INFORMS Open Forum

Dear friends and colleagues,

You may be interested in the article below where I reflect on the last six years as the Editor-in-Chief of Management Science. You can also read the editorial in the attachment or in Management Science: https://doi.org/10.1287/mnsc.2023.editorial.v69.n12.

As I point out in the article, "the emergence of Management Science as the dominant journal in our field would not have been possible without the major changes and bold initiatives" implemented by the editorial board in 2018. And this could not have been achieved without your support and contributions!

Thank you all so much.

Best

David

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From the Editor: Reflections on the Last Six Years as Editor-in-Chief of Management Science

David Simchi-Levi 

With this issue of Management Science, the second term of the editorial board is coming to an end. For me personally, this year marks the culmination of a 15-year stint as the editor-in-chief of Management Science (2018–2023), one of the two flagship journals of INFORMS; editor-in-chief for Operations Research (2006–2011), the second flagship journal of INFORMS; and for Naval Research Logistics (2003–2005). This is an opportunity to assess the reputation, stature, and health of the journal. It is also an opportunity to reflect on trends that I have observed over the past decade, particularly as they relate to Management Science.

The State and Stature of Management Science

When I became editor-in-chief of Management Science in January 2018, I announced (Simchi-Levi 2018, p. 2) major changes and bold initiatives aimed at ensuring that,

"the journal better serves and promotes research conducted by the entire INFORMS community and is true to its name, a journal dedicated to the science of management, with the ability to impact practice and the ability to serve as the top outlet for new big-idea, high-impact papers."

These changes and initiatives include the following:

• Changing departmental structure: This includes the introduction of three new departments-Data Science, Healthcare Management, and Revenue Management and Market Analytics-and the integration of three departments-Decision Analysis, Behavioral Economics, and Judgment and Decision Making-into a single department named Behavioral Economics and Decision Analysis.
• Repositioning of existing departments: Finance has expanded its footprint to include financial technology (fintech) and machine learning as well as cryptocurrency-related technology. Information Systems has also expanded its footprint to include the development of predictive analytics which combine a methodological advance with an important and novel managerial application. Organizations expanded its coverage to include computational social science methods. Finally, Accounting now emphasizes papers that span the intersection of accounting and other fields important to Management Science such as risk management, data science, strategy, operations, finance, entrepreneurship, healthcare, and marketing.
• Promoting diverse methodologies: Management Science now focuses on a healthy mix of Computer Science, Operations Research, Social Science, and Statistics, so that the journal is truly interdisciplinary, publishing scientific research on the theory and practice of management. Most importantly, data science is now embedded in almost every Management Science department, both through the recruiting of appropriate Department Editors and the position of each department as reflected in department statements.
• Replication initiatives: Management Science implemented a new data and code disclosure policy with the objective of ensuring the availability of the material necessary to replicate the research published in the journal. In parallel, the journal initiated two large-scale replication projects, one of which has already resulted in a published paper.
• Introducing the Fast Track submission process: This is a submission process designed to attract short papers with high-quality, original, and high-impact research that is of broad interest. The intent is to rapidly disseminate high-quality work while maintaining Management Science's publication standards.

I am pleased to report that these changes and initiatives have made a significant impact on the journal and the management science community.

1. Submission Volume

The number of new submissions in 2023, including both Regular and Fast Track papers, is projected to be around 4,100. This will mark the third year in a row where submissions will exceed 4,000 papers; see Figure 1 below.

Figure 1. Original Submissions: Regular and Fast Track Papers
*Note. Data for 2023 represents projection based on Regular and Fast Track papers submitted between January 1, 2023, and September 30, 2023.

Two observations:

• The largest departments in terms of submission volume include Finance, Behavioral Economic and Decision Analysis, Operations Management, and Accounting.
• Significant submission volume for Fast Track papers. Indeed, in 2018, when the new submission channel was introduced, Management Science received 244 Fast Track submissions. This year, the Fast Track submission volume is expected to nearly double. We are pleased to see that several other INFORMS journals (Operations Research, Manufacturing & Service Operations Management, and Marketing Science) have recently introduced similar submission channels.

