Apr 24

Decision Sciences – Call for Papers: Special Issue on “Information and Operational Decision Sciences

DSJ

 

Call for Papers: Special Issue on “Information and Operational Decision Sciences: The Interplay of Information Technology and Operational Decision Sciences”

 


Guest Editors

Jose Benitez, Rennes School of Business, Rennes, France & University of Granada, Spain, email: joseba@ugr.es
Xin Luo, University of New Mexico, USA, email: xinluo@unm.edu
Gyula Vastag, Szechenyi Istvan University, Hungary, vastag.gyula@sze.hu

Special Issue Theme and Scope: Information and Operational Decision Sciences
The theme of the Special Issue is “Information and Operational Decision Sciences: The Interplay of Information Technology and Operational Decision Sciences”. Information technology (IT) capabilities enable firms to digitally transform their business processes to achieve business flexibility, quality, and innovation activities, thus improving the firms’ operational performance. Operational capabilities and performance are the heart of the firm’s business model to survive in the long run. Firms’ ability in using and leveraging innovative IT resources may facilitate the development of operational capabilities to increase firm performance.

As two separate fields in many Business Schools [i.e., Information Systems (IS) and Operations Management (OM)], sometimes it seems as IT is from Mars and OM comes from Venus. The Decision Sciences community and Decision Sciences are the ideal platforms to pursue a “transdisciplinary” approach (that is looking for the unity of knowledge beyond disciplines) to develop high quality and innovative research work growing out of these two fields. Because in the interplay of IT and operational decision making the whole is more than the sum of its parts, this Special Issue calls for original research on the interplay of IT and operational decision making.

This Special Issue welcomes the best submissions of the 8th Annual Conference of the European Decision Sciences Institute (EDSI 2017), as well as other high-quality submissions of other relevant conferences in 2017 (including but not restricted to DSI 2017, EURO 2017, EurOMA 2017, INFORMS 2017, and ICIS 2017).

Research Topics
This Special Issue calls research submissions about “Information and Operational Decision Sciences” using novel theories and/or methods as well as research contexts that make a significant contribution to knowledge in Decision Sciences. Submissions that focus on the interplay of IT and operational decision-making are especially welcome. Topics of interest include but are not limited to the following:

 Interplay of IT and operational decision making,
 Business value of IT,
 IT-enabled organizational capabilities,
 IT and innovation activities,
 Business value of social media,
 Social media and business activities,
 Social media and customer value proposition,
 Social media and innovation activities,
 Big data and operational decision making,
 Big data and business analytics.
 Supply chain management and value creation,
 Innovation management,
 Supply chain management and open innovation,
 Globalization of manufacturing operations,
 Quality management,
 Triple bottom line, operations management, and IT,
 Behavioral aspects of operations management,
 Decision making in public organizations.

Submission Instructions
Inquiries should be directed to the Guest Editors. Manuscripts should be submitted online directly. Please see instructions on the Journal’s website: http://onlinelibrary.wiley.com/journal/10.1111/(ISSN)1540-5915. Please select article type “Special Issue” and “Information and Operational Decision Sciences” while uploading your paper. Please indicate any of the Special Issue Guest Editors (Jose Benitez, Xin Luo, or Gyula Vastag) as your Senior Editor. All submitted papers will go through the rigorous peer-review system of the Decision Sciences Journal.

Tentative schedule
Initial paper submission deadline: January 1, 2018.
First round author notification: March 1, 2018.
Invited revisions deadline: May 1, 2018.
Second round author notification: July 1, 2018.
Final revision deadline: August 1, 2018.
Final author notification: September 1, 2018.
Projected publication: Fall 2018 or Spring 2019

Guest Editors’ Bios
Jose Benitez is an Associate Professor of IS at the University of Granada, Spain. He will be appointed in June 2017 as Full Professor of IS at Rennes School of Business, Rennes, France. Dr. Benitez is also a Visiting Professor of IS at the University of Twente, The Netherlands, and an Instructor of Partial Least Squares (PLS) Path Modeling at the PLS School. His research interests cover the study of how the firm’s portfolio of IT capabilities affects organizational capabilities and firm performance, and the development of PLS path modeling in the field of IS. His research has been published or is forthcoming in leading IS journals such as MIS Quarterly, European Journal of Information Systems, Information & Management, and Journal of Information Technology. He currently serves as Associate Editor for European Journal of Information Systems, and Information & Management.

Xin Luo is an Endowed Regent’s Professor and Associate Professor of IS and Information Assurance in the Anderson School of Management at the University of New Mexico, USA. He is the Associate Director of Center for Information Assurance Research and Education at the University of New Mexico. He received his Ph.D. in IS from Mississippi State University, USA. He has published research papers in leading journals including Decision Sciences, Decision Support Systems, European Journal of Information Systems, Communications of the ACM, Journal of the AIS, Journal of Strategic Information Systems, Information Systems Journal, Information & Management, and Computers & Security. He is currently serving as an Ad Hoc Associate Editor for MIS Quarterly and an Associate Editor for European Journal of Information Systems, Electronic Commerce Research, Journal of Electronic Commerce Research, and International Conference on Information Systems. His research interests center around information assurance, innovative technologies for strategic decision-making, and global IT management.

