Mission Statement

Decision science is entering a new era in which decisions are no longer made solely by humans, but increasingly by autonomous AI agents and human-AI collectives. While prior research has largely treated AI as a tool for prediction or support, agentic AI systems now decide, act, learn, and coordinate, fundamentally altering the nature of decision-making within a firm, or across firms in the value, or within an ecosystem. Agents can act simultaneously as decision-makers, environments for other agents, and sources of emergent system-level behavior, producing outcomes that are often unpredictable from the perspective of human reasoning. Even when humans remain in the loop, the decision-making unit has shifted: agents are not mere tools. They are autonomous actors whose capabilities, goals, and interactions fundamentally transform the dynamics, structure, and evaluation of decision systems. The purpose of launching the new department is to fundamentally redefine the focus of analysis in decision science: from purely human decision-making to agentic AI & human-agent collaboration.

Hence, we invite papers from both academia and industry on “Agentic AI and Human-Agent Collaboration in Business” across any facet of business. We call for research in two complementary directions, wherein: (1) agents are decision-makers (in single-agent or multiple-agent systems), as well as (2) human-agent collaborations. We especially encourage papers that use an experimental approach, both in lab experiments and field experiments, to facilitate a replicable research paradigm in the “Decision Sciences in the Agentic AI and Human-AI Collaboration in Business” era and foster a fast-evolving community. We also encourage agent-driven research approaches that use agents as designers for experiments and systems and vehicles for knowledge creation and discovery. We hope to foster more agent-related research (agentic AI and human-agent collaboration) and agent-driven research (using agents to design experiments and decision systems). Together, these directions position Decision Sciences to lead the study and design of decision-making in an increasingly agentic world.

Departmental Editors

Associate Editors and Editorial Review Board Members

Mission Statement

The AI and Data/Business Analytics: Methodologies and Applications department of the Decision Sciences Journal seeks to publish high-quality, methodologically rigorous research that advances the frontier of artificial intelligence and data-driven decision-making. Suitable submissions should introduce novel models and/or methodologies for important classes of business decision problems or develop innovative, generalizable approaches that leverage existing analytics and empirical methods in new ways. We particularly encourage work that integrates artificial intelligence and machine learning into operational decision processes.

As data became central to organizational operations in the decade of 2010, new opportunities emerged to uncover insights and to construct models that better capture underlying data-generating mechanisms. State-of-the-art analytics often require combining machine learning with analytical modeling and optimization to support more effective operational decisions. However, until GenAI models were introduced in late 2010 and came into public consciousness in 2022, the focus of business analytics was on structured data with a relatively small focus on the vast majority of organizational data - unstructured data. In light of these developments, desirable submissions to this department go beyond traditional econometric analyses, deterministic or stochastic models with externally predicted inputs, or purely data-mining-focused studies. Instead, they should demonstrate how data-enabled information informs new modeling paradigms and choices, yields operational insights, or leads to improved business policies.

Research addressing human, organizational, and policy issues in the context of AI is critically important, as these dimensions often represent the most challenging and consequential barriers to effective AI adoption. For example, organizations frequently grapple with questions about what should be permitted or restricted when developing internal RAG systems. Such governance considerations—balancing technical capabilities with human judgment, ethical responsibilities, and policy constraints—are among the most commonly cited challenges by practitioners.

Theoretical or mathematical contributions along the lines of machine learning and artificial intelligence research, such as novel convergence analysis (both statistical and optimization), algorithmic innovations, online and reinforcement learning research, as well as data integrity issues arising from ML/AI techniques such as data privacy and fairness, are welcome in this department, provided that clear and convincing business or managerial motivations or applications are provided. General machine learning or AI research that does not have clear or specific applications in business or management questions, on the other hand, is discouraged.

While strong theoretical development is welcome, authors must clearly articulate the business value of data-enabled decision making, especially in settings with limited data or evolving environments. Empirical methods—broadly defined to include machine learning, statistical modeling, econometrics, and related techniques—must be central to the contribution. Ultimately, submissions should address meaningful decision-making challenges and demonstrate methodological rigor, novelty, and generalizability.

Departmental Editors

Associate Editors and Editorial Review Board Members

Mission Statement

Operations: We aim to advance the scientific understanding and practical application of AI to enhance decision-making, operational efficiency, and organizational performance. We particularly welcome theory-driven research that develops innovative AI methodologies in domains such as supply chain management, logistics, healthcare, and platform management.

