Management Science is a scholarly journal that publishes scientific research on the theory and practice of management. The journal includes within its scope all aspects of management related to strategy, entrepreneurship, innovation, technology, and organizations as well as all functional areas of business, such as accounting, finance, information systems, marketing, and operations. The journal includes studies on organizational, managerial, group and individual decision making, from both normative and descriptive perspectives. The articles are primarily based on the foundational disciplines of computer science, economics, mathematics, psychology, sociology, and statistics, but cross-functional, multidisciplinary research that reflects the diversity of the management science professions is also encouraged. The journal interest extends to managerial issues in diverse organizational forms, such as for-profit and nonprofit firms, private and public sector institutions, and formal and informal networks of individuals. We welcome theoretical, experimental (field or lab) and empirical contributions.
The unifying thread of all Management Science articles is an analytical focus on improving the understanding of management. An acceptable manuscript must be relevant to the theory or practice of management, must meet high standards of rigor, and must be of broad interest to the community of management science scholars.
Abridged Statement of Editorial Policies
Articles submitted to Management Science must be readable and well organized and must exhibit good writing style.
The submission of a paper to Management Science for review means that the author certifies that the manuscript is not copyrighted, nor has it been accepted for publication (or published) by any peer-reviewed journal, nor is it being reviewed elsewhere. If the paper (or any version of it) has appeared or will appear in a non-peer-reviewed publication, the details of such publication must be made known to the editor-in-chief at the time of submission so that the suitability of the paper for Management Science can be assessed.
The review process in Management Science may result in an invitation to resubmit a rejected manuscript, that is, a “reject and resubmit” decision. Independent of the reason to resubmit a rejected paper, it is the policy of the journal that if a submitted manuscript is based on a paper previously rejected by Management Science, it is the responsibility of the author(s) to reveal this in the submission cover letter and to state clearly why resubmission is justified. Failure to do this is a violation of our ethical guidelines.
As a condition of final acceptance of a paper for publication in Management Science, the author(s) must indicate if their paper is posted on a working paper website other than their own. They are responsible for assuring that, if any part of the paper has been copyrighted for prepublication as a working paper, the copyright can and will be transferred to INFORMS when the paper has been accepted. This includes both print and electronic forms of the paper. Authors may post their working papers on websites after acceptance and prior to publication, as long as the sites are not copyrighted and do not serve as formal depositories. Management Science requires that at least one of the authors of each accepted article sign a Copyright Transfer Agreement.
Departmental Editorial Statements
Data Editor Mission
The task of the Management Science Data Editor is to help authors to get their published research compliant with the Management Science Data Policy. In particular, the Management Science Data Editor advises authors of published papers on the data, materials, and information to be provided to allow other researchers to replicate the original results.
The Management Science Data Editor and her/his team will also occasionally verify that all results reported in an accepted paper can indeed be reproduced using the provided data, materials, and information.
The Management Science Data Editor consults the Editor-in-Chief and the Editorial Board of Management Science on the journal’s strategy to promote reproducible and replicable research, in particular on the further development of the Management Science Data Policy as well as on procedures to ensure reproducibility and replicability of results reported in Management Science.
The Accounting department seeks to publish innovative research on how accounting information relates to issues that are relevant to corporate managers and/or practitioners (e.g., investors, analysts, creditors, consultants, regulators, etc.). We encourage submissions in all areas of accounting, but we particularly welcome papers that (i) explore new theories, new phenomena, and new data sets, without an overriding concern about perfect identification strategies or eliminating all possible sources of endogeneity; (ii) span the intersection of accounting and other fields, such as risk management, data science, strategy, operations, finance, entrepreneurship, healthcare, marketing, etc.; (iii) and/or embrace multi-method approaches, such as integrating mathematical theory with archival data analysis or combining survey, interview, and experimental evidence. We are receptive to papers that use whatever methodology is best suited to the research question, be it theoretical, empirical/archival, big data analytics, experimental, field surveys, interviews, or clinical/small sample.
