Call for Papers
CfP Special Issue on "Machine Learning in Business Networks"
Submission Deadline: July 15, 2018
- Volker Bach, Steinbeis University Berlin, STI Business and Software Engineering, Germany
- Junbin Gao, University of Sydney Business School, Australia
- Xi Chen, Nanjing University Business School, China
An ever-expanding digital world has created a desire for increasingly smart business applications. Virtually every enterprise now aims to explore and harness the full potential of large data volumes. For data-driven goals, machine learning offers the opportunity of automated extraction and processing of actionable information from vast amounts of raw data.
While the investigation of learning problems as well as the development and analysis of learning systems can be considered as a dynamic, yet well-established research area, the broad application of machine learning in business contexts gains more and more attention from both researchers and practitioners. These business contexts result in a wide range of research objects: single business decisions which benefit from incremental improvements of existing analytical applications; entire business processes with their innovation and improvement cycles; business networks that interact based on new types of products and services and the like.
The substantial impact of machine learning technologies on these research objects causes growing demand for adequate approaches in research and practice. On the level of information systems this includes methods for the management of the enterprise architecture based on envisioned next generation enterprise systems. In the areas of organizational and strategic design, several approaches need to be extended or re-thought, including the definition and improvement of business processes and their management, the creation and integration of new product and service offerings as well as the coordination and (re-)configuration of business networks.
Deepening knowledge about the underlying causal relationships between design decisions, actors’ behavior, and business outcomes is an ongoing and challenging task that deserves to be addressed by researchers, managers and decision makers seeking for innovative and sustainable business models.
Central issues and topics
Possible topics of submissions include, but are not limited to:
Machine Learning Applications in Business Networks
Concepts for designing, developing, integrating, and managing machine learning applications in business networks
Design and evaluation of machine learning applications all along the value chain
Adoption of Machine Learning Technology
Roadmaps and success factors of machine learning adoption
Impacts of machine learning on IS/IT organizations
Machine learning in enterprise systems development and integration
Conceptual Integration of Machine Learning
Fundamental impacts of machine learning on the understanding of business networks
Machine learning in business process development
We encourage contributions with a broad range of methodological approaches, including conceptual, qualitative and quantitative research. All papers should fit the scope of Electronic Markets (for more information see www.electronicmarkets.org/about-em/scope/) and will undergo a double-blind peer review process. If you would like to discuss any aspect of the special issue, please contact the guest editors.
Electronic Markets is a SSCI-listed journal (IF 3.818) and requires that all papers must be original and not published or under review elsewhere. Papers must be submitted via our electronic submission system at elma.edmgr.com and conform to Electronic Markets publication standards (see instructions and templates at www.electronicmarkets.org/authors). Please note that the preferred article length is around 6,500 to 8,000 words.
Submission Deadline: July 15, 2018
- Volker Bach (Steinbeis University Berlin, STI Business and Software Engineering, Germany) volker.bach(at)stw.de
- Junbin Gao (University of Sydney Business School, Australia) Junbin.gao(at)sydney.edu.au
- Xi Chen (Nanjing University Business School, China) chenx(at)nju.edu.cn