Baker, B. J., Godde, K., & Ullinger, J. (2025). Exploring kinship within a late meroitic to medieval cemetery in sudan using k-modes clustering. [استكشاف صلة القرابة داخل مقبرة تعود الى الفترتين المروية المتأخرة والمسيحية في مود K السودان باستخدام تقنية تجميع البيانات ال] American Journal of Biological Anthropology, 186(3) doi:10.1002/ajpa.70016
Objectives: Intracemetery patterns at the Qinifab School site, a late Meroitic to Christian period cemetery (c. 250–1400 CE) between the fourth and fifth cataracts of the Nile River in Sudan, are modeled to explore the role of kinship in cemetery organization during significant sociopolitical and religious shifts. Materials and Methods: Sixty-eight cranial and 36 dental nonmetric traits were examined among 67 adults. A machine-learning clustering method (k-modes clustering algorithm), new to biological anthropology, was used to detect patterning among biological, demographic, and temporal data and validated with inter-individual Mahalanobis distances and mixed data principal components analysis using only biological data. Results: When three validated clusters were examined, a pattern emerged that aligned with the archaeological context of the cemetery when time period is considered. One cluster concentrated at the oldest end of the site appeared to be a founding group, with the other two groups equally comprising later periods. Some mortuary patterning by sex was visually identified; a few late to Post-Meroitic males grouped together in one cluster. Discussion: Individuals from Clusters 1 and 3 were buried near each other infrequently, which suggests two different kin groups may have intermarried with the third, or one group held a status that allowed identification with the other clusters. Kinship was not a determinant for males with archery items or individuals with incisor avulsion, as they were found in all three clusters. The Qinifab School cemetery reflects temporal organization, a multilocal residence pattern, and a lack of sex stratification for the medieval period. © 2025 Wiley Periodicals LLC.
Goryunova, E., & Yawson, R. M. (2025). “Nested complexity” framework for human-centered AI-augmented leadership. Journal of Leadership Studies, 19(2) doi:10.1002/jls.70016
Persistent complex environmental and socioeconomic challenges, political turbulence, and accelerating technological innovation create an intricate and dynamic environment. Artificial intelligence (AI) offers potential solutions for navigating complexity. However, its deployment introduces its own multifaceted challenges—technological, ethical, and regulatory—all embedded within environmental complexity. Conceptualizing AI implementation as a “nested complexity” recognizes AI as a dynamic phenomenon contributing to the complexity within which organizations operate, thus encouraging organizational leaders to be cognizant of the intricacies, constraints, and evolving nature of AI, and to utilize an adaptive and iterative approach to its implementation. © 2025 University of Phoenix.
Kovbasiuk, A., Triantoro, T., Przegalińska, A., Sowa, K., Ciechanowski, L., & Gloor, P. (2025). The personality profile of early generative AI adopters: A big five perspective. Central European Management Journal, 33(2), 252–264. doi:10.1108/CEMJ-02-2024-0067
Purpose: This pilot study aimed to evaluate the impact of the big five personality traits on user engagement with chatbots at the early stages of artificial intelligence (AI) adoption. Design/methodology/approach: The pilot study involved 62 participants segmented into two groups to measure variables including engagement duration, task performance and future AI usage intentions. Findings: The findings advocate for the incorporation of psychological principles into technology design to facilitate more tailored and efficient human–AI collaboration. Originality/value: This pilot research study highlights the relationship between the big five personality traits and chatbot usage and provides valuable insights for customizing chatbot development to align with specific user characteristics. This will serve to enhance both user satisfaction and task productivity. © 2024, Anna Kovbasiuk, Tamilla Triantoro, Aleksandra Przegalińska, Konrad Sowa, Leon Ciechanowski and Peter Gloor.