Our goal is to be a competitive top-tier outlet for accounting-related papers. In a recent sOur goal is to be a competitive top-tier outlet for accounting-related papers. In a recent survey of senior accounting faculty, a majority of respondents said that school-level tenure committees and tenure letter writers perceive accounting papers in the journal to be top-tier publications. In order to continue to attract high-impact studies, we are committed to providing authors a fast and fair review process. We strive to both minimize the turnaround time on each round and reduce the number of rounds required to make an editorial decision. Our policy is to only use reviewers for two rounds. Thus, in almost all cases, authors will receive a minor revision or reject decision after submitting their first revision. We will also provide quick desk rejections for papers that are unlikely to be published due to contribution or topic fit. All reviews are double-blind, but we welcome suggestions from authors on appropriate potential reviewers and associate editors for their papers. Finally, we encourage authors to contact the department editors when they have papers that may be appropriate for the Fast Track process, which is designed for short papers with original, high-quality, and high-impact research.
The Behavioral Economics and Decision Analysis Department of Management Science seeks papers that promote the understanding of how decisions are and should be made by individuals and groups. Thus, the scope of the department includes topics that are in the domains of behavioral economics, decision analysis, and judgment and decision making. Papers must meet the rigorous standards of the journal. They must also be relevant to the science of management, by dealing with issues important to managers and executives and having the potential to impact management practice by providing insights into behavior and decision making.
Papers may consider individual and group decisions, judgments of likelihood and confidence, negotiations, game theoretic analysis of strategic interactions, as well as the design of markets, allocation rules, and decision architectures. Papers should be theory-based and may develop original theory, address problems of implementation, propose new models or methodologies, present empirical evidence either from the laboratory or from the field, and/or involve innovative or important applications. Descriptive theory papers should be integrative and account for both existing findings and new data. We particularly encourage multidisciplinary research that embraces different methodologies in the social and management sciences.
The Business Strategy department seeks papers with research questions that deepen our understanding of business performance. The department is interested in rigorous analyses that show how strategic choices impact performance, broadly construed, with special attention paid to persistent differences amongst competitors. The department will eschew papers that focus primarily on internal functions (e.g., finance or marketing), but welcome studies that deal with important strategic choices, such as choices about firm organization, governance, location, purpose, sustainability, nonmarket strategies, culture, employees, and human capital.
The primary criterion for consideration for publication in Management Science is the potential for impact on future study. This means that the research must conform to rigorous standards of quality in both theory development and empirical methodology and execution. We are agnostic as to the disciplinary origins of analysis. Given our primary criterion, this means that significant contributions may very well raise more questions than they answer and generate challenging and controversial findings with respect to the existing literature. The department also expects that published studies inform managers about the nature, characteristics, and implications of strategic choices that they can actually make.
Data Science (formerly Big Data Analytics)
Increased computational power and the explosion of data are rapidly changing the way and the extent to which organizations capture data, build models, and make decisions. A number of business decision problems impacted by this rapid change are germane to research in management science. The data science (DS) department (formerly, big data analytics) solicits research that advances our ability to solve complex business decision problems by learning from large datasets and complex environments.
Areas of interest include problems of dynamic optimization (e.g., how might we exploit structure in high dimensional exploration?); data-driven decision making (e.g., how might we make optimal decisions in the face of high-dimensional contextual data?); inference (e.g., how do we draw causal inferences from rich observational data? How might we design experiments on commerce platforms?); the interface with optimization (e.g., how can machine learning techniques benefit traditional online algorithms? How can integer programming techniques certify the robustness of deployed DS models?); and fairness and equity (e.g., how can bandit models increase efficiency and equity in hiring?). In all cases, we care particularly that the problem studied is soundly motivated by a relevant business or application context. This could range from problems in transportation, to healthcare, to social science contexts, and beyond. The department will be welcoming of any broad impactful application area. In exceptional research, the connection of the research to the motivating application context will be evident from an empirical study with data from the motivating context.
