AI, copyright, and business: navigating global legal challenges in the era of generative content and digital replicas
Kyle Nash, Maria Kalyvaki & Heather McIntosh
International Journal of Public Administration, 1-15

Artificial intelligence (AI) is revolutionizing content creation, raising  significant questions about copyright, authorship, and intellectual property. The integration of AI into creative industries brings both opportunities and risks, as companies need to balance  innovation with legal compliance. The advent of technologies like DALL-E, Stable Diffusion, and Midjourney has sparked legal debates over the ownership and control of AI-generated content. Traditional copyright laws, designed for human creators, struggle to address these emerging challenges. This paper examines the legal ambiguities surrounding AI-generated works, including whether machines can be considered creators or if ownership should lie with the developers or users directing them. It also explores potential copyright reforms that could resolve these issues and considers how businesses must adapt to comply with evolving intellectual property regulations. By analyzing key legal precedents and global responses, this paper presents a comprehensive framework for addressing the legal and business challenges posed by AI-generated content.

Mapping the Pathways: Stigmergy Theory as a Lens for Analyzing e-Participation
K Nash, R Wakefield.
International Journal of Public Administration, 1-15

This study explores drivers of e-participation using archival data from 145 countries (2012–2020), guided by stigmergy theory. Results show that adult literacy, ICT-based government services, and household Internet access positively influence e-participation. However, Internet access does not sustain long-term global growth. Literacy promotes Internet access but does not impact basic services’ growth, potentially excluding low-literacy populations. Countries with higher literacy and basic services see slower growth, suggesting diminishing returns. These findings emphasize that factors influencing e-participation evolve with development, offering insights for public administrations to create inclusive strategies for sustainable digital engagement.

Exploring the impact of self-concept and IT identity on social media influencers’ behavior: A focus on young adult technology features utilization
K Nash.
International Journal of Human–Computer Interaction 40 (22), 6941-6952

This research investigates the inspiration of self-concept and Information Technology (IT) identity on the behavior of Social Media Influencers (SMI), specifically regarding their utilization of technology features. With the advent of social media, individuals have been presented with a unique opportunity to showcase their creativity and connect with a broader audience, known as “followers.” Drawing upon the unified theory of acceptance and use of technology (UTAUT), this study examines the relationship between SMIs behavior in utilizing technology features, self-concept, and IT identity within the context of social media platform usage. The research methodology employed in this study is partial least squares (PLS) regression, providing a comprehensive understanding of how individuals’ self-concept shapes their IT identity concerning the usage of social media platform features. The findings of this study have significant implications for businesses seeking to engage with SMIs and individuals aiming to establish their presence on social media. The research highlights TikTok’s potential as a platform for self-expression and personal brand development, underscoring the importance of self-concept and IT identity in the realm of SMIs.

Exploring the Dynamics of Career Events in Education: A Study Using Activity Theory.
K Nash, M Kalyvaki
Journal of Higher Education Theory & Practice 24 (11)

Numerous educational institutions employ career events (CEs) to enhance students’ overall learning experiences, but further research is required to optimize these events. Utilizing activity theory, this study seeks to systematically investigate the interplay between collective and individual factors that shape students’ experiences during CEs. 463 survey respondents with recent and significant networking CE exposure participated in the study. The results confirm the proposed research model, indicating that the student experience construct comprises a multi-dimensional framework of second-order components, including individual and peer experiences, each encompassing a set of first-order constructs. The results also validate that students’ experiences can predict subsequent perceived value and satisfaction evaluations. The study’s limitations, research, and practical implications for crafting meaningful and fulfilling experiences for CE attendees are discussed

Virtual selfhood and consumer behavior: Exploring avatar attachment and consumption patterns in Second Life’s metaverse.
M Kalyvaki, H McIntosh, K Nash
Computers in Human Behavior: Artificial Humans 1 (2), 100016

In the vast digital landscape of the Metaverse, users can create and personalize their avatars as virtual representations of themselves. This study delves into the emotions users experience in relation to their avatars and examines how this attachment influences their consumption behaviors within the virtual world. The research employed a sample of 214 active users participating in Second Life, a prominent virtual world platform. By analyzing survey data, we explore the dynamics of self-presentation and attachment between users and their virtual personas across this well-established platform. Our research offers valuable contributions to the existing literature on the Metaverse, providing empirical evidence on how virtual reality platforms like Second Life foster avatar customization and how this, in turn, affects consumer behavior. As the Metaverse gains prominence in the business world, understanding the habits and preferences of virtual reality users is increasingly crucial. We aim to enhance our understanding of consumer behavior by incorporating attachment theory into our research on long-standing virtual environments like Second Life. Our study of Second Life provides valuable insights into the dynamics of consumer behavior within a well-established virtual world, which can be applied to the emerging Metaverse platforms. This knowledge helps businesses identify consumer profiles, address their needs, and enhance their virtual presence and success.

