Dear Colleagues,
It has been ten months since the new editorial board has implemented major changes in Management Science. We now have enough data to assess the impact of these changes. First, let me review some obvious statistics.
- Submission Volume: Submission volume in the first three quarters of the year is similar to last year and is projected to be 2880 submissions by the end of the year. As of today, almost 200 of the submitted papers are Fast Track papers. The departments that received most of the submissions so far in 2018 are Decisions Analysis, Finance, and Operations Management.
- Mean Time to First and Final Decision: There is a significant difference between regular and Fast Track papers. Average time to first decision for regular papers is 58 days (similar to 2017) while average time for Fast Track papers is 21 days. Average time to final decision for regular papers is 130 days (again similar to 2017).
- Acceptance Rate: The current acceptance rate of 9.4% is comparable to last year (9.8%). Some departments e.g. Finance, have much lower acceptance rate (7.5%).
- Time from Acceptance to Publications: At 20 months in 2018, this delay is significantly lower than 2017 (27 months); however, it is still too long. The editorial board is working with INFORMS to try reduce it further.
The article "Patient Triage and Prioritization Under Austere Conditions," by Zhankun Sun, Nilay Tanık Argon, and Serhan Ziya, published in the October issue of the journal exemplified the type of papers the journal is looking for. These are papers that deal with issues and problems important to managers and executives; they must be interesting to a wide range of people in the management science community; and they should have the potential to impact management practice. In their paper, the authors are focusing on daily emergency department operations where triage and prioritization help allocate limited resources to patients who need them the most, in an effort to save as many lives as possible. The authors develop a mathematical framework to capture the essential features of the problem, the trade-off between the time spent on acquiring more information and that spent on acting on the available information. They are able to characterize the optimal policy, which specifies if/when to use triage, and this depends on patients' clinical severity, service requirements, as well as the current patient mixture.
While there is no shortage of great articles in Management Science, and the statistics suggest that the state of the journal is healthy, some important challenges have emerged. These challenges are not unique to Management Science, but being a flagship journal of INFORMS demands that we are among those who lead the way in trying to address them. These challenges include:
- Replicability of Results: A fundamental principle of the scientific method is replication: the validity of a research finding requires that other researchers can reproduce it. For that reason, the editorial board has established a committee of senior academics from various disciplines to develop Management Science's Data and Code disclosure policy. Such a policy may help advance areas covered by the journal. Indeed, sharing of data and codes will be of value to the relevant research community, allowing them to leverage this prior work in their own pursuits. This sharing should increase the rate of scientific progress and impact.
Management Science already established a data disclosure policy (put in place in 2013), which encourages, but does not require, the disclosure of data associated with published manuscripts. The focus of the committee was to strengthen the policy so we achieve the objectives outlined above.
The members of the committee are Yan Chen (DE for Decision Analysis), Kay Giesecke (Finance), Stephen Graves (Chair and former EiC MSOM), Hamid Nazerzadeh (Big Data Analytics), Sridhar Tayur (Entrepreneurship & Innovation), and Juanjuan Zhang (Marketing). I hope to share the proposed policy with the Management Science community after the INFORMS meeting and receive feedback so we can finalize before the end of the year.
- Data Provenance Policy: Recently, we have seen a few submitted papers that raise legal and ethical questions. For example, papers that apply scraped data from a variety of websites, some of which ban such practice; or papers that use fake accounts to generate data. In response to these developments, Management Science has modified its data provenance policy; see Data Provenance 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.
Finally, I look forward to seeing many of you in the upcoming 2018 INFORMS Annual Conference in Phoenix, AZ.
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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