Recognizing the pace of research in the broader DS community, we particularly welcome submissions for which preliminary abridged versions of the research appeared recently in selective archival conferences (such as NeurIPS, ICML, COLT, and ICLR). Authors submitting such papers can submit, at their own discretion, all (anonymized) reviewer feedback from such conference submissions.
This department considers research on entrepreneurship and innovation. Entrepreneurship includes new business creation as well as entrepreneurial activities undertaken within existing businesses or through new market mechanisms. Innovation includes novel and creative ways to create value through new products or services, new business models, or new processes.
Topics of interest to this department include (but are not limited to) new venture formation processes, financing, and strategies; R&D and project management, performance metrics, and portfolio evaluation; regional and global dynamics of entrepreneurship and innovation; university and science-based innovation and technology transfer; continuous improvement and new process development; new product development, development processes, and service design; patents, licensing, and intellectual property; business model innovation (e.g., operations, marketing, or network innovation); open innovation and distributed innovation; and market and financial impact of innovation.
The department encourages submissions of shorter papers with high-quality original high-impact research that is of broader interest, analogous to what might appear in Science, Nature, or PNAS, and to what appeared more frequently in Management Science in its first decades. These may be a suitable vehicle to cover emerging (and possibly risky) topics such as AI/ML applications in personalized cancer therapy, VR/AR applications, cryptocurrencies, or applications of quantum computing (and other moonshots). Field studies are of particular interest, especially on contemporary topics of importance in society such as gender (or other) bias in entrepreneurship. Such papers should be no more than 5000 words (including references and everything else not provided in a separate supplementary appendix). Such papers will undergo faster review, with initial decisions being returned to the authors within 4 weeks. Papers will either be accepted with potentially modest revisions, or rejected. Authors invited to revise their work should do so within a matter of weeks; the final decision will follow within 1-2 weeks.
The principal review criteria for papers in the department are as follows: (i) Does the paper address a question of managerial importance and relevance? (ii) Does the paper use appropriate methodology to answer the question convincingly and rigorously? (iii) Does it change our thinking on an important topic in entrepreneurship or innovation? (iv) Does it provide improved management principles to operate new ventures or manage innovation?
Most successful papers will be grounded in a phenomenological understanding or built from illustrative examples. Papers that are purely theoretical or conceptual in nature must be well motivated and connected in a substantial and realistic way to a problem of managerial importance. Because most problems in entrepreneurship and innovation are inherently cross-functional or interdisciplinary or both, papers submitted to this department need not adhere to a particular disciplinary or methodological approach.
The goal of the Finance Department of Management Science is to be a competitive alternative to the top three journals in Finance.
All signs are positive that the finance department can achieve this goal. It is currently the fastest growing section of the journal. The interdisciplinary nature of Management Science encourages submissions of innovative work with high potential for long-run impact. Its strong commitment to a double-blind and fast review process, with quick desk rejections when the fit is poor, guarantees authors a timely, unbiased, and fair review process.
We continue to encourage submission of papers in all areas of finance, especially those that have the potential to influence financial practice, or those that provide innovative conceptual frameworks. We are particularly interested in papers that examine substantial issues in important emerging areas. Financial technology, which represents potentially transformative innovations such as blockchain, cryptocurrencies, and artificial intelligence applications, constitutes one such area. We look for thoughtful papers that are creative, insightful, and policy relevant. When it comes to theoretical work, we are looking for papers that change our way of thinking on important topics. When it comes to empirical work, we prefer papers that provide a balanced view of the evidence and acknowledge limitations of their empirical designs. We welcome well-crafted papers that provide rigorous identification of existing theories. In addition, we welcome papers that break new ground even when identification is not perfect. Finally, we look for papers that break new ground on the empirical-methodological front, especially those that develop and analyze innovative statistical methods that harness advances in areas such as machine learning, computation, algorithms, and big data. Such papers may very well cut across departmental boundaries.
