The AIS SIG CCRIS track at AMCIS 2025 (August 14-16, Montreal, Canada), titled "Global, International, and Cross-Cultural Research in Information Systems in an AI Age," features four mini-tracks:
- Evaluating the Value of AI Technologies: Cross-Cultural Perspectives
- Facilitating Social Inclusion for Older Adults in the Age of Human-Centered Artificial Intelligence
- AI-Driven Organizational Transformation and Learning in a Cross-Cultural World
- Inclusive Translation of Underserved Languages
Track Co-Chairs:
Shengnan Yang, Western University, yang290@iu.edu
Xiaohua Zhu, University of Tennessee Knoxville, xzhu12@utk.edu
Pnina Fichman, Indiana University, fichman@indiana.edu
The following papers have been accepted and will be presented at AMCIS 2025:
| Title |
Authors |
| * AI-Driven Misinformation: A Comparative Legislation Analysis |
Zhu, Xiaohua; Di Valentino, Lisa; Yang, Shengnan |
| No Joke: Refusal Policies for Cross-Cultural Sensitivity |
Zhou, Mengyuan; Xuenan Cao |
| Bits and Rituals: The Role of Information and Communication Technology in Preserving and Sharing Intangible Cultural Heritage |
Ding, Wenwen; Sabherwal, Rajiv |
| * Artificial Intelligence or Artificial Trolls？ |
Sun, Honglei Lia; Fichman, Pnina |
| New Elderly, Digital Future, and Intergenerational Support |
Liang, Tian-Tian; Wang, Xi; Tang, Jian; Li, Hui |
| * Older, But Not All Fear: Latent Profile Analysis on Technophobia and Technology Adoption Among Older Adults |
Dai, Shuo; Xi, Wanyu; Xue, Xiang |
*Reviewed by other tracks
Minitrack details:
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Evaluating the Value of AI Technologies: Cross-cultural Perspectives
AI technologies have been integrated into information systems across contexts and cultural landscapes, making it urgent to consider if perceptions and practices of AI respond to context-specific needs and make AI applications more effective and mindful. Emerging AI-powered software, tools, and applications such as Tableau Pulse, Adobe Express, and GPT4 are leveraging the ability of generative AI to be creative, to imitate human intelligence, but also to surpass, or diverge from, human abilities. How do we make better use of AI across various information systems? In this mini-track, we approach this question by creating a discussion on the socio-technical affordances that impact AI evaluation in information systems, particularly from a cross-cultural perspective. We understand cross-cultural practices broadly to cover differences across nations, regions, organizations, disciplines, and societies. We welcome studies that are both culture-specific and adopt a comparative lens. Both empirical work and theoretical contributions are welcomed.
Rongqian Ma, Indiana University Bloomington, rm56@iu.edu
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Facilitating Social Inclusion for the Older Adults in the Age of Human-Centered Artificial Intelligence In the age of human-centered Artificial Intelligence (AI), technology possesses tremendous potential to reshape social structures and interactions among people. However, the digital divide frequently places older adults at a disadvantage, potentially leading to increased social marginalization. We are calling upon scholars from various fields worldwide to engage in discussions about a new paradigm—one that fosters inclusive environments for seniors in the AI era, ensuring that they are not merely participants in the digital revolution but also active contributors. Incorporating AI into societal systems aims to enhance seniors’ ability to access information, facilitate intergenerational dialogue, and encourage their active participation in community activities. A more inclusive social structure requires a multidisciplinary approach, blending insights from information systems, library and information science, gerontology, computer science, sociology, and ethics to develop AI solutions that are sensitive to the cultural, physical, and cognitive needs of older adults.
Yuxiang Chris Zhao, Nanjing University, yxzhao@vip.163.com Peng XIAO, Sun Yat-sen University, xiaop25@mail.sysu.edu.cn Shijie Song, Hohai University, ssong@hhu.edu.cn Wanyu XI, Nanjing University of Chinese Medicine, xiwanyu@njucm.edu.cn
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AI-Driven Organizational Transformation and Learning in a Cross-Cultural World We invite researchers to submit their research which addresses critical issues at the intersection of AI, organizational adaptation, and learning in today’s rapidly evolving landscape. This minitrack encourages contributions on how AI technologies not only facilitate individual learning within organizations (e.g., through AI-driven learning platforms) but also promote broader organizational adaptation processes that support resilience and innovation.
We seek submissions that explore diverse methodologies, including empirical case studies, theoretical frameworks linking AI and organizational learning, and comparative analyses of AI adoption across regions or industries. Key themes include the dual impact of AI on technological and human aspects of transformation, cross-cultural challenges in AI implementation, and the role of leadership in guiding adaptive efforts. We especially welcome contributions that examine regional and sectoral differences in AI-driven learning practices, highlighting strategies organizations use to foster a culture of continuous learning and adaptive capacity.
Niklas Kühl, University of Bayreuth, kuehl@uni-bayreuth.de Laura Watkowski, Branch Business & Information Systems Engineering of the Fraunhofer FIT, laura.watkowski@fim-rc.de Philipp Spitzer, Karlsruhe Institute of Technology, philipp.spitzer@kit.edu
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Inclusive Translation of Underserved Languages This mini-track asks the question “can ethically informed radically inclusive machine translation uphold data sovereignty?” It investigates the ethical use of machine translation technology for language revitalization and the support of underserved languages. It incorporates work in areas of natural language processing, language revitalization, AI Ethics, language revitalization, and data sovereignty for groups from underserved languages. For many underserved languages, large corpora of documents do not exist to train machine translation systems. Lexical data sources, such as multilingual wordnets and thesauri, serve to bridge this gap by providing machine readable resources for underserved languages. In recent times, projects such as Wikifunctions have made explicit use of lexical linked open data LLOD to close this gap. However, this rarely involves explicit partnership with indigenous and other minority language communities. This track invites paper submissions related to:
NLP, machine translation, lexical semantics, underserved or minority languages, language revitalization, indigenous data sovereignty, linked open data.
John Kausch, Western University of Ontario, jkausch@uwo.ca Stacy Allison-Cassin, Dalhousie University, stacy.allison@dal.ca