2. Review Cycle Time

Management Science continues to maintain an outstanding cycle time. Indeed, the median time to first decision for Regular and Fast Track papers sent to reviewers (that is, not including desk reject papers by department or associate editors) is 85 days, whereas the median time to final decision is 112 days. In particular, the time to final decision represents significantly shorter response times than similar statistics just a few years ago.

The average time to first decision across all Regular papers submitted is 70 days. The data suggest that Fast Track delivers on its commitment to provide authors with a faster review, with initial decisions returned to most authors in fewer than 31 days.

Finally, 39% of the submitted papers are desk rejected without going to reviewers. This is important because it provides authors with quick feedback that can be applied when submitting to other journals.

3. Acceptance Rate

Management Science's acceptance rate this year across all submissions (Regular and Fast Track) is 13.1%, which is slightly higher than the acceptance rate in recent years; see Table 1. This acceptance rate is not uniform across all departments, with some departments, for example, Entrepreneurship and Innovation, having a lower acceptance rate, whereas other departments, for example, Information Systems, and Revenue Management and Market Analytics, have a higher acceptance rate.

Table 1. Acceptance Rate Based on Manuscripts with Decisions in a Specific Year

In addition, the bar is significantly higher for Fast Track papers. This is reflected by its 7.8% acceptance rate, which is significantly lower than that of Regular papers.

4. Interest in Papers Published in Management Science is Growing

Interest in papers published by Management Science has increased dramatically in recent years. For example, the number of downloaded papers has almost tripled, from 576,894 downloads in 2017 (before the new board was appointed) to 1,575,914 downloads last year.

Papers published in Management Science influence policy at the highest levels of decision making. For example, last year the 2022 U.S. Economic Report of the President (ERP 2022), which "presents an overview of the nation's economic progress and makes the case for the Administration's economic policy priorities," cited three papers published by Management Science (see Simchi-Levi 2023). This year, the 2023 Economic Report of the President (ERP 2023) cited another four Management Science papers:

• Brynjolfsson E, Hu Y, and Smith M (2003) Consumer surplus in the digital economy: estimating the value of increased product variety at online booksellers. Management Sci. 49(11):1580–1596.
• Brynjolfsson E, Smith M (2000) Frictionless commerce? A comparison of internet and conventional retailers. Management Sci. 46(4):563–585.
• Lambrecht A, Tucker C (2019) Algorithmic bias? An empirical study of apparent gender-based discrimination in the display of STEM career ads. Management Sci. 65(7):2966–2981.
• Farronato C, Fong J, Fradkin A (2023) Dog eat dog: balancing network effects and differentiation in a digital platform merger. Management Sci., ePub ahead of print, February 24.

I was also thrilled to see that the 2022 U.S. Economic Report of the President refers to supply chain resiliency concepts developed by INFORMS members, including Supply Chain Stress Tests, Time-to-Recover, and Time-to-Survive, and cites two of my papers in this area, Simchi-Levi (2020) and Simchi-Levi and Simchi-Levi (2020).

Perhaps another reason for the increased interest in Management Science papers is the 2018 introduction of the Featured Article (FA) initiative.

The initiative allows the editor-in-chief to identify articles to highlight in a given issue. During my term as the editor-in-chief, I have used a mix of criteria to identify FAs, including broad appeal, importance of the topic, and impact on the practice of public and private organizations. INFORMS supports this initiative by allowing free downloads of FAs for a short period of time, typically one month.

5. Ranking and Impact Factor

Management Science has been and continues to be one of the most prestigious journals in the field. As Tinglong Dai and I pointed out in a July 2023 blog (Simchi-Levi and Dai 2023), Clarivate's impact factor calculations are in transition, making it difficult to decipher important insights and trends about INFORMS journals this year. Indeed, of the 12 INFORMS journals with at least two years of impact factor history, 10 experienced declines. On average, the impact factors of INFORMS journals fell by nearly 12%. In that blog, Tinglong and I provided a detailed explanation of this decline.

However, improvements can be seen by looking at the 2023 Google Scholar Metrics. Indeed, Management Science's h5-index has increased from 109 last year to 114 this year, and the h5-median has increased from 165 to 173. These two metrics count citations for papers published in the last five complete calendar years. As you can see in Table 2 below, Management Science continues to rank very high compared with the rest of the 24 journals used for the University of Texas at Dallas (UTD) Top 100 Business School Research Rankings.