Information Systems: AI technologies are transforming firms, labor, and institutions, creating unprecedented opportunities for efficiency while raising important questions about ethics, equity, and organizational integrity. We aim to publish high-quality Information Systems (IS) research that brings socio-technical perspectives to these developments, opens the “black box” of AI, and tackles consequential problems facing business and society. We welcome diverse approaches within the IS discipline, including computational design science, the economics of AI and IS, behavioral research, and other rigorous perspectives that advance understanding at the intersection of business and technology.

Marketing: We seek to publish high-quality research that integrates advanced AI methods into marketing research to elevate customer insight and support evidence-based decision-making. This includes, but is not limited to, the use of AI-driven analytics, automation, and adaptive communication tools to study customer needs, examine personalization strategies, and evaluate the performance of marketing activities. We aim to develop innovative empirical approaches that enhance market responsiveness, improve resource allocation, and contribute to value creation, while placing strong emphasis on relevance by producing findings that resonate with a broad audience and offer actionable guidance for managerial decision-making.

Departmental Editors

Associate Editors and Editorial Review Board Members

Mission Statement

The Analytical Studies in Supply Chain Management Department is dedicated to advancing both the theory and practice of supply chain management through the development, analysis, and application of rigorous analytical methods. Its mission is to publish high-quality research that examines strategic and operational decision-making within supply chains and provides managers, practitioners, and policymakers with analytically-sound insights. The department welcomes manuscripts that offer guidance for effective decision-making and provide insights for managing contemporary challenges such as supply chain resilience, risk management, sustainability, logistics, sourcing, digitalization, and global operations.

The department is committed to fostering scholarship that employs analytical methods – including but not limited to modeling, optimization, simulation, statistics, stochastic processes, game theory, econometrics, and emerging data-driven approaches – to generate substantive insights for enhancing supply chain performance. Recognizing the value of methodological diversity, the department also encourages mixed-method studies that integrate analytical modeling with empirical and behavioral methods, when such combinations deepen theoretical insights or strengthen the practical relevance of findings.

The department values manuscripts that are methodologically sound, theoretically grounded, and practically relevant. By promoting innovative analytical perspectives and interdisciplinary engagement, Analytical Studies in Supply Chain Management aims to shape the future of supply chain research and contribute to more effective decision-making in organizations and society.

Departmental Editors

Mission Statement

The department focuses on research in operations & supply chain management that is behavioral in nature. Behavioral research in these areas is growing, and very important to further our understanding of operations and supply chain management in practice. The department will consider theoretical and methodological approaches to behavioral research that are rooted in economics or psychology.

Papers submitted to the department must demonstrate (a) a micro-focus on the behavior or decisions of individuals or small groups of individuals, (b) a point of view that allows such individuals to deviate from rational behavior, and (c) a focus on a context in operations and supply chain management.

Rational behavior has three properties: (1) individuals are motivated by self-interest, usually expressed in monetary terms, (2) they act in a conscious and deliberate manner, and (3) they behave optimally for a specified objective function. Behavioral research studies observed or potential violations of these three properties, plus other factors that influence decisions. For example, studying social preferences and social comparisons is behavioral in nature; similarly, studying emotions at work is behavioral.

The department welcomes submissions using various research methodologies, including experiments, field research, surveys, modeling, and system dynamics. All submissions must be of high quality and clearly justify why the chosen methodology is appropriate for the research question. If a study involves human subjects, authors must explain and justify the decision to use (or not use) participant incentives. All submissions must focus on problems relevant to operations or supply chain management. Research on other topics will only be considered in exceptional circumstances.

Departmental Editors

Mission Statement

This department seeks to publish high-quality, methodologically rigorous research that addresses novel business issues in retail operations as well as consumer behavior and analytics, both within the retail sector and in broader contexts. Since these problems often cut across multiple business domains, the editorial team is open to papers with an emphasis on operations management, information systems, marketing, or any combination/intersection of those areas.

There is particular emphasis on retail operations reflecting the complex and fast-evolving nature of the industry, and how retailers increasingly use data and analytics to incorporate consumer behavior into their decisions. This gives rise to a broad range of new operational challenges including, but not limited to, managing online and omnichannel operations, feature-based forecasting, assortment optimization, pricing and logistics of returns, store-level execution and customer service, innovative pricing and promotion strategies, and shelf-space and display optimization. 

