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The Cost of Inaccessibility: Retail Discrimination and Mobility-Constrained Consumers
Get PDFThis study investigates whether individuals with mobility-related health conditions face systematic disadvantages in retail pricing, both in physical stores and on online platforms. While price discrimination is a common feature of modern markets, limited attention has been paid to how physical mobility constraints may affect consumers' ability to access lower prices or respond to promotions. Using matched observational data from the NielsenIQ and the Open E-Commerce dataset, we compare purchasing behavior and price outcomes between consumers with and without mobility-related health conditions. We find that mobility-constrained individuals pay modestly higher prices in physical stores and face even larger price disparities in online purchases, particularly among wheelchair users. Our findings highlight mobility as an underexamined axis of vulnerability in consumer markets. The results have implications for the design of retail pricing systems and digital platforms, as well as for policy efforts aimed at improving equity in access to essential goods
Sweeping Up Digital Dirt: Navigating Immediate Working Experiences to Alleviate the Precarity of Online Crowd Work Conditions
Get PDFThe increasing use of artificial intelligence (AI) systems across sectors relies on online crowd work to generate, label, and curate the large datasets required for AI functionality. However, this form of labor is frequently conducted under precarious and ethically contentious conditions. While these systemic issues have received scholarly attention, little empirical research explores their psychological impact on workers. This study applies the Psychology of Working Theory (PWT) to examine how the satisfaction of core psychological needs, i.e., autonomy and belongingness, shapes work enjoyment and frustration among 1,291 European crowd workers engaged in such AI-related tasks. Findings indicate that autonomy and belongingness play a critical role in fostering positive crowd work experiences and enabling psychological resilience in the face of adverse conditions. This research contributes to understanding how AI is reshaping labor and offers theoretical and practical guidance for the ethical and human-centered design of AI-supported work system
AI Teammates: Silverbacks, Quarterbacks or Knick-Knacks? The Effect of AI Teammates on Humans’ Status Perceptions and Intention to Collaborate
Get PDFArtificial Intelligence (AI) based teammates are being used by organizations to enhance working teams. Research on the impacts that such AI teammates have on their human coworkers is still developing. This study explores whether and how the formation of human-AI teams (HATs) impacts humans’ intra-group status perceptions and their intention to collaborate with a new AI teammate. We propose that AI teammates will be attributed a status based on high capability and low prosocial behavior perceptions. Further, human incumbents, who perceive their own status to be based on competence and integrity, will exhibit zero-sum beliefs that negatively influence their collaboration intention with the new AI teammate, while prosocial oriented incumbents will rather exhibit increased levels of collaboration intention with the AI teammate. Hypotheses will be tested with data gathered via a 2 by 2 between-subject experiment. We expect to enhance the understanding of human-AI collaboration and the effects of status dynamics within HATs
Gamifying Climate Change: How Climate-Oriented Boosts Influence Strategy in a Virtual Reality Common Pool Resource Game
Get PDFDespite acknowledging that climate crisis is a global emergency, human attempts to curb emissions are still insufficient. This partially stems from unprecedented nature of this threat which looms large but for many is relatively distant on a temporal and social horizon. Participants in this study engaged in an incentivized common-pool resource game within an immersive virtual reality (VR) environment. We compare participants behavior in VR game in two conditions in which either additional environmental cues and interventions are present (experimental) or not (control). Both quantitative and qualitative results from the experiment provide clear evidence of interventions impact on resource management strategy. Participants in the experimental condition reported enhanced solidarity with other players and natural environment. Over the course of the game they shifted their initial strategy to more sustainable, even in the face of clear self-interest to maximize own resource intake
AI Application in Semiconductor Manufacturing: A Patent-driven Approach
Get PDFAs semiconductor structures continue to shrink, semiconductor manufacturers are facing increasing challenges, such as yield degradation, process complexity, and rising production costs. While artificial intelligence (AI) is expected to improve profitability, limited research has systematically investigated how AI is actually being used in this context. Therefore, this study explores the current application of AI technologies in semiconductor manufacturing using patent data and identifies promising application areas. A technology-function matrix analysis reveals that AI is primarily applied to process control and defect detection through image analysis. Additionally, a generalized linear mixed model (GLMM) analysis shows that technologies related to error correction, scheduling, and advanced metrology have recently demonstrated high growth rates. These findings offer practical implications for managers and practitioners seeking to leverage AI in semiconductor manufacturing