Lang, G., Triantoro, T., & Sharp, J. H. (2024). Large language models as AI-powered educational assistants: Comparing GPT-4 and gemini for writing teaching cases. Journal of Information Systems Education, 35(3), 390–407. doi:10.62273/YCIJ6454
This study explores the potential of large language models (LLMs), specifically GPT-4 and Gemini, in generating teaching cases for information systems courses. A unique prompt for writing three different types of teaching cases such as a descriptive case, a normative case, and a project-based case on the same IS topic (i.e., the introduction of blockchain technology in an insurance company) was developed and submitted to each LLM. The generated teaching cases from each LLM were assessed using subjective content evaluation measures such as relevance and accuracy, complexity and depth, structure and coherence, and creativity as well as objective readability measures such as Automated Readability Index, Coleman-Liau Index, Flesch-Kincaid Grade Level, Gunning Fog Index, Linsear Write Index, and SMOG Index. The findings suggest that while both LLMs perform well on objective measures, GPT-4 outperforms Gemini on subjective measures, indicating a superior ability to create content that is more relevant, complex, structured, coherent, and creative. This research provides initial empirical evidence and highlights the promise of LLMs in enhancing IS education while also acknowledging the need for careful proofreading and further research to optimize their use. © Copyright ©2024 by the Information Systems & Computing Academic Professionals, Inc. (ISCAP). Permission to make digital or hard copies of all or part of this journal for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial use. All copies must bear this notice and full citation. Permission from the Editor is required to post to servers, redistribute to lists, or utilize in a for-profit or commercial use. Permission requests should be sent to the Editor-in-Chief, Journal of Information Systems Education, editor@jise.org.
Laskin, A. V., & D'Agostino, G. (2024). The delphi panel investigation of artificial intelligence in investor relations. Public Relations Review, 50(4) doi:10.1016/j.pubrev.2024.102489
In recent years, there has been a discernible upswing in the attention dedicated to artificial intelligence (AI) within the domains of public relations, advertising, and marketing. Notably, the subdomain of investor relations has maintained a significant historical engagement with AI, actively employing AI and AI-enabled tools for several decades, a practice traceable back to the 1980s. This protracted involvement presents a reservoir of invaluable insights germane to comprehending the broader integration of AI within the purview of public relations. This scholarly inquiry embarks on a Delphi panel examination to scrutinize the deployment of AI in investor relations, proffers a systematic classification of AI-enabled tools within this realm, and prognosticates the trajectory of AI's influence on investor relations and financial communications. The panel of Delphi participants comprises seasoned authorities in the field, boasting a cumulative professional experience spanning 161 years. Leveraging the depth of expertise inherent in investor relations, the study not only illuminates the current landscape but also posits conceivable trajectories for the evolution of AI across other subfields within the domain of public relations. © 2024 Elsevier Inc.
Laskin, A. V., & Freberg, K. (2024). PUBLIC RELATIONS AND STRATEGIC COMMUNICATION IN 2050: Trends shaping the future of the profession. (pp. 1–264) Retrieved from https://www.scopus.com/inward/record.uri?eid=2-s2.0-85210685608&doi=10.4324%2f9781003426653&partnerID=40&md5=bcdae94ab6874fd13fa502701b57bacc
Taking stock of the technological, political, economic, and social trends that exist today, this book extends the discussion to analyze and predict how these trends will affect the public relations and strategic communication industry of the future. This book is divided into two sections, the first addressing such key topics as artificial intelligence (AI), big data, political polarization, and misinformation, the second looking at key facets of the profession, such as media relations, crisis communication, and measurement and evaluation. Leading researchers in the discipline share their analysis of these topics while also providing theoretically based and practically relevant insights on how the industry must evolve to keep up with, and perhaps anticipate, changes in culture, society, and technology. This book will be of interest to scholars, industry professionals, and advanced undergraduate and graduate students in public relations and strategic communication. © 2025 selection and editorial matter, Alexander V. Laskin and Karen Freberg; individual chapters, the contributors.