All research methodologies are welcome – analytical, empirical, experimental, case study, or field/action-based – provided that research objectives are well defined and framed, research designs are crafted to meet those objectives in a methodologically rigorous way, and research conclusions are derived from deductive and inductive reasoning applied within the scope of the research design.

While we welcome manuscripts focusing on applications of AI technologies, the authors must make a compelling case that this department, instead of the other AI-focused departments of the journal, is more suitable. More specifically, the authors must demonstrate that the core contributions of their work concern consumer behavior and/or retail operations.

Departmental Editors

Mission Statement

Technological innovation is rapidly reshaping decision-making, markets, and value networks, often disrupting existing industries and altering fundamental operational models. This department welcomes research on disruptive technologies, broadly defined–those that transform business processes, introduce new products or services, or enable novel business models–when their primary impact involves operations, supply chains, or related decision processes.

We are particularly interested in work that demonstrates how such innovations fundamentally shift operational design, execution, or performance. We also encourage research on technological disruptions beyond traditional settings, including applications in finance, information systems, marketing, accounting, and broader societal domains.

Methodologically, the department is problem-driven and inclusive, welcoming empirical, analytical, experimental, and simulation-based work that rigorously examines transformative technological phenomena.

Departmental Editors

Departmental Editors

Mission Statement

The "FinTech and OM-Finance Interface" Department is dedicated to advancing innovative, rigorous scholarship at the nexus of finance, operations management, and financial technology (FinTech). The Department fosters research that uncovers how operational decisions shape financial performance, how financial markets, instruments, and incentives drive operational strategy, and how FinTech innovations, such as digital payment systems, blockchain technologies, and data analytics, transform both business operations and financial management. We seek innovative empirical, analytical, and theoretical work that illuminates cross-functional dynamics, such as risk management, supply chain finance, working-capital optimization, FinTech-enabled operations, real-options–driven decision-making, and the financial implications of operational resilience and sustainability. By bringing together researchers across these interconnected disciplines, the Department aims to deepen understanding, inspire new methodologies, and spark impactful insights that inform bothmanagerial practice and academic inquiry.

Departmental Editors

Departmental Editors

Mission Statement

Humanitarian Supply Chains, Nonprofit Operations, and Disaster Management advances rigorous, relevant decision sciences to address community and individual needs in a world facing more frequent, severe, natural, man-made, and protracted crises. In today's world, amid current and anticipated funding constraints, humanitarian and nonprofit organizations are asked to do more with less. At the same time, shocks and disruptions are magnified by complex, tightly interconnected systems—globally linked supply networks, volatile funding flows, data gaps and interoperability constraints, and climate risk. This moment calls for research that is ambitious in scope, methodologically strong, and valuable to practice.

We invite contributions that extend both the theoretical and practical foundations of operational and supply chain decision-making in humanitarian and nonprofit organizations. We also welcome work that tackles big, high-impact problems across the disaster lifecycle—mitigation, preparedness, response, and recovery. Submissions may employ analytical modeling (OR/MS), econometrics and data science, simulation, system dynamics, experiments (lab/field), qualitative and case research, design science, or multi-method and interdisciplinary approaches. We value studies that are theory-informed, transparent in assumptions and data, and explicit about decision implications.

Papers should be connected with real problems in actual contexts and deliver decision-relevant insights supported by robust evidence—through implementation results where feasible, or validated analyses (e.g., field data, experiments, simulation, or well-grounded theory). We value credible indicators of impact, such as measurable improvements in cost, quality, speed, equity, resilience, or carbon footprint. Topics of interest include (but are not limited to): coordination and governance across public–NGO–private actors; funding and procurement mechanisms under constraints; localization and equity; information/visibility; climate adaptation and risk; technology and innovation at scale. We particularly encourage collaborations with operational partners and perspectives and data from the Global South.

Our ambition is twofold: to advance the academic literature of this critical field, and to produce knowledge that significantly improves practice in humanitarian and nonprofit operations—so that scarce resources reach people faster, more fairly, and more sustainably.

