Special Issue

The ongoing digital transformation is challenging the way business is conducted and how value is created and captured. While prior digitalization waves focused on replacing paper as physical carrier of information, leveraging the Internet as global communication infrastructure, and developing reactive, partly automated business processes and systems (e.g., Legner et al. 2017), the next wave will be about transforming these processes/systems into proactive sustainable autonomous systems (SAS). Such SAS are characterized by complex “systems of systems” of different age, quality, reliability, and performance, which may develop their own dynamics. A unique characteristic of SAS is their reliance on large amounts of data, along with the use of advanced technologies—such as the Internet of Things, Artificial Intelligence, or Blockchain—that allow for gathering and processing ‘big’ data with limited, or even no, human involvement. Even though SAS are designed, developed, and implemented in a process of socio-technical interaction; once in use, they take on the role of agents that make decisions and perform actions independently of humans. In other words, what has been created in a socio-technical way by implementing patterns—including organizational rules, as well as social norms and values—into a technical system, turns into a techno-social system once operating, where social actors in the organizational environment respond to the technical system and where the system may self-adapt to environmental changes. Thus, agency, decision rights, and responsibility are handed over to autonomous systems, while the ultimate accountability and decision rights to change the systems may still reside with the governing entity owning those systems.

By now, SAS can be found in various fields of application. Popular examples include driverless cars, smart cities, and smart homes, which often rely on a combination of sensors, algorithms, and self-executable code. Besides these tangible SAS, we note a growing number of intangible SAS in the form of software systems that operate either entirely in the background or at the interface with humans. Examples are intelligent chatbots and recommender systems, as well as algorithmic management and control systems, such as the ones used by Uber and other gig economy firms to control the behaviors of their digital workforce (e.g., Kellogg et al. 2020).

Autonomous systems, however, do not only differ in terms of application areas and contexts, but also in terms of their temporal dimension, as captured by the notion of sustainability, which generally refers to some long-term existence. This means that, once in use, SAS should be able to exist and fulfill their function for some time without further human interaction. In this sense, sustainability may refer to self-learning systems that are constantly improving themselves, such as an autonomous vehicle that, on a daily commute, keeps optimizing the route it takes. In doing so, the vehicle may also constantly improve its fuel consumption, which points to another perspective on sustainability directed toward the effects of SAS operation and use. As such, sustainability may also concern the long-term economic, social, and environmental effects of using autonomous systems (Hart & Milstein 2003), commonly referred to as the “3Ps” (profit, people, and planet) of the triple bottom line (Elkington 1997). This perspective includes the effects of SAS on the efficient use of tangible resources, such as energy (e.g., smart offices), space (e.g., smart cities), or food (e.g., smart fridges), as well as their effects on intangible resources, such as the longevity of data (e.g., for auditing purposes) or human and social capital in general. In addition, focusing on the sustainability of autonomous systems also demands the recognition of their limits. For example, given that such systems can be expected to come with unforeseen second-order or spillover effects, we may ask ourselves whether we really want to rely on systems that are on ‘autopilot.’ Also, critical ethical questions arise, for example, with regard to the decision rules according to which autonomous systems act (e.g., how a driverless car should react to unforeseen circumstances affecting humans).

Invited Contributions

With this call for papers, we seek to enhance our common understanding of the implications surrounding the design, development, and use of information systems that can be characterized by both autonomy and sustainability (i.e., SAS). Here, we acknowledge that neither autonomy nor sustainability are fixed end states, or ultimate goals, by which information systems should be evaluated. Rather, they represent information systems characteristics that need to be better understood as they appear in various forms and degrees, change over time, and have manifold consequences for individuals, organizations, and society.

The special issue focus on SAS leads to a number of important questions that can be fruitfully studied from a broad range of perspectives. As such, we encourage submissions from different methodological and theoretical perspectives—including different conceptions of causality (Markus & Rowe 2018)—stemming from information systems, economics, organizational behavior, strategic management, software engineering, and others. In this regard, authors must clearly outline why they consider their study to be new and interesting for research and practice, as well as how it addresses the special issue theme. Also, while the theoretical and conceptual foundations may stem from neighboring disciplines, we expect clearly articulated contributions to information systems research.

In the following, to stimulate submissions, we put together a list of sample questions that may serve prospective authors as a source of inspiration; however, this list is meant to be neither exclusive nor exhaustive:

What goals drive the design, development, and use of SAS and what potential tensions and/or paradoxes can be associated with those goals? How do organizations create an effective balance between different sustainability goals? Under what conditions do they prioritize certain goals at the expense of other goals? How can SAS be designed to achieve a particular set of objectives and ‘cushion’ its inherent tensions? How can the implementation and use of SAS be controlled and governed? Who should be in charge of corresponding control and governance activities? As well, designing and developing SAS from an end-to-end point of view may require novel and mindful systems engineering, evaluation, and testing approaches that go beyond traditional ones; if so, how would such approaches look like; who would approve them; and what criteria or requirements would have to be met to ensure the proper functioning of SAS along with their seamless integration into existing structures? How can interdependencies among different SAS be managed in due consideration of sustainability? If the development of SAS was outsourced to third-party vendors, who would ensure their adaptation to environmental changes and how would this process be governed? How should network and/or platform-based organizations—whose operations and business models tend to be based on the use of algorithmic systems—be governed and regulated with respect to sustainability goals? What about the ethical dilemmas and issues arising from algorithmic decision-making and how can managers, organizations, and society cope with those dilemmas/issues? What are the limitations of SAS and how can their appropriate use be influenced by a societal discourse? What level of digital maturity and what dynamic capabilities are needed for the value-enhancing use of SAS? Etc.