The role of identity in green IT attitude and intention.
Kyle Nash, Robin L Wakefield
Journal of Computer Information Systems

Green IT practices are of global concern with huge environmental implications. Reducing CO2 emissions, conserving energy, and recycling IT are among a variety of green IT behaviors that protect the natural environment. We explore the role of green IT identity as an additional factor in a model based on the Theory of Planned Behavior. Theoretically, our findings show green IT identity plays a substantive role in predicting behavioral intention independent of attitude as well as indirectly through attitude. The addition of green IT identity in a behavioral model provides a more complete prediction of green IT behavior. Practically, strengthening one’s green IT identity is likely to stimulate both favorable attitudes and green IT behavior. We discuss the implications of our study for green IT research.

Enhancing Urban Sustainability: The Role of AI-Driven Transportation Systems in Reducing Carbon Footprints
K Nash, HA Ahmed, M Soliman

Urban transportation is a significant contributor to global carbon emissions, exacerbating climate change and urban air quality issues. This study explores how Artificial Intelligence (AI)-driven transportation systems can mitigate these environmental impacts by optimizing routes, reducing congestion, and enhancing public transportation efficiency. Through a combination of empirical data analysis, cities cases, and simulation models, this research evaluates the effectiveness of AI applications in urban mobility. The findings demonstrate that AI-driven optimizations lead to substantial reductions in carbon emissions, improved energy efficiency, and better utilization of existing infrastructure. Additionally, the study addresses the challenges and barriers to implementing AI solutions in urban settings, providing recommendations for policymakers and urban planners to foster sustainable transportation ecosystems. This research contributes to the growing body of knowledge on sustainable urban development and highlights the pivotal role of AI in achieving environmental sustainability goals.

Revolutionizing Alzheimer’s Diagnosis: A Hybrid Deep Learning Approach for Enhanced MRI Analysis.
HA Ahmed, SH Ahmed, K Nash

Alzheimer’s Disease (AD) is a neurodegenerative disorder that primarily affects the elderly, causing cognitive decline and memory loss. Traditional diagnostic methods, such as neuropsychological tests and cerebrospinal fluid analysis, are invasive and time-consuming. Neuroimaging techniques like MRI and PET provide valuable insights but require manual analysis by specialists. This study proposes a hybrid model combining EfficientNetB0, a deep learning architecture, with Convolutional Neural Networks (CNN) to automate AD detection in MRI scans. The model uses data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset, which includes over 200 MRI scans and clinical information. Our results show that the hybrid model outperforms existing methods in accuracy and efficiency, detecting key AD pathology features such as amyloid beta plaques and neurofibrillary tangles. This work demonstrates the potential of AI-driven approaches for AD diagnosis, offering a more accessible, cost-effective solution for clinical settings with limited resources. Future research should explore multimodal integration and model interpretability. Keywords: Alzheimer’s Disease, Deep Learning, MRI, EfficientNetB0, Convolutional Neural Networks.

Transforming Data Centers: How AI Revolutionizes Energy Efficiency in Cooling
K Nash
AMCIS, 2024

Data centers are the backbone of the digital age, serving as the critical infrastructure that powers our modern world. However, this immense technological growth comes at a price: data centers are voracious consumers of energy, with cooling systems being one of the largest contributors to their energy bills. As the demand for data processing and storage continues to skyrocket, addressing the energy efficiency of data centers becomes paramount. This is where Artificial Intelligence (AI) steps in, revolutionizing the way data centers manage their cooling systems to optimize energy efficiency and sustainability. Data centers house thousands of servers and electronic components that generate substantial amounts of heat during operation. To prevent overheating and ensure optimal performance, sophisticated cooling systems are essential. Traditional cooling methods, like air conditioning, often involve excessive energy consumption and high operational costs (Nadjahi et al. 2018); (Ni and Bai 2017). These systems also pose environmental concerns due to their carbon footprint, contributing to climate change. AI has emerged as a transformative force in the pursuit of energy-efficient data centers (Gill et al. 2019); (Lu et al. 2020). By leveraging the capabilities of machine learning (Farahnakian et al. 2014); (Shaw et al. 2022) and data analytics (Çağlar and Altılar 2022), AI-driven solutions have the potential to revolutionize the operation of cooling systems. AI brings several key innovations to enhance energy efficiency in data center cooling: One such innovation is Predictive Analytics (Al Kez et al. 2022); (Stergiou and Psannis 2022). With AI, extensive datasets that encompass server temperatures, workload patterns, and weather forecasts can be meticulously analyzed to forecast cooling requirements in advance. This predictive capability empowers proactive adjustments to the cooling system, effectively reducing energy wastage. Another significant advancement is Dynamic Temperature Control (Zhao, Cai, et al. 2022); (Zhao, Chang, et al. 2022). AI can dynamically adapt cooling settings in real-time based on current conditions, ensuring that server temperatures remain within the optimal range without resorting to excessive cooling, a common issue with conventional systems. In certain instances, Zonal Cooling is employed (Li et al. 2019); (Rahman et al. 2020). AI can fine-tune cooling zones within a data center, directing cooling resources to areas with the highest demand while conserving energy in less critical zones. Renewable Energy Integration serves as another compelling example (Huang et al. 2020). AI can optimize cooling operations to align with the availability of renewable energy sources like solar and wind, further diminishing the carbon footprint associated with data centers. Additionally, some data centers have embraced Predictive Maintenance (Decker et al. 2020); (Teoh et al. 2021). AI, in this context, excels at predicting equipment failures and identifying maintenance needs well in advance, thereby preventing downtime and ensuring the optimal performance of cooling systems. As the demand for data continues to grow, the energy efficiency of data centers becomes increasingly critical for both economic and environmental reasons. AI-driven cooling solutions represent a significant step towards reducing the environmental impact of data centers while cutting operational costs (Dharaniya et al. 2023). By harnessing predictive analytics, dynamic temperature control, and renewable energy integration, data center operators can ensure that their facilities are not only more energy-efficient but also more sustainable. The digital revolution shows no signs of slowing down, and data centers will remain at the forefront of this transformation. However, the environmental impact of data centers cannot be ignored. AI-powered cooling solutions offer a promising path to drastically improve energy efficiency and reduce the carbon footprint of these vital facilities. By embracing AI-driven innovations, data center operators can contribute to a greener and more sustainable digital future while optimizing their operations and reducing costs.