Table 2. Google Scholar Metric for Journals on the UTD List

Journal	h5-index (2023)	h5-median (2023)
Journal of Financial Economics	123	202
The Review of Financial Studies	119	200
Management Science	114	173
Journal of Finance	104	173
Academy of Management Journal	90	128
Strategic Management Journal	88	146
Journal of International Business Studies	78	123
Academy of Management Review	76	120
Journal of Marketing	71	124
MIS Quarterly	71	129
The Accounting Review	69	105
Manufacturing and Service Operations Management	67	102
Journal of Consumer Research	62	101
Production and Operations Management	62	96
Information Systems Research	60	82
Administrative Science Quarterly	56	102
Operations Research	56	82
Journal of Accounting and Economics	55	98
Journal of Marketing Research	54	92
Organization Science	54	79
Marketing Science	52	80
Journal of Accounting Research	51	75
Journal of Operations Management	44	73
Journal on Computing	35	48

Note. Data sources: Google Scholar Metrics, updated in July 2023 https://scholar.google.com/citations?view_op=top_venues.

6. Significant Reduction in Journal Backlog

A challenge faced by the journal is to control the time from acceptance to publication. When the new editorial board was appointed in January 2018, the backlog was 27 months (2017 backlog). Collaborating closely with INFORMS, the backlog has been continuously reduced and it is currently around nine months. This is important because it affects authors' satisfaction and citation counts.

Updates on Major Initiatives

Below is a report on some of the major initiatives introduced by the journal.

1. The Replication Project

You may recall from my January 2020 editorial (Simchi-Levi 2020, p. 2) that the editorial board initiated the following challenge to the management science community:

"The editorial board would like to publish a paper, likely a Fast Track paper, that reports replicability of laboratory experiments published by Management Science. This was done in economics (Camerer et al. 2016) and in social science (Camerer et al. 2018), and it is time to do the same for Management Science papers."

A team of eight academics with significant experience in behavioral operations collaborated on this replicability challenge. The team includes members from five institutions with established laboratories, which allowed us to conduct each replication at multiple sites. The faculty involved were Andrew Davis, Cornell University; Blair Flicker, University of South Carolina; Kyle Hyndman and Elena Katok, University of Texas at Dallas; Samantha Keppler and Stephen Leider, University of Michigan; and Xiaoyang Long and Jordan Tong, University of Wisconsin.

The team collected survey results from the community, asking participants to vote for the papers they would like to see replicated. The papers were in the following five areas: inventory management, supply chain contracts, queueing, forecasting, and sourcing. The team selected two papers with the highest number of votes from each category, for a total of 10 papers. Each paper was replicated at two different sites.

I am pleased to report that the paper describing the project (Davis et al. 2023), "A Replication Study of Operations Management Experiments in Management Science," was accepted by Management Science and published in the September 2023 issue of the journal. I asked two behavioral economists familiar with recent replication studies in the social sciences, Colin F. Camerer (California Institute of Technology) and Yan Chen (University of Michigan), to reflect on the significance and implications of the paper for our community, in general, and for behavioral research, in particular. Their perspectives can be found on the journal's blog page.

2. Data and Code Disclosure Policy

In 2019, Management Science introduced a Data and Code Disclosure Policy (https://pubsonline.informs.org/page/mnsc/datapolicy), with the goal of ensuring the availability of materials necessary to replicate the research published in the journal. Early in 2020, the editorial board appointed a data editor, whose responsibility is to ensure that accepted papers comply with the Data and Code Disclosure Policy and to verify the ability to replicate results published in the journal.

The journal's code and data editor, Ben Greiner, and his team of associate editors (Miloš Fišar, Christoph Huber, and Ali Ozkes) recently completed reviews of more than 730 replication packages.

Statistics on Reviewed Papers.

The number of reviewed replication packages has been steadily increasing, with about 350 packages in the last 12 months alone. For context, in 2022, Management Science had 4,200 submissions and accepted 430 papers. Reviewed papers come from all Management Science departments. About 60% of the reviewed articles are based on empirical data. About 18% report laboratory, online, field experiments or surveys, and 22% are theory/simulation papers (typically with code but no data).