In addition, the list below categorizes and exemplifies topics of interest:

• SAS design:
  • SAS affordances
  • SAS design principles
  • SAS design parameters and effective design configurations
  • Tensions or paradoxes in SAS design
  • SAS design(er) autonomy
• SAS development:
  • Quality assurance in SAS development
  • SAS integration
  • Outsourced SAS development
  • SAS (project) governance and control
  • Platform-based SAS development
• SAS use:
  • SAS affordances actualization
  • Conditions for effective SAS use
  • Usage patterns and traits
  • Dynamics of SAS usage
  • SAS adaptation and learning
  • SAS and trust
  • SAS and stress
  • SAS and user autonomy
  • SAS and organizational autonomy
  • SAS and team autonomy
• Ethical aspects & Societal implications:
  • Ethical dilemmas of SAS applications
  • Regulating SAS applications
  • Responsible adoption and use of SAS
  • SAS and societal discourse
  • SAS and social media
• SAS technologies and application areas:
  • AI, machine learning
  • Blockchain, decentralized autonomous organizations (DOAs)
  • Chatbots, robotic process automation
  • Hybrid intelligence systems
  • Industry 4.0
  • Online platforms, Sharing economy
  • Robo-advisory
  • Smart cities, smart mobility, driverless cars, etc.

Submission Guidelines

The new submission deadline is July 1st, 2021 (changed schedule, see below). All manuscripts should be submitted through the journal’s online submission system (http://www.editorialmanager.com/buis/). Please observe the instructions on manuscript formatting and length. Submission guidelines and general author guidelines are available under: https://www.bise-journal.com/author_guidelines.

Each submission will be reviewed anonymously (double-blind process) by at least two referees with respect to its relevance, originality, and research quality. In addition to the editors of the special issue, distinguished international scholars will be involved in the review process as associate editors.

Schedule

• Initial paper submission deadline: February 28th, 2021 July 1st, 2021
• First round authors notification: June 15th, 2021 September 1st, 2021
• Completion of first revision: August 31st, 2021 November 1st, 2021
• Second round authors notification: November 15th, 2021 December 16th, 2021
• Final revision deadline: January 31st, 2022
• Final authors notification: February 15th, 2022
• Projected publication: Summer 2022

Editors of the Special Issue

Roman Beck
BusinessIT Department
IT University of Copenhagen
Denmark
beck@itu.dk

Jens Dibbern (corresponding)
Institute of Information Systems
Information Engineering
University of Bern
Switzerland
jens.dibbern@iwi.unibe.ch

Martin Wiener
Chair of Business Informatics, esp. Business Engineering
TU Dresden
Germany
martin.wiener@tu-dresden.de

Associate Editors

Alexander Benlian, TU Darmstadt
Jens Förderer, TU Munich
Robert Gregory, University of Virginia
Thomas Huber, ESSEC Business School, Paris
Oliver Krancher, IT University of Copenhagen
Thomas Kude, ESSEC Business School
Stefan Morana, Saarland University
Esko Penttinen, Aalto University
Ulrich Remus, University of Innsbruck
Marten Risius, University of Queensland
Nils Urbach, University of Bayreuth
Tim Weitzel, University of Bamberg
Till Winkler, Copenhagen Business School

References

Elkington, J. (1997) Cannibals with Forks: The Triple Bottom Line of 21^st Century Business. Oxford: Capstone Publishing.

Hart, S. L., and Milstein, M. B. 2003. “Creating Sustainable Value,” Academy of Management Executive (17:2), pp. 56-67.

Kellogg, K. C., Valentine, M., and Christin, A. (2020) “Algorithms at Work: The New Contested Terrain of Control,” Academy of Management Annals (14:1), pp. 366-410.

Legner, C., Eymann, T., Hess, T., Matt, C., Böhmann, T., Drews, P., Maedche, A., Urbach, N., and Ahlemann, F. (2017) “Digitalization: Opportunity and Challenge for the Business and Information Systems Engineering Community,” Business & Information Systems Engineering (59:4), pp. 301-308.

Markus, M. L., and Rowe, F. (2018) “Is IT Changing the World? Conceptions of Causality for Information Systems Theorizing,” MIS Quarterly (42:4), pp. 1255-1280.