Management Science Finance Paper Collection: Management Science has compiled a list of papers published in the journal in the last few years in its Finance department. These papers cover all major fields of financial research and hence the website can serve as a resource for research done by the Finance and Management Science communities. Our hope is that these papers will inform policy, impact education, and motivate new research. Click here to see the collection.
The department invites submissions that advance knowledge of how to better organize and manage the delivery of healthcare services in developed, emerging or developing economies. Papers will offer rigorously evaluated insights that have the potential for practical impact. Impact can be measured in many ways, including implementation with statistically and practically significant results, recommendations that can be directly applied in practice, as well as results which will shift the standard manner in which healthcare practitioners (e.g. senior managers, clinicians, policy makers) think about their systems and challenges.
Papers should focus on significant challenges of healthcare management and derive motivation from real-world challenges facing existing healthcare systems/providers. This may include improving patient access, improving outcomes and patient experience, reducing costs, reducing errors, managing demand, optimizing patient flow, measuring and improving population health, optimizing public health programs, leveraging technology, engaging the workforce, developing new business models, improving alignment and coordination between organizations, or improving organizational learning and innovation capabilities.
The department encourages submissions that engage with current industry trends and their managerial challenges, such as the digitization of patient records, genomics and precision medicine, machine learning to guide care decisions, value-based healthcare, integrated care, patient empowerment, behavior, and choice.
Papers may draw on theory across disciplines, as appropriate for the problem addressed, and use statistical, modelling or experimental methodologies. The department particularly welcomes papers that exploit large, granular datasets and leverage the emerging field of data analytics.
Criteria for publication are (i) the paper’s potential for practical impact, (ii) the strength of its analysis and evidence, (iii) the originality of its main insight. Both modelling and empirical papers are expected to include an intuitive explanation of the main insights (the “story” of the paper) that is digestible by practitioners. This may be done in the introduction or hypothesis development section (if applicable). The department prefers short and focused papers. The submission must include a brief nontechnical executive summary for senior healthcare leaders, explaining the paper’s main insight and its potential for practical impact (max 200 words).
Digital technologies have become an important agent of change in the economy with transformative implications at the individual, organizational, societal, and macroeconomic levels. Accordingly, the objective of the Information Systems Department is to publish groundbreaking and distinctive research that addresses the design, adoption, use, and the impacts of all forms of digital technologies (broadly defined) with clear managerial and theoretical implications.
Research submitted to the Information Systems Department may draw on a wide variety of disciplines including economics, mathematics, psychology, sociology, computer science, and statistics. Research methods may include economic modeling, operations research modeling, experiments, and analyses of archival, survey, or field data. We are also interested in the development of predictive analytics that clearly combine a methodological advance with an important and novel managerial application. Regardless of reference discipline or research method, all research published must meet a high standard of rigor and credibility, and the results should be of broad interest to management scholars and represent an advance in the frontier of knowledge.
The Marketing Department seeks to publish papers that address both substantive and theoretical marketing issues. We are very interested in current topics, such as consumer search, mobile marketing, and digital marketing, as well as core marketing issues such as marketing strategy, product line management, new product development and launch, design and management of distribution channels, sales-force management, pricing, advertising, promotions, buyer behavior, and demand estimation. Because of the journal’s cross-functional readership, the Marketing Department particularly welcomes interdisciplinary work on the interface between marketing and other functional areas.
We are open to a diverse set of methodologies and paradigms including surveys, experiments, econometric and statistical data analyses, structural econometric models, analytical models, machine learning tools and algorithms, and applications. While we are open to a broad set of methodologies, we look for manuscripts that apply the chosen methodology rigorously.
The best manuscripts address problems that are both important and faced by a broad segment of marketing practitioners. Furthermore, they make an original and significant contribution to the marketing literature. A theoretical manuscript should add to our understanding of consumers and firms, but also have clear relevance to marketing practice. A methodological manuscript should provide new methods leading to superior actions relative to existing methods. An empirical manuscript should provide either new empirical generalizations or new insights that can improve marketing practice. An applications-oriented manuscript should describe implementation of leading-edge methods or models that can have significant managerial consequences.
