artificial Intelligence

Emerging technologies are reshaping the way how organizations create, deliver, and capture value. Especially, two technological developments stood out in recent years: The metaverse and artificial intelligence (AI). On the one hand, the metaverse is evolving as a socio-technical ecosystem, blending the virtual and physical world to immersive, persistent digital environments. On the other hand, artificial intelligence—as a general-purpose, data-driven technology— structures coordination and decision-making, and lately, with the rise of large language models (LLMs) like ChatGPT, increasingly generates new content, such as text, video, or audio. Both developments support humans already in their private life (e.g., the metaverse as shared, immersive venue for live co-experiences—attending a virtual concert with friends from all over the world—and AI for recommendations of films based on own preferences) and professional life (e.g., the metaverse for immersive, interactive high-risk training scenarios and AI for creating analyses to support decision making or even taking leadership tasks). Although technologies have always arrived in successive waves, the metaverse and AI have brought an unusually rapid pairing of attention and capability. Public and scholarly interest in the metaverse surged after Facebook's rebranding to Meta in 2021, with investments worth billions from the world’s leading technology companies, including NVIDIA, Microsoft, Snapchat, and Amazon. The situation is similar for AI—especially generative AI—with the release of ChatGPT in 2022 by OpenAI, where nearly the same providers as Microsoft or Google now embed generative AI at the core of their offerings, while a parallel wave of platforms natively built around generative capabilities is emerging. Therefore, organizations, groups, and individuals have recognized the potential for fundamental changes in how people live, work, and interact with these technological advancements and, vice versa, how the technologies change people. In contrast to earlier technological milestones, the metaverse and AI both fundamentally transform collaboration—the metaverse as a new collaboration and acting environment, and AI as a collaboration partner or even leader. Together, the metaverse and AI shift attention beyond efficiency gains toward more profound questions of value creation and knowledge acquisition, task organization and execution, authority and responsibility distribution, and collaboration, while enabling new ways of working that open avenues for novel value propositions, business model redesign and innovation, and scalable capability development. But many organizational initiatives cannot unfold their full potential because a diverse set of challenges comes with the possibilities of technological innovations, such as organizational entry conditions being misjudged, design options being poorly specified, and evaluation focusing on narrow efficiency gains rather than sustained value. For the metaverse, teams struggle to convert the technological potential of immersive, digitally mediated environments into effective collaboration, while AI systems raise questions of transparency, contestability, leadership, and the preservation of human judgment. For individuals, training and its adoption hinge not only on performance but also on affect and perceived efficacy. The central question is therefore not whether the metaverse and AI matter, but how they should be designed, integrated, and governed so that their potential translates into durable outcomes. Thus, this dissertation provides guidance to practitioners and researchers through a multi-level analysis for designing, integrating, and governing the metaverse as an environment and AI as a technology across organizational, team, and individual levels, including initiating and orchestrating metaverse ventures and business models, using AI for collaboration and leadership, and evaluating measurement practices that capture value and learning across hybrid journeys. At the organizational level, this dissertation develops a coherent scaffold for engaging the metaverse as an environment and aligning it with long-term strategy and resource allocation. The first study adopts an organizational readiness perspective, grounded in the technology–organization–environment (TOE) framework, and structures the pre-adoption gate by clarifying technological preconditions, organizational enablers, and environmental contingencies. Readiness is positioned as a decision discipline rather than a checklist—an entry architecture that helps organizations stage investments, pace uncertainty, and connect early exploration to strategic intent. Building on the readiness lens articulated above, the second study advances from assessing entry conditions to configuring engagement, formalizing a taxonomy of metaverse-driven business models and archetypes, based on the analysis of 100 real-world companies from Crunchbase. The taxonomy delineates the core design space, allowing configurations to become comparable and discussable across various contexts. By articulating foundational dimensions—such as value proposition, key offering, degree of immersion, inter-world interaction, ecosystem partnering, or target group—the taxonomy provides a common language for design choices and various manifestations. It supports portfolio thinking (what to build, buy, or partner), reduces category errors when assessing opportunities, and enables benchmarking against recognizable archetypal patterns without presuming a single dominant model. Taking the taxonomy as a blueprint for configuration, the third study defines the evaluative architecture by adapting cross-channel funnel metrics to capture value creation in and through the metaverse. A measurement logic for hybrid journeys adapts the marketing funnel thinking to pathways that traverse physical, online, and virtual touchpoints. Rather than treating the metaverse as a promotional add-on, the framework aligns stage-appropriate objectives and indicators—from awareness to consideration, conversion, and customer loyalty—while acknowledging cross-channel spillovers and attribution. This logic makes value realization observable and comparable over time, enabling stronger handoffs between virtual and physical world experiences and adjacent channels. With evaluation presented, the fourth study advances to orchestrating resources, using a dynamic capabilities perspective to align experimentation, ecosystem positioning, and continuous renewal. A dynamic capabilities lens situates