Gyula Vastag is Professor and Director of the Management Ph.D. Program at Szechenyi University (Győr, Hungary) and at the National University of Public Service (Budapest, Hungary). He co-authored five books (by Elsevier and Pearson), wrote eight business cases, contributed chapters to about 20 books, and published papers in a variety of peer-reviewed academic and professional journals in the United States and in Europe including – among others – Decision Sciences, Journal of Operations Management, Production and Operations Management, European Journal of Operational Research, European Management Journal, International Journal of Production Economics, and International Journal of Production Research. Gyula received several research awards: New Central Europe Distinguished Senior Researcher Scholarship (2014), Best Applications Paper Award by Alpha Iota Delta – The International Honor Society in Decision Sciences and Information Systems (2012), and Award for Research Excellence from Corvinus University (2009). He was the Founding Editor of the Pannon Management Review, is Associate Editor of Decision Sciences and serves on the editorial boards of the Central European Business Review, Business Research, Logistics Research, and International Journal of Quality Innovation. Currently, Gyula serves as President-Elect of the European Decision Sciences Institute and as Board Member of the European Operations Management Association.

Apr 24

Call for Papers – SPECIAL ISSUE in Computers & Industrial Engineering Novel applications of learning curves in production planning and logistics

CIEAims and Scope

Workforce demographics are changing. The cost of retaining skilled labor is becoming expensive, tasks more complex, and alternating between repetitive tasks is getting more common. Learning is primary in such environments and vital for the competitiveness of a firm.

Researchers have studied human learning (and its opposite phenomenon, forgetting) for many years acknowledging its complexity and its related processes. They have been interested in better understanding the types of human learning (e.g., cognitive vs. motor learning), the factors that influence it (e.g., task characteristics, interruptions in performing the task), and the environment within it occurs (individual learning vs. group learning vs. organizational learning), among others.

Starting with the seminal work of T.P. Wright, the first scientific approach to model learning in industry, researchers have also tried to formalize learning processes using so-called learning curves. In this context, they measure the reduction in the time required to complete a repetitive task. Forgetting curves, in contrast, measure the increase in task completion time following interruptions in the learning process. Learning and forgetting curves have been applied in many different areas, including production planning and control, military training, medical procedures, supplier selection, construction, manual order picking, technology management, maintenance, and more.

Learning and forgetting curve models are of high importance for manufacturing firms and can be valuable tools for managerial decision support. They can be applied as a performance measure, an aid in setting labor standards, or a forecasting tool, for example. In the literature, however, there especially seems to be a large research gap on new and innovative learning and forgetting curve models based on empirically collected data.

This special issue addresses this gap and aims to publish innovative approaches for the integration of learning and forgetting curves into mathematical models, simulation approaches or decision support models with a special focus on logistics and production and operations management. The special issue looks especially for works that develop quantitative models based on empirically collected data on learning and forgetting. In addition, authors are invited to submit novel applications of learning curves in particular for specific industries or environments (individual, group and organizational learning).

Topics may include, but are not limited to:

 Learning in teams
 Transfer of knowledge within groups and organizations
 Learning in light of demographic changes
 The impact of learning on human errors and quality
 Opportunities that facilitate learning in firms
 The stochastic nature of learning
 Workforce cross training and flexibility issues in manufacturing
 Learning, forgetting, and quality in projects
 Learning in emerging areas and industries; e.g., additive manufacturing.

The editors of the special issue intend to publish a range of different topics and reserve the right to limit the number of papers included in any one topic.

Submission

Manuscripts should be submitted via Elsevier Editorial System http://ees.elsevier.com/caie/.
Please indicate in the Article Type “SI: Learning Curves”. Manuscripts should not have been previously published nor be currently under consideration for publication elsewhere. For Guide for Authors, please refer to the webpage:

http://www.elsevier.com/wps/find/journaldescription.cws_home/399/authorinstructions.

Deadline

The submission deadline is December 31, 2017. Submissions to the special issue will be processed immediately upon receipt. The Special Issue is scheduled for publication in 2018.

Special Issue Editors:

Prof. Christoph H. Glock, TU Darmstadt, Germany. Managing Guest Editor (glock@pscm.tudarmstadt.de) Dr. Eric H. Grosse, TU Darmstadt, Germany (grosse@pscm.tu-darmstadt.de) Prof. Mohamad Y. Jaber, Ryerson University, Canada (mjaber@ryerson.ca) Prof. Timothy L. Smunt, University of Wisconsin, USA (tsmunt@uwm.edu)

Apr 21

Call for Papers: ISIR session at the 2018 ASSA

The International Society for Inventory Research (ISIR) sponsors a session each year at the ASSA meetings.  The 2018 meetings will be held in Philadelphia, PA on January 5-7, 2018. This year the session is being co-organized by Felipe Schwartzman of the Federal Reserve Bank of Richmond and Thomas Winberry of Chicago Booth.

The ISIR encompasses research not just on inventories but also on related areas such as plant and equipment investment, or inventory-theoretic approaches to other areas of economics, including money and liquidity.   Because your research has overlapped with these areas, we hope you will be interested in participating in the ISIR session next January.

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If you have (or expect to have) a paper that would be suitable for the session, please send an abstract or a draft of the paper by

May 31, 2017

to Felipe at felipe.schwartzman@rich.frb.org and
to Thomas at Thomas.Winberry@chicagobooth.edu.

 

James Kahn
Economics Section Head
International Society for Inventory Research

 

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