Beyond Screens: A Family-Centered, Unplugged, Gamified Intervention to Support Social Skills in Autistic Children
Get PDFGamification has recently been used to improve social skills among autistic children. However, barriers related to screen dependence and unequal access to devices and connectivity, particularly in low-resource settings, limit its suitability. We address these barriers by proposing a set of unplugged, gamified flashcards designed to support family engagement in promoting social skills among autistic children. The flashcards were designed through a multimodal study structured into four steps: (i) systematic literature review, (ii) expert brainstorming, (iii) prototype design, and (iv) interview with a family of autistic children. The study delivered a set of flashcards with a trail-shaped board to support families in activities that promote social skills in autistic children. We contribute a practical, low-cost tool and a replicable design path for mental health and gamification research and practice, focusing on everyday, family-led communication. Primary users are families with the autistic child, alongside therapists and teachers in clinical and school contexts
Empowering Boards through Continuous, Empirically Generated Cybersecurity Patterns Using a Criminological-informed Analytical Tool
Get PDFAs boards face increasing regulatory pressure to strengthen organizational cybersecurity, there is a need for methods that support evidence-based, informed decision-making. By analyzing cyber risks from a criminology perspective, this study addresses the challenges boards face in managing cyber threats. Using Crime Script Analysis and Situational Crime Prevention, we systematically examined Singapore’s regulatory data breach reports to identify cybersecurity patterns, describe them in a business process context, and provide actionable guidance aligned with ISO/IEC 27002:2022 controls. We explored the automation of this approach using large language models and manually analyzed the result, identifying the top three patterns and visualizing them in a dashboard: Authentication deficiencies, Supplier management risks, and Access control failures. Despite frequent attention, these areas remain ineffectively addressed in practice. This study also offers research opportunities in the continuous generation of empirical cybersecurity insights, assisting boards to facilitate better cybersecurity management in line with regulatory and compliance responsibilities
Carbon Taxation in an Imperfectly Competitive Power Sector
Get PDFPower-sector decarbonisation necessitates variable renewable energy (VRE), viz., wind and solar. VRE's intermittency could be balanced by energy storage and fossil-fuelled generation. However, flexible plants may enjoy enhanced leverage, and carbon policy will have to adapt to mitigate economic and environmental distortions. Absent market power and storage inefficiency, the optimal carbon tax on fossil-fuelled generation equals its marginal cost of damage (MCD) from emissions. Departures from this idealised setting require reflecting an imperfectly competitive industry's attributes. In particular, a bi-level framework in which a policymaker anticipates industry’s Nash-Cournot equilibria distils a carbon tax that is lower (higher) than the MCD if fossil-fuelled generation (hydro storage) exerts market power. Intuitively, a fossil-fuelled plant (hydro storage) withholds output (conducts temporal arbitrage) to manipulate electricity prices, which alleviates (exacerbates) the environmental impact. We solve our problem instances by reformulating the bi-level model as a mathematical program with primal and dual constraints (MPPDC)
MAEBE: Multi-Agent Emergent Behavior Framework
Get PDFExplainability in evaluations of isolated large language models (LLMs) likely does not transfer to multi-agent AI ensembles (MAS), as MAS introduce novel emergent agent interaction and decision-making behaviors. To systematically assess differences in decision behaviors between isolated and ensemble agents, we present the Multi-Agent Emergent Behavior Evaluation (MAEBE) framework. Using MAEBE with the Greatest Good Benchmark, a double-inversion question technique, and explainability analysis, we demonstrate that: (1) Robustness of decision preferences is substantially brittle in MAS LLM ensembles similarly as in isolated LLMs, as preferences shift significantly with changes to question framing. (2) Ensemble behavior is not directly predictable from isolated agent behavior due to emergent group dynamics. (3) Specifically, ensembles exhibit phenomena like peer pressure influencing decision convergence, even when guided by a supervisor. Our findings underscore the value and necessity of evaluating explainability of multi-agent AI systems in their interactive context to properly assess results generated by MAS, with potential implications for AI safety and alignment