The Operations Management Department of Management Science publishes research in established and newly emerging areas of operations management. Examples of established areas include process design and improvement, production planning, quality management, inventory management, supply chain management, logistics, services operations, and project management. Examples of emerging areas include additive manufacturing; automation, such as self-guided vehicles; digital technologies, such as blockchains and Internet of Things; emerging market operations; homeland security; humanitarian operations; machine learning and artificial intelligence; marketplaces, platforms, and the sharing economy; and sustainable and responsible operations. The department particularly seeks to promote research in emerging areas and using new methodologies.
Research papers could address operational questions from diverse perspectives, such as those of line workers, operations managers, C-suite executives, third-party providers, or policy makers.We encourage studies related to different organizational contexts, such as private, public, or nonprofit sectors. Research articles pertaining to industry-specific studies and those evaluating innovative business models are also encouraged.
The department also welcomes interdisciplinary work at the interface of operations with other disciplines, such as economics, finance, healthcare, innovation, marketing, organizational behavior, and strategy. Such manuscripts would be judged based on their contributions to the field of operations management. For example, a paper on healthcare operations would be expected to have a significant operations relevance and implication for it to be considered by the department.
Research papers should seek to provide substantive academic contributions and practically relevant insights and algorithms. We expect manuscripts to be of interest to a broad audience in operations management and, ideally, beyond. Thus, the contribution of a manuscript will be evaluated based on whether it delivers novel and relevant insights to theory and practice or solves important operational problems.
The department encourages all types of research methodologies—analytical, empirical, and behavioral. Successful analytical papers will use sound modeling techniques or creative algorithms drawn from the fields of mathematical optimization, stochastic processes, statistics, simulation, and game theory. In empirical research, the range of methodologies and datasets has been expanding rapidly. A successful empirical paper will have an appropriate research design, use sound statistical analysis, and relate its research questions and methods to operations management theory of interest. A successful behavioral operations paper will have theory and experiments or field-based research drawing on operations management and applications of behavioral economics and decision analysis in operations. Ideally, authors should aspire to validate their models, the insights, and the algorithms. Rigorous execution is necessary, but is not sufficient for publication. The department welcomes both regular-length and Fast Track submissions. Click here to read more about the Fast Track submission process.
The Optimization Department seeks contributions on all aspects of optimization and its applications, especially papers that bridge the gap between different fields and connect the dots to bring new tools and perspectives to bear on classical problems. All accepted papers are therefore expected to score high in at least one of the following questions: (i) How realistic and significant is the application studied? (ii) How original/ creative is the approach adopted? (iii) How original/creative is the optimization methodology? (iv) How significant is the impact in practice?
As in all other major optimization journals, a nonexclusive list of methodologies that the department covers is as follows: convex optimization (including linear optimization); general-purpose nonlinear optimization; discrete optimization (combinatorial, integer, and mixed-integer programming); optimization under uncertainty (dynamic programming, stochastic programming, robust optimization, simulation-based optimization); infinite-dimensional optimization; online optimization; equilibrium problems and game theory.
Especially welcome are contributions studying new and significant applications of optimization methods in new important fields such as business analytics, data-driven optimization, automation, AI in supply chain and manufacturing, internet economy and fintech, to name a few. Accepted papers should seek to provide substantive academic contributions and practically relevant insights and algorithms. Importantly, authors should aspire to validate the models, the insights, and the algorithms.
Authors are expected to write succinctly, with a clear articulation of their contributions and main results so as to address a broad audience of scholars interested in optimization methods and applications.
The department will also consider fast track publication of shorter papers with high-quality original high-impact research that is of broader interest. Papers in this track will ask and answer original questions by rigorous but simple methods. Traditional technical notes, with a limited focus and narrow appeal, will not be considered for this track.