For papers using data sets, 68% use Stata, and 20% use R. For code-only papers, Matlab and Python are the most popular software environments, along with R; see Figure 2 for details. However, across all replication packages, the reviewers encountered a wide variety of statistical and computational tools, including, for example, C/C++, C#, Excel, Fortran, Gams, Gauss, Gurobi, Java, Julia, Jupyter, Lingo, Maple, Mathematica, Perl, SPSS, SQL, and Stan.

Figure 2. The distribution of software and code for papers with data

Among all papers that include data, 52% of the eventual replication packages include all data necessary for replication, whereas the others rely on proprietary data. Of the papers with proprietary data, 47% of the data sets are publicly accessible (e.g., via subscription services such as WRDS or Compustat), and 33% of the papers provide at least sample or synthetic data. The Code and Data review team was able to check whether the code runs through without errors for 64% of all replication packages. However, they do not check the soundness of methods or the actual results; this is left to the academic community.

Statistics on the Review Process.

There has been a significant improvement in the number of submitted replication materials that could be accepted as is, without further revisions, increasing from 28% to 43% in the last year. Most replication packages, however, required one revision round, while last year about 10% required two or more rounds before the replication package was approved (compared to 19% in the first two years). Most often, the code and data editor had to ask for better documentation (variable dictionaries, data set construction instructions, code documentation, etc.). Sometimes parts of the code were missing. Code errors are not uncommon; about a one-third of the submitted and tested codes have had issues. See details in Figure 3.

Figure 3. Number of revisions and issues detected in replication packages

The code/data review process at Management Science requires about 30 days (median 16) from the time a paper is submitted for review until it is approved. A paper spends about 14 days (median 8) with the code and data editor and his team and about 16 days (median 3) with the authors for revising their replication materials. In the first two years, the processing time was about 24 days; however, due to significant increase in workload over time, it increased to 36 days in the last year (6 more days with the code and data editor, 6 more days with the authors than the first two years).

In 5% of the papers reviewed, the authors identified issues that required changes in the paper while revising their replication materials. However, all of these identified issues were minor (rounding errors, typos, etc.), and the changes were approved by a department editor. This number is a slight improvement from the previous 6%.

In summary, the Code and Data review at Management Science is active and provides important value to the management science community. Importantly, we see improvements over time as our community understands the importance of providing replication packages with data, code, and documentation. In fact, in the last year or so I have received feedback from authors that they are now focusing on preparing their code and data at the start of, and throughout the entire, research project, not just at the end. This is such an important change in mind-set as we ensure that results published in Management Science have a broad credibility.

3. Management Science Reproducibility Project

In this project, we aim to quantify the reproducibility of results published in Management Science articles submitted before and after the new Data and Code Disclosure Policy came into effect.

The project is well underway. Initially, the editor-in-chief sent emails to Management Science reviewers asking for volunteers; 927 researchers registered and provided information about their area of expertise (typical departments), software skills, and data access.

Since the beginning of February 2023, 675 volunteer reviewers have provided 753 reproducibility reports on 459 articles published in Management Science. Of these, 40 articles had replication packages that were accepted prior to the implementation of the 2019 policy, and 419 articles were submitted and accepted after the implementation.

The team is now completing a manuscript describing the entire process, analysis, and the results. But we already have a few initial observations. Among papers with data/code that were submitted and accepted after the implementation of the 2019 Code and Data Policy, more than 95% of the articles could be fully or largely computationally reproduced.

This is not the case for papers accepted before the 2019 policy was implemented. In that case, authors were not required to submit replication packages, and those who did (40 papers), did so voluntarily. Even though this is a highly biased sample, the review team was able to replicate only 55% of the pre-2019 policy papers.

As we pointed out when introducing the 2019 policy, "A fundamental principle of the scientific method is replication: the validity of a research finding requires that it can be reproduced by other researchers." The statistics provided above underscore the importance of the new policy in ensuring that research findings are replicable.