Departmental Editors

Mission Statement

The Information Systems and Technological Interventions department invites papers that create new knowledge on the creation, adoption, use, and implications of information technology interventions at the societal, organizational, group, or individual levels. We welcome rigorous analytical approaches grounded in any research method, including qualitative and quantitative. We are particularly interested in groundbreaking work that makes contributions to both theory and practice. We also welcome research that makes innovative methodological contributions. Topic areas include, but are not limited to the following:

• The role of information systems (IS) in generating data, information, and knowledge:
  • Big data and analytics
  • Management of knowledge
  • Ideation through information technologies
• Processes associated with information technology interventions:
  • IS adoption, diffusion, continuance, and discontinuance
  • IS planning, development, and implementation
  • Management and design of IS projects
• Human aspects of information technology interventions:
  • Security, privacy, resilience, deviance, and ethics of IS
  • Human computer interactions, design issues, gamification, and design science
  • IS and user behavior, such as resistance, interactivity, engagement
  • Healthcare IT
• IS platforms and implications:
  • Social media, digital platforms, and digital collaboration
  • Online communities and online shopping
  • Peer-to-peer and crowdsourcing markets
• Broader implications of IS:
  • Strategy, structure, IT governance, and organizational impacts of IS
  • Global and cross-cultural IS Issues
  • E-business and e-government
  • Business value and economics of IS
• Artificial intelligence (AI) and algorithms:
  • Algorithmic management, governance, and decision-making
  • Human-AI and AI-AI interactions
  • Process automation and innovation
  • Content moderation
  • The use and impact of AI and machine learning, especially in organizational contexts
• Blockchain and cryptocurrency:
  • Transaction fee mechanism designs
  • Decentralized Autonomous Organization (DAO)
  • Non-Fungible Tokens (NFTs)
  • Transparency and traceability services in retail and supply chain
  • Crypto-based funding
  • Blockchain infrastructure

Departmental Editors

Mission Statement

The Operations, Information Systems, and Supply Chain Management in Emerging Economies department seeks to advance scholarly understanding of operations, information systems, and supply chain management within the unique contexts of emerging economies. Recognizing the dynamic challenges and opportunities of these rapidly evolving markets, the department focuses on research that addresses resource constraints, institutional voids, technological transition, and socio-economic transformation.

The department emphasizes the development of innovative theories, methods, and solutions that are tailored to the distinctive characteristics of emerging economies. By bridging global best practices with local insights, the department strives to contribute to both the academic literature and the practical management of operations, information systems, and supply chains in these critical regions, which serve as global growth engines.

The department seeks to become the leading platform for high-impact research that addresses the intersection of operations, information systems, and supply chain management within emerging economies, fostering sustainable, inclusive, and transformative practices that contribute to global economic and social development.

The department welcomes submissions that explore a wide range of topics related to operations, information systems, and supply chain management in emerging economies. Examples of focus include, but are not limited to:

1. Operations Management in Emerging Economies: Managing operational efficiency in resource-constrained environments. Process innovations for low-cost, high-impact service and manufacturing operations. Strategies for operational resilience in markets prone to disruptions (e.g., political instability, natural disasters, or pandemics).

2. Information Systems in Emerging Economies: Development, adoption, and impact of digital platforms and technologies (e.g., mobile payment systems, blockchain, AI-driven solutions). Role of information systems in bridging institutional voids and enabling inclusive economic participation. Cybersecurity, data privacy, and ethical concerns in digital ecosystems.

3. Supply Chain Management in Emerging Economies: Sustainable and socially responsible supply chain practices in emerging markets. Smart supply chain management empowered by big data and artificial intelligence. Supply chain resilience in emerging economies. Construction and governance of supply chain ecosystems. Supply chain finance with agentic AI. Demand planning with social network analytics. Autonomous warehouses and fulfilment centres.

4. Cross-Disciplinary Interfaces: Intersections of operations, information systems, and supply chain management with public policy, international trade, and global value chains. Leveraging technology for supply chain integration and coordination across fragmented markets. Analysis of socio-economic impacts of operations and supply chain initiatives in emerging economies.

