Publications

The integration of artificial intelligence (AI) into healthcare decision-making holds promise for improving equity and transparency, particularly in organ transplantation. This paper examines the practical and ethical implications of applying AI to the kidney transplant donor supply chain, with specific attention to limitations in existing data collection practices. Although algorithmic matching systems are used to rank donor–recipient pairs, current datasets often lack systematic documentation of physicians’ rationales for deviating from standard allocation rankings. These undocumented “rank-jumping” decisions introduce opacity into the process and may unintentionally perpetuate inequities in access to transplantation.

   The central argument of this paper is that greater data transparency and granularity regarding physician decision-making can improve both the fairness and effectiveness of AI-supported allocation systems. By incorporating interpretable representations of clinical judgment and contextual factors into algorithmic models, AI systems can better support consistent, accountable decision-making while preserving necessary human oversight. Where access to comprehensive historical data is limited, simulation and synthetic data approaches are proposed to explore how data completeness and model design influence allocation outcomes.

   The paper also considers the broader ethical implications of AI-enabled organ allocation, including issues of privacy, autonomy, and distributive justice. By focusing on the kidney transplant supply chain, this research contributes actionable insights into how AI can be responsibly deployed to reduce health disparities while maintaining ethical integrity in life-critical healthcare processes.

Economic crises often reveal long-standing institutional tensions rather than create them, and the COVID-19 pandemic was no exception. This article examines how disruptions to labor markets and global supply chains exposed structural vulnerabilities that had accumulated over decades through technological change, demographic contraction, and institutional choices favoring efficiency over resilience. Drawing on institutional economics, the analysis situates recent labor shortages, accelerated automation, platform-mediated employment, and supply chain fragility within a longer historical trajectory linking technological specialization, organizational governance, and the distribution of rights and power. The article integrates classical and contemporary perspectives on task displacement, labor valuation, and supply chain coordination to show how demographic aging, opaque information systems, and concentrated production networks magnified crisis impacts. Rather than treating the pandemic as a singular shock, the paper argues that it functioned as a revealing moment that rendered visible the fragility of inherited institutional arrangements. Understanding what follows the pandemic therefore requires attention to the institutional structures shaping labor and supply chains, not episodic crisis management. The analysis highlights how institutional redesign, rather than technological capability alone, will shape the evolution of economic organization beyond the pandemic.

Artificial intelligence is increasingly embedded in learning environments, shaping how learners access information, receive feedback, and engage in problem-solving. While AI-enhanced systems promise efficiency and personalization, they also risk encouraging cognitive offloading if design choices prioritize automation over engagement. This paper examines how AI-mediated learning environments can be intentionally designed to foster critical thinking by balancing pedagogical structure, heutagogical autonomy, and cognitive integrity.

   Pedagogy emphasizes structured guidance and sequencing, while heutagogy emphasizes learner self-determination and agency. In AI-enhanced contexts, this tension becomes a design challenge rather than a purely instructional choice. Drawing on learning sciences and neuroscience, this paper introduces cognitive integrity as a design criterion that preserves learners’ responsibility for reasoning, reflection, and judgment.

   The study presents a design framework operationalizing these concepts and applies it to multiple course implementations using an AI-supported learning platform across undergraduate and graduate contexts, including a graduate-level Strategic Sourcing course completed in Fall 2025. Preliminary observations focus on learner interaction patterns, including hint usage, revision behavior, persistence under challenge, and reflective engagement.

   The findings suggest that AI systems designed with optional, graduated support; delayed feedback; and revision-oriented interactions can support productive struggle and learner agency without undermining cognitive engagement. The paper concludes with design implications for AI-enhanced learning systems, positioning critical thinking as a design outcome rather than an assumed learner trait.

Robert A. Heinlein did not simply tell stories; he built institutional worlds. His speculative societies expose how power, scarcity, technology, and culture collide to structure economic life, revealing institutional dynamics that continue to shape contemporary debates about governance and social resilience. This analysis situates Heinlein’s narrative experiments within the institutional frameworks developed by Veblen, Commons, Galbraith, Ayres, and Ostrom. Four recurring institutional world types emerge across the corpus: technocratic or militarized orders, frontier libertarian societies, utopian engineer systems, and crisis polities. Each world type reveals distinct mechanisms through which institutions structure rights, obligations, and coordination, and through which legitimacy erodes or is reproduced under stress. By contextualizing Heinlein’s fictional yet conceptually coherent societies within institutional theory, this article offers a creative perspective on how speculative narratives can illuminate the dynamics of power, legitimacy, scarcity, and technological change. These insights contribute to contemporary debates on governance, accountability, and institutional resilience.

Artificial intelligence now sits at the center of both work and learning, challenging educators to strengthen, not surrender, human cognition. This paper links economic change, cognitive neuroscience, and educational pedagogy to show how critical thinking becomes the decisive learning outcome in an AI age. Drawing from research on memory, feedback, and reinforcement, it then outlines how knowledge is built, not merely accessed. These principles are then applied in redesigning an undergraduate analytics course using an AI platform to individualize challenge and feedback. The result is a model for teaching that uses AI to cultivate reasoning, adaptability, and ethical awareness.