metaverse initiatives within iterative cycles of sensing, seizing, and transforming. Drawing on a systematic literature review and 21 expert interviews across industries, the study articulates 13 microfoundations that structure the development, implementation, and ongoing adaptation of metaverse-driven business models and ventures. Positioned at the intersection of strategic management and digital innovation, these contributions offer practice-oriented guidance for the design and realization. Together, these studies highlight a coherent progression from disciplined initiation to configuration, evaluation, and orchestration of metaverse engagements—structuring entry through a TOE-based readiness lens, organizing design choices via a shared business-model taxonomy, evidencing value with hybrid-journey metrics, and sustaining adaptive scaling through dynamic-capabilities microfoundations— thereby aligning the metaverse as an environment with long-term strategy, governance, and resource allocation. At the team level, this dissertation views collaboration as being constituted by environment and technology, rather than merely supported by tools. The first study provides a structured synthesis of avatar-mediated effects by organizing prior work in the form of a structured literature review on the Proteus Effect. Related mechanisms offer a lens for design, distinguishing between behavioral and attitudinal outcomes and translating avatar attributes—such as morphology, attire, and status cues—into actionable parameters for equitable participation, prosocial interaction, and attention alignment, with safeguards against stereotype reinforcement and objectification. The second study employs a design science research approach, based on eleven expert interviews, to address the challenges of virtual team collaboration and build upon these requirements, incorporating metaverse-specific nudges to enhance virtual collaboration within the metaverse. In parallel, the third study shifts its focus from the environment to technology, with AI as team member in the form of a leader. Using a design science methodology, an AI leadership agent is instantiated, grounded in the “characteristics of effective virtual-team leaders” as a kernel theory. The artifact was iteratively refined across three design cycles, drawing on expert interviews with virtual team members (N=24) and focus-group discussions (N=28) involving virtual team members and AI specialists. The findings suggest that AI leadership can assume bounded leadership functions, indicating a reconfiguration of leadership roles with significant implications for the future of work. Together, these studies highlight that virtual collaboration can—and must—be intentionally engineered, with avatar representation and spatial configuration serving as design levers for equitable participation and social presence. Metaverse-specific nudges align attention and restore nonverbal signaling, while AI leadership is instantiated as configurable and transparent routines that coordinate work. At the individual level, this dissertation examines how immersion and generative AI assistance reshape learning and everyday work. The first study conducted a within-subject lab experiment with 30 participants. It isolates immersion as a design lever by comparing the same training task across real, metaverse, and conventional computer environments, demonstrating that immersive settings can be deployed without increasing mental burden and are particularly suitable when scenarios in the physical world are hazardous, costly, or impractical. Extending this foundation, the second study reframes immersive training through a human capital lens, positioning the metaverse as a scalable environment that supports training as a strategic mechanism for building, maintaining, and leveraging human capital. Building on the experiment insights and moving from task-induced requirement lens of mental load to the investment side of cognitive effort, a third study assesses subjective and objective performance—error rates, completion time, and delayed recall—together with perceived cognitive effort, feelings, and enjoyment within an adapted cognitive-fit and task-technology-fit framework, in the context of training real-world assembly tasks. Metaverse-based training offers greater entertainment value without requiring greater cognitive effort. A finding of practical significance because sustained engagement often determines whether upskilling efforts persist over longer horizons. Shifting from environment to technology, a fourth study conducted an online within-subjects experiment with 161 Python programmers, comparing task performance and positive/negative affects across two programming tasks completed with and without ChatGPT assistance. The results show that ChatGPT improves performance and that higher positive affect is associated with better task outcomes. Assistance improves task performance, but adoption is not explained solely by utility; positive affect and emotions are central to continued use and learning, indicating that human–AI collaboration is best understood as an affect-conditioned process. Together, these studies highlight that individual outcomes hinge on more than efficiency: immersive environments enable interactive, repeatable practice without adding mental load or cognitive load, and AI support raises performance, but durable adoption and capability growth depend on affect, meaning, and feelings—design priorities that must sit alongside key AI characteristics, such as accuracy, traceability, and accountability. Taken together, these studies show that treating the metaverse as an ecosystem environment and AI as a technology enables new ways of value creation across organizational, team, and individual levels. At the organizational level, they articulate a coherent architecture that links readiness, business model configuration, value measurement, and dynamic orchestration. At the team level, they specify design levers and metaverse-specific nudges for avatar-mediated virtual team collaboration and formalize AI leadership as a governed and configurable capability for coordination and decision support. At the individual level they show immersive training in the metaverse as a viable mode for capability development within the context of long-term human capital development and the affect-conditioned dynamics of sustained adoption and work with AI. The result is a practical pathway that aligns design and governance with strategy and human needs, translating technological promise into durable, measurable outcomes.