Emerging Trends in Management Science

From my vantage point as the editor-in-chief, I have observed a tremendous change in the nature of papers submitted to Management Science over the past few years. Indeed, four trends have emerged:

• Practically relevant and theoretically elegant research: The data suggest that papers published in Management Science come from all areas of research, and they employ diverse methodologies. I am particularly proud of the innovative papers published by Management Science, many of which are practically relevant and theoretically elegant. One beautiful example is the paper "Design of lotteries and wait-lists for affordable housing allocation" (Arnosti and Shi 2020). The paper is motivated by the use of lotteries or wait-lists in the allocation of affordable housing to applicants. The authors show that some of the widely used allocation systems fail to match applicants with suitable housing, and they go on to propose several alternatives that achieve better matching. I asked Professor Paul Milgrom from Stanford University and Professor Martin A. Lariviere of Northwestern University to reflect on the importance and contributions of this research. You can read their perspectives on the blog page of the journal.
  
  
• Data-driven research: A significant portion of the papers published in the journal over the last decade present data-driven research using laboratory data, syntactic data, or real-world data. Many of these papers apply real-world data to formulate problems and identify research opportunities. Such papers enable the community to develop new engineering and scientific methods that explain, predict, and change behavior (Simchi-Levi 2014).

At the same time, data driven research presents a significant challenge to our community, as is exemplified by the current challenge faced by the behavioral science community, whose research's credibility is at risk, see Lewis-Kraus (2023). In fact, in April 2019, three years before the current crisis in behavioral science, Management Science established a new Data and Code Disclosure Policy with the goal, simply put, of eliminating or preventing the replication crisis that the editorial board was concerned with. We hope that this policy will increase authors awareness for the need to document code and data throughout their research projects, not just when they are ready to submit a paper for publication. We also hope that our policy will serve as a blueprint for other INFORMS journals to ensure that we protect our collective reputation and the credibility of our research.

• Interdisciplinary research: As Hopp and Simchi-Levi (2021) pointed out, Management Science has traditionally been more multidisciplinary than interdisciplinary. However, interdisciplinary research has now been on the rise since the current editorial board has used special issues as a vehicle to promote interdisciplinary research. Indeed, in the last six years, the journal focused on five special issues, each of which involved multiple Management Science departments. For example, the special section of Management Science on business and climate change published in this issue spans a wide range of management science disciplines. Similarly, the work-in-progress special issue on human-algorithm connection involves many of the journal's departments and includes coeditors representing Behavioral Operations, Data Science, Finance, Information Systems, Marketing, Market Design, and Operations Management.
  
  
• Fast Track papers: As pointed out by Simchi-Levi (2018) and Hopp and Simchi-Levi (2021), this submission process is designed to attract short papers with high-quality, original, and high-impact research that is of broad interest. In 2018, Management Science received 240 submissions to Fast Track, whereas this year the number is expected to be around 400 papers, which represents 10% of all submitted papers. An important objective of this submission process is to encourage department editors to accept high-risk, high-reward papers that claim to make significant, paradigm-changing contributions (Hopp and Simchi-Levi 2021). To achieve that goal in this submission process, the board has limited the role of referees to checking for technical correctness and clarity so that the process places subjective judgements in the hands of the department editors, allowing them to take appropriate risks. A second important goal of this submission process is to expedite the review and publication of papers with immediate public impact, in particular, healthcare-related publications. One such example is the paper "Controlling epidemic spread: reducing economic losses with targeted closures" (Birge et al. 2022), which proposed a spatial epidemic spread model to study the COVID-19 pandemic and recommend policy responses to curb epidemic spread. The paper was accepted 10 months after initial submission and received significant media coverage, including from the New York Times and The Associate Press, even prior to acceptance.

Taken together, these trends suggest that current research has shifted toward solving more relevant, interdisciplinary problems, using rich data sets, and applying a variety of techniques, from computer science and statistics to social science and operations research. It also demonstrates the willingness of the editorial board to take risks and accept (potentially) high-reward papers that are of interest to the entire management science community.

The nature of these trends and the emergence of Management Science as the dominant journal in our field would not have been possible without the major changes and bold initiatives described in this article. To embrace this resurgence in management research and to uphold and enhance the credibility and reputation of work published in Management Science, it is my hope and belief that under the new editorial board, the journal will continue to (i) serve and promote the best research conducted by the entire management science community, and (ii) strengthen our community's bold efforts to ensure reproducibility of research and transparency and availability of data.