The ‘silver tsunami’ of Baby Boomers suggests that the United States will increasingly face a declining number of heirs for entrepreneurs seeking to retire.  Unmet, this challenge risks acquisition or outright closure of firms, potentially disrupting continuity of production, supply, and local community employment.  This demographic shift is multifactor, with falling family sizes compounding retirement of Baby Boomers.  Yet, an aging workforce and population decline give opportunity to increase the share of employee-owned firms in the U.S., such as employee stock ownership plans.  Closing their business or selling to outside interests risks workforce reductions, outright relocation, loss of local goods and services, and jeopardizes tax revenue.  Alternatively, retiring owners can sell the going concern to their existing employees, with potential to defer taxes.  This essay asks three questions in exploring these issues.  First, how do employee-owned firms typically perform?  Second, why do we see so few employee-owned firms?  And third, will the ‘silver tsunami’ proliferate employee-owned firms?

Covid-19 supply chain disruptions clearly illustrated deficiencies in central coordination.  Meaningful improvement in central coordination of supply chains will require transparency into resource stocks and flows.  Latest technology, like 5G, blockchain and IoT, are primed to provide this transparency for collaboration during pandemics.  This will improve agility and service, reduce inventory and enable reverse logistics benefits.  Furthermore, transparent global networks can allow more inclusive and equitable distribution of critical supply, yielding quicker resolution to pandemics.  However, many challenges exist that portend to delay the adoption of a holistic and transparent digitalized supply chain.  This paper explores the most recent pandemic with attention to the limiting factors at all levels of emergent global crisis response.

Counterfeit products cause financial losses for both the Manufacturer and the end-user, e.g., fake foods and medicines pose significant risks to the public’s health. Moreover, it is challenging to bring trust into the product’s supply chain, preventing counterfeit goods from being distributed throughout the network. However, fake product detection 
methods are expensive and need to be more scalable, whereas a unified traceability system for the packaged product is not available. Therefore, this research proposes a product traceability system, named Trusted Traceability Service (TTS), using Blockchain and Self-Sovereign Identity (SSI). TTS can be incorporated across diverse industries because of its generic and manageable four-layer product packaging strategy. Blockchain-enabled SSI empowers the distributed nodes to verify them without a centralized client-server authorization architecture. Moreover, due to its distributed nature, the proposed TTS framework is scalable and robust with the combination of web3.0 distributed application development. The adoption of Fantom, a public blockchain infrastructure, allows the proposed system to handle thousands of successful transactions more cost-effectively than the Ethereum network. The deployment of the proposed framework in both public and private blockchain networks demonstrates its superiority in execution time and the number of successful transactions.

The critical materials supply chain has yet to reckon with negative externalities from extraction and production of the building blocks for green energy and exponential growth in computing. Organizations are heavily investing in foreign direct investment in offshore sourcing of these critical natural resources. These geographically, hyper-concentrated supply chains have all been in the making for decades and there are on-going geo-political concerns on this in Africa with rare earth minerals and Asia with semiconductor fabrication. Public policy efforts through government regulation and financial incentives driving transition to green energy have to date largely ignored the challenges of operationalizing institutional adjustments in the supply chain of critical materials. For example, driving demand for battery electric vehicles has resulted in cobalt sourced in predatory, unethical, ecologically damaging fashion in the Democratic Republic of Congo, frequently with child labor, and without fostering or supporting domestic sources of same. Supply chain economics is the systematic study of the production, distribution, and consumption of goods, services, and capital by networks of organizations, internal and external, with which any given organization pursues its goals and objectives. As such, labor and its social reproduction cannot be de-coupled from critical material supply chains. Compounding the challenges to re-establishing domestic sources of these strategic materials are our aging workforce, declining birth rates, promotion of 4-year degrees, stigmatization of blue-collar work, and wage and housing inflation. Workforce development, including affordable housing, is integral to re-shoring extraction and production activities and establishing domestic supply of these critical material supply chains.

Exponential acceleration of technological change is a hallmark of industrial revolutions. Each occurrence forces disruptive institutional adjustments, with increasing capital intensity, de-skilling of existing labor, while also providing expansive entrepreneurial opportunities. Institutional adjustments during the (First) Industrial Revolution took
centuries to fully manifest. With Industry 4.0, similar changes occur in less than a decade. Artificial intelligence (AI) has already demonstrated its ability to disrupt in mere months. Of particular concern is the tendency for automation to displace the lowest-skilled workers, given predominantly routine tasks. When entry-level jobs disappear, how will tomorrow’s workforce gain the critical experience to manage and lead? Yet whether this technological disruption renders dystopia or utopia is still within our grasp. While products, services, and organizations will increasingly be built digitized from day one, AI will continue to struggle with context and risk. And therein lies a clear role for humanity, as AI requires homo economicus to describe and recognize good ideas. Human ability to contextualize is key to expediting decision trees and arriving at ethical judgments. When humans encounter novel ethical dilemmas, they process them through their moral frameworks. We must equip workers with critical thinking skills to employ AI ethically, minimizing negative externalities.