Departmental Editors

Mission Statement

The Platforms and Sharing Economy department seeks to publish high-quality and impactful research on digital platforms, sharing and gig economies, and the pivotal role of data-driven systems and AI algorithms in their operations, value creation, and governance. We welcome rigorous theoretical and empirical studies that explore the operational, economic, organizational, societal, and ethical dimensions of platform-mediated markets, including multi-sided market design; pricing, matching, and recommendation systems; AI development and deployment; creator economy; AI-enabled automation; labor displacement; platform governance, and market power. Through critical yet constructive scholarship, we aim to deepen understanding of the business models, competitive dynamics, and societal implications of platform ecosystems, while informing evidence-based policy, responsible algorithmic design, and strategies for sustainable growth in an AI-enabled economic landscape.

Departmental Editors

Mission Statement

The Pricing and Revenue Management Department promotes the use of analytical and empirical research tools to study how organizations better match the supply of goods and services with demand over time. In addition to traditionally emphasized pricing, inventory, and capacity decisions, contemporary research considers a broad set of decision levers and market features, including information structure, personalization and recommendation systems, matching mechanisms, liquidity and marketplace design, algorithmic decision-making, and the use of machine learning and artificial intelligence. The department welcomes research that examines these developments, as well as the implications of such developments for strategic behavior, transparency, fairness, and privacy. The scope of the department spans a broad range of applications, including transportation, hospitality, retail, digital platforms, online marketplaces, advertising and media markets, and sharing-economy models.

The department welcomes innovative research and applications that enhance our understanding of increasingly complex market conditions and promote the dissemination of best practices. These may include, but are not limited to, theoretical and economic models that yield managerial insights, methodological contributions that advance existing approaches or address new classes of problems, empirical or behavioral studies that validate existing theory or examine market phenomena, and data-driven and AI-assisted approaches that leverage recent technological advances. In line with the editorial mission of Decision Sciences, the department places strong emphasis on rigor, clarity of exposition, and practical relevance, with the goal of advancing both scholarly knowledge and managerial decision making in pricing and revenue management.

Departmental Editors

Departmental Editors

Mission Statement

Public policy plays a crucial role in shaping the structure, governance, and operations of global supply chains to achieve outcomes in the general public interest, for instance:

Efficacy: Because supply chains span multiple countries, legal systems, and regulatory environments, coherent, well-designed public policies are essential to ensure that goods, services, and resources (financial capital, labor, and technological capabilities) move efficiently while protecting public interests. Trade policies, customs regulations, and infrastructure investments directly affect the cost, speed, and reliability of cross-border flows, thereby influencing firms’ decisions about production location and logistics strategies.

Social responsibility and transparency: Regulations on labor standards, environmental protection, and product safety help prevent a “race to the bottom” in which cost pressures and market competition lead to unsafe working conditions or environmental harm. Policies on transparency and due diligence increasingly require firms to monitor their suppliers, promoting responsible sourcing and accountability across multiple tiers of production.

Resilience: Public policy also strengthens supply chain resilience in the face of global shocks, including pandemics, geopolitical conflicts, and climate-related disruptions. Governments can use strategic stockpiles, diversification incentives, industrial policy, and international cooperation to reduce overreliance on single suppliers or regions. In this way, public policy not only supports economic competitiveness but also safeguards national security, public welfare, and sustainable development in an interconnected global economy.

There may be other desired outcomes. We invite submissions – using any methodology – that focus on the need, implementation, impact, or critical evaluation of public policies related to global supply chains and their role in achieving intended outcomes.

We recognize overlaps with other departments; therefore, if a manuscript does not consider public policy related to supply chains—ideally in a global context—it should be submitted to another department. All manuscripts must address the managerial or policy relevance of the insights and findings.

Departmental Editors

Associate Editors and Editorial Review Board Members

Mission Statement

The Service Operations Department of the Decision Sciences Journal aims to advance rigorous and impactful research on the design, management, and analysis of service systems. We seek to publish work that deepens understanding of and offers meaningful insights into decision-making in service settings across industries and organizational contexts.

The department welcomes research that studies processes, capacity, quality, and innovation within service operations, as well as their interactions with pricing, information, technology, policy, customer behavior, and agent incentives. We are particularly interested in work that recognizes the defining features of services, such as demand uncertainty, capacity constraints, time sensitivity, and co-production, and examines how these features shape operational decisions and performance outcomes.

We encourage a broad range of methodological approaches, including (but not limited to) analytical modeling, empirical and econometric analysis, field and laboratory experiments, simulation, and data-driven or computational methods. Interdisciplinary research that connects service operations with marketing, economics, information systems, and public policy is especially welcome.