Nichols, T., Zemlanicky, J., Luo, Z., Li, Q., & Zheng, J. (2024). Image-based PDF malware detection using pre-trained deep neural networks. Paper presented at the Retrieved from https://www.scopus.com/inward/record.uri?eid=2-s2.0-85194066547&doi=10.1109%2fISDFS60797.2024.10527343&partnerID=40&md5=ad96ad418e7d04755b26f9aa44d692fb
PDF is a popular document file format with a flexible file structure that can embed diverse types of content, including images and JavaScript code. However, these features make it a favored vehicle for malware attackers. In this paper, we propose an image-based PDF malware detection method that utilizes pre-trained deep neural networks (DNNs). Specifically, we convert PDF files into fixed-size grayscale images using an image visualization technique. These images are then fed into pre-trained DNN models to classify them as benign or malicious. We investigated four classical pre-trained DNN models in our study. We evaluated the performance of the proposed method using the publicly available Contagio PDF malware dataset. Our results demonstrate that MobileNetv3 achieves the best detection performance with an accuracy of 0.9969 and exhibits low computational complexity, making it a promising solution for image-based PDF malware detection. © 2024 IEEE.
Przegalinska, A., & Triantoro, T. (2024). Converging minds: The creative potential of collaborative AI. (pp. 1–158) Retrieved from https://www.scopus.com/inward/record.uri?eid=2-s2.0-85195743463&doi=10.1201%2f9781032656618&partnerID=40&md5=5dc2ac21bb905afefce724640a19ff9b
This groundbreaking book explores the power of collaborative AI in amplifying human creativity and expertise. Written by two seasoned experts in data analytics, AI, and machine learning, the book offers a comprehensive overview of the creative process behind AI-powered content generation. It takes the reader through a unique collaborative process between human authors and various AI-based topic experts, created, prompted, and fine-tuned by the authors. This book features a comprehensive list of prompts that readers can use to create their own ChatGPT-powered topic experts. By following these expertly crafted prompts, individuals and businesses alike can harness the power of AI, tailoring it to their specific needs and fostering a fruitful collaboration between humans and machines. With real-world use cases and deep insights into the foundations of generative AI, the book showcases how humans and machines can work together to achieve better business outcomes and tackle complex challenges. Social and ethical implications of collaborative AI are covered and how it may impact the future of work and employment. Through reading the book, readers will gain a deep understanding of the latest advancements in AI and how they can shape our world. Converging Minds: The Creative Potential of Collaborative AI is essential reading for anyone interested in the transformative potential of AI-powered content generation and human-AI collaboration. It will appeal to data scientists, machine learning architects, prompt engineers, general computer scientists, and engineers in the fields of generative AI and deep learning. Chapter 1 of this book is freely available as a downloadable Open Access PDF at https://www.taylorfrancis.com under a Creative Commons [Attribution- No Derivatives (CC-BY -ND)] 4.0 license. © 2024 Aleksandra Przegalinska and Tamilla Triantoro.
Przegalinska, A., Triantoro, T., Kovbasiuk, A., Ciechanowski, L., Freeman, R. B., & Sowa, K. (2025). Collaborative AI in the workplace: Enhancing organizational performance through resource-based and task-technology fit perspectives. International Journal of Information Management, 81 doi:10.1016/j.ijinfomgt.2024.102853
This research examines how artificial intelligence, human capabilities, and task types influence organizational outcomes. By leveraging the frameworks of the Resource-Based View and Task Technology Fit theories, we executed two distinct studies to assess the effectiveness of a generative AI tool in aiding task performance across a spectrum of task complexities and creative demands. The initial study tested the utility of generative AI across diverse tasks and the significance of AI-related skills enhancement. The subsequent study explored interactions between humans and AI, analyzing emotional tone, sentence structure, and word choice. Our results indicate that incorporating AI can significantly improve organizational task performance in areas such as automation, support, creative endeavors, and innovation processes. We also observed that generative AI generally presents more positive sentiment, utilizes simpler language, and has a narrower vocabulary than human counterparts. These insights contribute to a broader understanding of AI's strengths and weaknesses in organizational settings and guide the strategic implementation of AI systems. © 2024 The Authors