Examining GRI Sustainability Reports through the Lens of the Stakeholder Theory
K Nash, R Wakefield
AMCIS 2022 Proceedings

Publishing a successful sustainability report is a rising concern among organizations seeking to meet the expectations of their stakeholders. The purpose of this research is to examine how stakeholder engagement influences Global Reporting Initiative’s (GRI) reporting processes. We use stakeholder theory to assert that an organization’s sustainability practices are prompted by the demands of a variety of stakeholders. Cisco GRI reports were chosen for analysis because in 2020 Cisco was ranked 4th amongst the global 100 most sustainable corporations in the world. We conducted a longitudinal analysis of Cisco’s corporate social responsibility reports from 2005 to 2020. Using text mining techniques and text statistical analysis we identified the primary stakeholders in each year’s sustainability report and document stakeholder-related sustainability practices. Our results demonstrate that organizational sustainability practices are a function of the extent of engagement with core stakeholders. This study contributes to understanding how stakeholders’ engagement relates to organizational sustainability reporting processes.

A dynamic approach to examine the growth trajectory of e-participation factors
Kyle Nash
Baylor University

This research explores key factors that drive e-participation growth among 147 nation-states over seven years (2014-2020). While the literature utilizing information and communication technologies (ICT) to advance e-participation research has proliferated in recent years, these studies generally do not clarify how e-participation growth occurs and how it is sustained. The current study develops an e-participation model based on Stigmergy Theory to identify core factors that drive e-participation. Then, Latent Growth
Curve Modeling (LGM) is used to examine differences in countries’ growth trajectories over time. This study contributes to understanding the factors that expand and sustain eparticipation to reduce developing countries’ learning curves.

Data Visualization Can Shifts our Sharing Economy Perceptions: Austin, Texas Airbnb Landscape.
Kyle Nash
AMCIS 2021 Proceedings. 4

During the last few years, we have witnessed remarkable growth in the sharing economy. Since 2008, Airbnb revolutionizes the travel industry; Airbnb is an enabled digital sharing economy platform that is a profoundly alternative to the lodging sectors. Have you ever used Airbnb services (as a host or guest)?. This study is an exploratory analysis study to understand the rental landscape in Austin, Texas (ATX) through various static and interactive visualizations methods. Our study focuses on the impact of a peer-to-peer
accommodation platform on the local economy and the lodging industry; and how positives reviews promote the most lucrative listing, accommodation, or host. Our findings elevate Airbnb’s colossal impact on the Austin economy and lodging sectors. We found that offering the entire property in a good location, with high cleaning standards is attractive on the Airbnb platform, which means more lucrative.

Dare to be green: The role of environmental passion and green IT identity on green IT practices
Kyle Nash, Robin Wakefield
AMCIS 2019 Proceedings.

Individuals play a role in environmental sustainability including green IT practices. We use the Theory of Planned Behavior and create a model to examine the influence of environmental passion and green IT identity on behavioral intention to practice green IT. We test the model with a respondent group from the IT industry. The findings indicate environmental passion is an important indicator of favorable green IT attitude and is directly related to green IT identity. However, green IT identity has a direct influence on green IT intentions suggesting a contribution to behavior that is independent of attitude. We discuss the implications of green IT identity for research and practice.