Dear Colleagues,
It is that time of the year again. As we head to the new academic year, INFORMS has released the 2019 Q1-Q2 journals statistics. Here is the Management Science's information:
Submission volume in Q1-Q2 of 2019 increased by 20% relative to the same period in 2018. There are two drivers of the submission volume increase: (i) the special issue on Data-Driven Prescriptive Analytics that received more than 200 submissions; and (ii) an increase in submissions to the Accounting department (26% relative to same period last year); Finance (24%); and Information systems (37%). It seems the journal is on its way to cross the 3000-submission line this year;
Acceptance rate is currently at about 9.2%, which is slightly below that of 2018 (9.7%);
Average Time-to-First Decision is about 67 days for regular papers and 28 days for Fast Track papers. Time-to-final decision-the time from submission of the original paper to the final decision made at the end of last round of review-is about 120 days, significantly below previous years;
Publication delay, the time from acceptance to publication in a hard copy, is around 14 months. This delay is similar to 2018 and is significantly lower than in 2017 (27 months);
Impact factor increased from 3.544 in 2017 to 4.219 in 2018.
All of these positive developments have been achieved at the same time the profession, in general, and Management Science, in particular, have gone through dramatic changes associated with the emergence of data, analytics and marketplaces. The paper "Learning in Repeated Auctions with Budgets: Regret Minimization and Equilibrium", Santiago R. Balseiro and Yonatan Gur, Management Science, 2019, Volume 65, Issue 9, is motivated by these changes, focusing on online advertising that are typically associated with large auction marketplaces, where budget-constrained advertisers compete in sequential auctions. I asked Nicolas Stier-Moses, Facebook Core Data Science and Ramesh Johari, Stanford University, Department of Management Science and Engineering, to reflect on the importance and contributions of this research. You can read their perspectives in the blog page of the journal.
The blog page of Management Science's web-site is a new feature implemented last year. Since that time, the journal has introduced a feature article in every issue and summarized the paper's contribution in a short blog. From time to time, the journal will ask industry and academic experts to reflect on the featured paper, as was done in the case of the paper by Balseiro and Gur.
Since last year, the journal and its web-site have exhibited many important changes, not the least of which are important guidelines and policies associated with the explosion of data used in many research papers published by the journal. Indeed, in a recent analysis done for Management Science's 65th anniversary, it was found that 60% of the 2018 published papers include real-world data and about 20% include laboratory data. These data-rich papers are critical for scientific progress and impact, but they raise ethical and replicability challenges.
For this purpose, Management Science established a new Data Provenance Policy, see here. Beyond the formal wording of the policy, it is important to clarify its intent. The objective of the policy is for authors to not use data obtained by means that materially harms individual, business, public sector, or societal interests. The journal has also established a Data and Code Replicability policy, see here to assure the availability of the material necessary to replicate the research published in the journal.
Both of these are works in progress that will be updated as we go through the implementation process. But one thing is clear, we need to make sure that we strike the right balance between the need to address ethical and replicability challenges and the freedom to pursue academic research. My question to you is "Do we have the right balance between these seemingly conflicting objectives?"
David Simchi-Levi
Editor-in-Chief
Management Science
E-mail: mseic@mit.edu
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David Simchi-Levi
Professor of Engineering Systems
Massachusetts Institute of Technology
Cambridge MA
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