MISQ Special Issue "Managing AI"


MIS Quarterly Special Issue on “Managing AI”

Call for Papers


Submission Deadline:

Full papers due November 15, 2019


Artificial intelligence (“AI”) refers to machines performing the cognitive functions typically associated with humans - including perceiving, reasoning, learning, interacting, etc. AI is not confined to one or a few applications, but rather is a pervasive economic, societal, and organizational phenomenon. Examples of AI technologies include robotics and autonomous vehicles, facial recognition, natural language processing, virtual agents, and machine learning, which are being deployed in a variety of problem domains ranging from cybersecurity to fintech to education to healthcare. Technologies involving AI provide inestimable possibilities for enhancing people’s lives in a variety of areas including their homes, healthcare, education, employment, entertainment, safety, and transportation.  Similarly, AI provides businesses with unprecedented opportunities for designing intelligent products, devising novel service offerings, and inventing new business models and organizational forms. But AI is not a technological panacea. Accompanying the horizon of possibilities are a host of emerging and complex challenges around business strategies, human-AI interfaces, data, privacy, security, ethics, labor, human rights, and national security. Today’s managers need to deal with both possibilities and challenges that accompany widespread AI. This special issue of MIS Quarterly focuses on understanding the management of AI.  For details, see:



MIS Quarterly, and the information systems field more generally, has a rich tradition of work dealing with the human and technical elements in managing information systems. Whereas the transformative potential of AI is widely recognized, there is significant uncertainty for businesses on how to manage AI and its implications. The information systems field has developed substantial knowledge on managing information technologies and systems for different objectives, stakeholders, and levels of analysis.  To what extent this knowledge translates to AI and to what extent AI falsifies assumptions, raises new questions, and creates new opportunities remains an open question that requires careful empirical and theoretical work. AI presents a great opportunity to challenge how we think about managing information systems and how we need to recalibrate that knowledge to manage AI.


The special issue is looking for papers that meet four specific criteria:


  1. Papers must distinguish fundamentally between AI and other forms of digital technologies, and theorize on the specific differences.  Direct applications of existing theory on IT and organization (without differentiating AI from generic IT) are not suitable for the special issue.


  1. Papers must focus on management practices of AI to enhance value or mitigate harm in the development, implementation, management, use and/or governance of AI.  We particularly encourage research on new forms of management on the inter-actions between human resources, AI, and other material resources.  As boundaries between human and machine become increasingly blurry, we call for new thinking on management forms and structures.


  1. Papers must provide novel contributions to knowledge about the management of AI.  Any form of rigorous theoretical contribution (conceptual or empirical) using any scholarly method is welcome.  While we welcome papers across diverse theoretical perspectives and research methods, descriptive studies that summarize state of practice of AI applications without a corresponding contribution to theory are not suitable for the special issue.


  1. Papers must consider both the social and technical aspect of AI.  Studies that focus only on the technical aspect of AIwithout placing salience on management of AI do not correspond to the focus of the special issue.  We encourage studies on management of AI at and across a variety of levels of analysis, including organizations, institutions, platforms, ecosystems, and societies.


Potential topics include, but are not limited to:

  • Management, control, and governance of AI-related resources and capabilities.
  • Changes in strategy, structure, functions, workforce, alignment, processes, and control that flow from management of AI.
  • Managing intended and unintended AI-related outcomes across levels of analysis.
  • AI-enabled changes to business strategy, business models, and value creation processes.
  • Management of AI-fostered innovations, including digital product development and software development.
  • Managing  policy,  legislative,  ethical,  moral,  and  societal  implications  of  AI,  including  intellectual  property rights ownership.
  • Data guardianship, security, and privacy in AI contexts.
  • AI as management, in conjunction with humans or otherwise.
  • Evaluation and monitoring of AI and associated organizational activity.
  • Managing design issues associated with AI in infrastructure, artefacts, products, platforms, ecosystems and markets.


Key Dates:

  • First round submissions:  November 15, 2019
  • Round 1 decisions:  February 15, 2020
  • Workshop (location TBD):  March 20, 2020
  • Second round submissions:  June 15, 2020
  • Second round decisions to authors:  August 31, 2020
  • Third and final round submissions:  November 15, 2020
  • Third and final round decisions to authors:  December 31, 2020


Special Issue Editors:

Nicholas Berente, University of Notre Dame {nberente@nd.edu}

Bin Gu, Arizona State University {bin.gu@asu.edu}

Jan Recker, University of Cologne{jan.recker@wiso.uni-koeln.de}

Radhika Santhanam, University of Oklahoma {radhika@ou.edu}


Special Issue Editorial Board:

Michael Barrett, Cambridge University

Roger Chiang, University of Cincinnati

Kevin Crowston, Syracuse University

Hailiang Chen, University of Hong Kong

Pei-Yu Chen, Arizona State

Brad Greenwood, University of Minnesota

Robert Gregory, University of Virginia

JJ Hsieh, Georgia State University

Peng Huang, University of Maryland

Ke-wei Huang, National University of Singapore

Zhengrui Jiang, Nanjing University

De Liu, University of Minnesota

Karthik Kannan, Purdue University

Deepa Mani, Indian School of Business

Jeff Nickerson, Stevens Institute of Technology

Brian Pentland, Michigan State University 

Yuqing Ren, University of Minnesota

Lionel Robert, University of Michigan

Rajiv Sabherwal, University of Arkansas

Nachiketa Sahoo, Boston University

Stefan Seidel, University of Liechtenstein

Mari Clara Stein, Copenhagen Business School

Jingjing Zhang, Indiana University

Rong Zheng, Hong Kong University of Science and Technology