Special Thanks

I am grateful to all of those who have made the accomplishments reported here possible. First and foremost, I thank the editorial board including more than 40 senior researchers, hundreds of associate editors, and thousands of referees! Without their dedication, hard work, and tremendous efforts, Management Science would not to be in such an excellent shape, with a huge increase in submission volume, a large impact on and interest from practitioners, shorter review time, and effective processes designed to address current and future crises. The significant changes and bold initiatives reported here would not have been possible without the support of the entire management science community.

Second, I would like to express my deepest appreciation to the INFORMS staff for all of their support over the past six years, and in particular, Matthew Walls, the director of publication.

Third, one person deserves special thanks from all of us: the journal's managing editor, Toni Riley. She has provided timely and tireless support to authors, reviewers, and the editorial board. Without her capable hands, none of us would have been able to do our jobs or achieve our goals for the journal.

I hope you will agree that the hard work of all these individuals has ensured that the journal's reputation, stature, and health are strong!

References

1. Arnosti N, Shi P (2020) Design of lotteries and wait-lists for affordable housing allocation. Management Sci. 66(6):2291–2307.
2. Birge JR, Candogan O, Feng Y (2022) Controlling epidemic spread: Reducing economic losses with targeted closures. Management Sci. 68(5):3175–3195.
3. Brynjolfsson E, Smith M (2000) Frictionless commerce? A comparison of Internet and conventional retailers. Management Sci. 46(4):563–585.
4. Brynjolfsson E, Hu Y, Smith M (2003) Consumer surplus in the digital economy: Estimating the value of increased product variety at online booksellers. Management Sci. 49(11):1580–1596.
5. Camerer CF, Dreber A, Forsell E, Ho T-H, Huber J, Johannesson M, Kirchler M, et al. (2016) Evaluating replicability of laboratory experiments in economics. Sci.351(6280):1433–1436.
6. Camerer CF, Dreber A, Holzmeister F, Ho T-H, Huber J, Johannesson M, Kirchler M, Nave G, Nosek BA, Pfeiffer T, et al. (2018) Evaluating the replicability of social science experiments in Nature and Science between 2010 and 2015. Nature Human Behav. 2:637–644.
7. Davis AM, Flicker B, Hyndman K, Katok E, Keppler S, Leider S, Long X, Tong JD(2023) A replication study of operations management experiments in Management Science. Management Sci.69(9):4977–4991.
8. Economic Report of the President (2022), https://www.whitehouse.gov/wp-content/uploads/2022/04/ERP-2022.pdf.
9. Economic Report of the President (2023), https://www.whitehouse.gov/wp-content/uploads/2023/03/erp-2023.pdf.
10. Farronato C, Fong J, Fradkin A (2023) Dog eat dog: Balancing network effects and differentiation in a digital platform merger. Management Sci., ePub ahead of print, February 24,  https://pubsonline.informs.org/doi/10.1287/mnsc.2023.4675.
11. Hopp WJ, Simchi-Levi D (2021) Management science: The legacy of the past and challenge of the future. Management Sci. 67(9):5306–5316.
12. Lambrecht A, Tucker C (2019) Algorithmic bias? An empirical study of apparent gender-based discrimination in the display of STEM career ads. Management Sci. 65(7):2966–2981.
13. Lewis-Kraus G (2023) They studied dishonesty. Was their work a lie? The New Yorker (September 30), https://www.newyorker.com/magazine/2023/10/09/they-studied-dishonesty-was-their-work-a-lie.
14. Simchi-Levi D (2014) OM research: From problem driven to data driven research. Manufacturing Service Oper. Management16(1):2–10.
15. Simchi-Levi D (2018) From the editor. Management Sci. 64(1):1–4.
16. Simchi-Levi D (2020) Three scenarios to guide your global supply chain recovery. Sloan Management Review, April 13, 2020.
17. Simchi-Levi D (2023) From the Editor-January 2023. Management Sci. 69(1):1–6.
18. Simchi-Levi D, Dai T (2023) Unraveling the mystery of impact-factor declines in most of INFORMS journals. INFORMS Open Forum (July 11), https://bit.ly/informs23if.
19. Simchi-Levi D, Simchi-Levi E (2020) We need a stress test for critical supply chains. Harvard Business Review, April 28, 2020.

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David Simchi-Levi
Professor of Engineering Systems
Massachusetts Institute of Technology
Cambridge MA
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