The Organizations Department welcomes submissions relevant to the internal operations and design of firms and other organizations. Papers of interest would include those that examine the dynamics of groups and teams, address formal and informal structures within firms, clarify implications from managerial policies, as well as study how organizations draw on and respond to their environments, such as through hiring processes. We are receptive to a broad range of methodological approaches and theoretical perspectives and particularly appreciate papers that connect or integrate those perspectives.
Manuscripts will be assessed in terms of the extent to which they (i) are of broad interest to the community of management scholars, (ii) advance our theoretical or empirical understanding of organizations, (iii) exhibit high standards of rigor, (iv) have useful implications for decisions being made by practitioners, and (v) address socially and economically important questions and settings. Advances to our understanding of organizations might come from identifying novel mechanisms or processes, resolving theoretical or empirical puzzles, or using novel data, methods, or research designs to adjudicate between competing perspectives or to challenge established beliefs or prior empirical results. Papers on understudied yet important settings, such as developing countries, are welcomed, as are those that implement advances from computational social science.
Rigor implies that manuscripts should implement methodological best practices and address key alternative explanations or interpretations of the results. Causal inference should be addressed and accurately represented for all empirical results. Manuscripts should also explicitly consider the relevance of their results to the decisions being made by managers or policymakers.
Revenue management has traditionally been concerned with managing scarce capacity by using pricing mechanisms and demand management as an operational tool. The practice of revenue management is taking hold in many industries and takes various forms: capacity allocation controls, dynamic pricing, dynamic bundling, bargaining and negotiated pricing, customized pricing, assortment optimization, auctions, and so on. From industries such as airlines, hotels and rentals, these days we also see such practices in e-commerce, taxis, energy, railways, and road pricing.
The rise of platforms is another new phenomenon. Recent trends point to an unprecedented level of control over the design, implementation, and operation of markets: more than ever before, we are able to engineer the platforms governing transactions among market participants. As a consequence, market operators or platforms can control a host of variables such as pricing, liquidity, visibility, information revelation, terms of trade, and transaction fees. On its part, given these variables, market participants often face complex problems when optimizing their own decisions. Operational study of these platforms and their design is still in their infancy and we encourage submissions studying these markets, both from the perspective of the market operator and the market participants.
We seek well-written papers that are grounded around important applications and have potential for impact on practice. The types of contributions we seek are broad, ranging from improving the understanding of the application domain at-hand, opening up new relevant problem areas, to devising novel algorithms or uncovering new insights. We look for a mix of approaches including modeling, theoretical, empirical, and computational.
The department also encourages Fast Track submissions; click here to read more about the Fast Track submission process.
The Stochastic Models and Simulation Department seeks to publish work that contributes to the modeling, analysis, or simulation of stochastic systems, broadly construed, through advances in methodology and/or application. These advances may stem from the development of new methods and models and/or creative applications of existing ones. The department seeks to attract papers that contribute to the science or practice of management through stochastic modeling.
In terms of methodological areas, the department is interested in a broad range of topics that pertain to the management of stochastic systems and more broadly address decision making under uncertainty. Examples of relevant problem areas include manufacturing, inventory and production management, delivery of healthcare services, service operations, revenue management, financial engineering, and information services. Methodological contributions to these areas may take the form of novel analytical, computational, simulation-based, or statistical methods. The department is also interested in contributions that emphasize new applications of stochastic methods. We are particularly eager to attract papers in emerging application areas with a strong stochastic modeling content, for example, sharing economy markets, management of online matching platforms, socially responsible operations. Furthermore, we welcome papers that focus on the synergies between traditional operations research methods and adjacent fields, such as statistics, economics and public policy, and their implications on the design and analysis of stochastic systems.
The department places particular emphasis on the originality and breadth of the approach as well as the quality of the results. Ideally these should transcend the specifics of the motivating problem but at the same time should remain grounded and avoid focusing on abstract theory per se. Although rigor plays an important role in assessing submissions, it is by no means sufficient, and a greater premium is placed on the novelty of the problem being studied and its overall importance and value to the Management Science community.