Special Issue: Artificial Intelligence and Machine Learning: New Frontier of Research, Practice and Method for Transformative Changes in IS

Artificial Intelligence (AI) encompasses the capability of machines to perform functions akin to those historically attributed to humans, including perception, reasoning, learning, and interaction (Rai et al., 2019; Dwivedi et al., 2021; Pappas et al., 2023). AI's influence extends beyond a singular realm; it permeates various facets of our lives, and has potential to bring a large scale ‘digital transformation’ (Markus & Rowe, 2023), impacting our economy, society, organizations, and our daily lives (Dennehy et al., 2023; Sharma et al., 2023; Vassilakopoulou et al., 2023). Some of the transformative changes available today range from robots and self-driving vehicles to language-processing computers, exemplified by ChatGPT (Dwivedi et al., 2023), and virtual assistants. These transformative capabilities associated with AI applications generate newer grand social challenges and present interesting opportunities for the Information Systems (IS) scholars to revisit the extant knowledge, and theories of IT adoption and diffusion.

This special issue aims to serve two purposes among others: (1) advance knowledge on adoption and diffusion of AI technologies, and (2) ‘novel’ applications of machine learning methods for inductive theory building.

The landscape of AI introduces novel challenges, encompassing concerns of privacy, bias, and delegation. Addressing these challenges necessitates an expansion of existing paradigms to accommodate the evolving dynamics. This track invites submissions that leverage the constraints of current adoption studies, and opens the ‘contextual envelope’ of AI applications in generating fresh insights. Such an approach promises to augment the cumulative tradition of the Information Systems discipline. This track seeks research that investigates the unique adoption and diffusion dynamics of AI, requiring new perspectives, frameworks, and methodologies. We encourage submissions that explore, but are not limited to, the following topics:

• The role of cognitive biases and behavioral factors in AI adoption.

• Socio-cultural impacts on the acceptance of AI technologies.

• Ethical considerations influencing the diffusion of AI in different contexts.

• Frameworks for evaluating the readiness of organizations for AI implementation.

• Comparative studies of AI adoption across different sectors and regions.

• Theoretical aspects of generative AI across various industry setting.

The rapidly evolving landscape of emerging technology adoption and diffusion necessitates innovative theoretical and methodological approaches. This track encourages the utilization of cutting-edge methodological advancements in data-driven, machine learning (ML)-based theory development to explore new frontiers in the field. Inductive theory generation through quantitative data analysis has its roots in grounded theory (Glaser and Strauss, 1967) and is informed by computational theory discovery (Berente et al., 2019). Computational theory discovery harnesses the power of ML models to discern intricate patterns within data, facilitating the identification of hypotheses through the extraction of replicable patterns (Shrestha et al., 2021). The availability of large data sets allows researchers to enhance efficiency and robustness in the identification of novel patterns emerging from the underlying data using state-of-the-art machine learning methodologies (Choudhary et al., 2021). Researchers are empowered to analyze and contextualize emerging patterns of associations within their data. These patterns can serve as "targeted and contextual theories in flux" (Tremblay et al., 2021) that may explain complex phenomenon better. They can extend existing theories or embark on the creation of entirely new theoretical frameworks. Unlike traditional parametric statistical techniques, machine learning models possess the unique ability to capture higher-order and interaction terms among predictors without the need for explicit a priori specification. This allows researchers to strike a balance between interpretability and predictive accuracy while uncovering complex data patterns in their pursuit for theory development (Shrestha et al., 2021). Recent advancements and guidelines in algorithmic induction provide a robust framework for generating reliable and generalizable patterns while mitigating both researcher as well as algorithmic bias (Miranda et. al, 2022). We encourage researchers to leverage these developments and explore a diverse range of research topics in the realm of adoption and diffusion of IT. Potential research areas include but are not limited to:

• Integrating ML-generated insights with existing theoretical frameworks.

• Using ML to uncover emergent phenomena and relationships in complex systems.

• Studies demonstrating the application of ML in theory-driven research.

About Information Systems Frontiers

Information Systems Frontiers (ISF) stands as a prestigious international scholarly journal with a primary mission to establish connections among diverse academic disciplines and foster a vital linkage between the academic realm and industry. The aim of ISF is to disseminate original, impeccably articulated, and self-contained scholarly works that illuminate pioneering research and innovation within the Information Systems/Information Technology (IS/IT) domain, thereby making substantial and fundamental contributions to the field.

In ABDC Ranking, ISF is listed as an A ranked journal, whereas in ABS ranking, it is listed as a 3-level journal.

Submissions

This special issue will comprise two types of articles: the first type will be selected through a competitive process from papers submitted openly. These papers will be chosen because they represent the best quality submissions. The second type of articles will be invited submissions. These invited papers will be enhanced or modified versions of papers previously accepted at the IFIP WG 8.6 Conference 2023. In this case, authors will need to make substantial improvements to their conference papers. They will also be required to provide a detailed explanation of the differences between the conference paper and the new version. All submissions, whether open or invited, will undergo a rigorous peer review process. In the case of invited conference papers, if they do not receive a satisfactory review, they will not be included in the special issue. Each submission will be evaluated by at least two independent reviewers, adhering to the same review process and standards as regular submissions to the Information Systems Frontiers journal. Submissions must be written in proof-read English and submitted in PDF format via the Editorial Manager of the journal: https://www.editorialmanager.com/isfi/default.aspx

Authors should follow the submission guidelines from the journal webpage: https://www.springer.com/journal/10796/submission-guidelines

Important Dates

• Submission deadline: 1st April 2024

• Notification of first round reviews: 1st July 2024

• Revised manuscript due: 1st September 2024

• Notification of second round reviews: 1st November 2024

• Final version due: 15 January 2025

• Tentative publication date: Mid of year 2025

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