The Service Operations Department values contributions that:

• Develop novel theories or models of service systems;
• Provide credible empirical evidence on service operations;
• Offer actionable insights for managers and policymakers.

Through these contributions, the department seeks to foster scholarship that not only advances academic knowledge but also informs practice and policy in an increasingly service-driven economy.

Departmental Editors

Mission Statement

The Social Technology and Social Media for Operations Department aims to publish high-quality, theory-driven, and methodologically rigorous research that advances the understanding of how digitally mediated social interactions shape organizational operations, decision-making, and broader societal outcomes. Serving as an intellectual interface between Operations Management and Information Systems research, the department welcomes submissions from OM and IS scholars whose work speaks to decision processes, system designs, and performance implications.

At its core, the department focuses on the design, management, and operational use of social technologies and social media as socio-technical systems, with particular emphasis on how information is generated, curated, transmitted, interpreted, and acted upon. Illustrative topics include content generation and diffusion, network structure and metrics, enterprise and platform-based social systems, value creation through social and digital technologies, and the design and operation of social media strategies and campaigns. A central theme of the department is the evolving role of artificial intelligence in social technologies and social media operations. This includes research on algorithmic content curation, platform governance, social AI agents, and the managerial and operational implications of human–AI collaboration in social and platform-based environments. The department also seeks contributions on information quality, misinformation, trust, and governance, recognizing their critical importance for effective and responsible social media. 

Methodologically, the department encourages diverse approaches, including analytical modeling, econometrics, machine learning, experiments, field studies, design science, and computational social science. Submissions may draw on data from public, enterprise, or platform-based social systems and may address applications across business, public sector, and societal contexts.

Departmental Editors

Mission Statement

The Sourcing, Procurement/Purchasing, and Logistics Department seeks to develop and publish papers that significantly enhance the sourcing/procurement/purchasing and logistics bodies of knowledge. Procurement papers focusing on traditional topics such as sourcing (e.g., global, strategic, sustainable, ethical, single/multiple), supplier selection, supplier development, buyer-supplier relationships, buyer-supplier exchanges, reverse auctions, supply-base complexity and supplier risk (among others) are certainly a good fit. Likewise, logistics papers focusing on traditional topics such as transportation, third-party logistics providers, distribution, shipper-carrier exchanges, and warehousing (among others) are appealing to the department. Furthermore, papers centering on broader issues that include key procurement or logistics content are welcomed by the department. Within this domain, research may examine key phenomena that drive (e.g., omni-channels, responsiveness, agility, JIT), shape (e.g., public policy), enable (e.g., technology), protect (e.g., continuity, security), hinder (e.g., disruptions), or reverse (e.g., recalls) product flow.

Departmental Editors

Mission Statement

The department invites research papers that address strategic, tactical, and operational issues in any stage of supply chains, where there is a significant analysis of environmental and social impacts, in addition to the usual economic implications in the research questions. Environmental and social impacts may be aligned with any of the United Nations 17 Sustainable Development goals; of particular interest are goals 6 (clean water and sanitation), 7 (affordable and clean energy), 10 (reduced inequalities), 11 (sustainable cities and communities), 12 (responsible consumption and production), 13 (climate action), and 17 (partnerships for the goals). If a manuscript does not contain an analysis of environmental and/or social impacts, it is not considered to be a fit, and it should be submitted to another department.

All manuscripts must address practically relevant, well motivated business/industry problems and are expected to have a rigorous analysis that contributes to practice and academia. The manuscript must highlight the managerial relevance of the insights and findings. The department welcomes all research methodologies.

Examples of topics include (in no particular order), but are not limited to:

• The circular economy.
• Closed-loop supply chains, including remanufacturing, recycling, and reuse of products post-consumer use.
• Impact of environmental regulations on operations.
• Environmental and social responsibility in supply chain management, including responsible sourcing.
• ustainable product design.
• Sustainable development and operations of critical infrastructure and essential services, including energy, water, food, information technology, and transportation.
• Servicizing and the sharing economy.
• Socially responsible platform operations.
• Access, affordability, and equity implications of operational decisions.
• Accounting for and measuring sustainable operations and supply chain.
• Quantifying the impact of sustainable operation and supply chain on shareholder value.

Departmental Editors