operation management research paper

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Operations Management Research Paper Topics

Operations management research paper topics encompass a wide array of subjects related to the effective planning, organizing, and supervision of business operations. These topics offer a rich field of inquiry for scholars and practitioners alike, reflecting the complexity and centrality of operations management in modern business. This page is designed to provide students with comprehensive guidance on operations management research, including a categorized list of research topics, insights into choosing and writing on these topics, and exclusive writing services by iResearchNet. Whether you are a beginner or an advanced researcher in the field, this resource aims to support your exploration of the diverse and dynamic world of operations management.

100 Operations Management Research Paper Topics

Operations management is a multifaceted field that integrates various aspects of business like production, logistics, quality control, and much more. For students looking to delve into research, here’s an extensive list of topics categorized into ten different sectors.

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• The role of technology in enhancing production efficiency.
• Sustainable production practices and their impact on profitability.
• Mass customization in modern manufacturing.
• Just-in-time (JIT) production: Pros and cons.
• Managing production lines for optimal workflow.
• The influence of automation on manufacturing processes.
• Ergonomics and production management.
• The future of 3D printing in manufacturing.
• Outsourcing production: Challenges and opportunities.
• Lean manufacturing principles and their application.

Supply Chain Management

• The importance of information sharing in the supply chain.
• Risk management in global supply chains.
• Ethical considerations in supply chain management.
• Impact of e-commerce on traditional supply chain models.
• Inventory management: Best practices.
• The role of transportation in the supply chain.
• Achieving sustainability through green supply chain practices.
• The influence of big data on supply chain decisions.
• Cross-border supply chain challenges.
• Vendor management and strategic partnerships.

Quality Control and Assurance

• Total Quality Management (TQM) in the 21st century.
• Six Sigma methodology in operations management.
• Quality assurance in the food and beverage industry.
• Role of continuous improvement in quality management.
• Balancing cost and quality in manufacturing.
• Role of customer feedback in quality assurance.
• Impact of quality control on brand reputation.
• Quality assurance standards in healthcare.
• The relationship between employee training and quality control.
• Quality management systems: ISO 9001 and others.

Logistics Management

• Technological advancements in logistics and distribution.
• Managing logistics in e-commerce.
• Impact of globalization on logistics management.
• Third-party logistics (3PL) vs. in-house logistics.
• Green logistics: Integrating sustainability.
• Humanitarian logistics in disaster management.
• Role of government regulations in logistics.
• Challenges of urban logistics.
• Reverse logistics: Principles and practices.
• The future of drone technology in logistics.

Service Operations Management

• The importance of customer experience in service operations.
• Managing service quality in the hospitality industry.
• Service blueprinting as a tool for service design.
• Role of technology in enhancing service efficiency.
• Balancing supply and demand in service industries.
• The application of lean principles in service operations.
• Innovations in healthcare service operations.
• Ethical considerations in service provision.
• Outsourcing services: A strategic perspective.
• Transforming traditional services with digital technologies.

Strategic Operations Management

• Aligning operations strategy with business goals.
• The role of operations management in organizational success.
• Developing a competitive advantage through operational excellence.
• Integrating innovation into operations strategy.
• Global strategies in operations management.
• The role of leadership in strategic operations management.
• Operations strategy in small and medium-sized enterprises (SMEs).
• Evaluating the performance of an operations strategy.
• Mergers and acquisitions: Integrating operations.
• Strategic considerations in outsourcing operations.

Sustainability and Environmental Considerations

• Incorporating sustainability into operations management.
• Environmental regulations and their impact on operations.
• Waste management practices in manufacturing.
• Achieving energy efficiency in operations.
• Sustainable practices in supply chain management.
• The role of corporate social responsibility (CSR) in operations.
• Life cycle assessment in product design.
• Sustainable procurement practices.
• The green factory: Myths and realities.
• Social sustainability in operations management.

Technology and Innovation Management

• The impact of Industry 4.0 on operations management.
• Implementing Artificial Intelligence (AI) in operations.
• Challenges of integrating IoT in manufacturing.
• The role of innovation in competitive advantage.
• Managing technology-driven change in organizations.
• Virtual reality (VR) and augmented reality (AR) in operations.
• The future of robotics in manufacturing.
• Innovation culture: Fostering creativity in operations.
• Technology management in healthcare operations.
• Digital transformation and its impact on operations.

Project Management

• Agile project management in operations.
• Risk management in project execution.
• The role of project management offices (PMOs).
• Project portfolio management: An integrated approach.
• Tools and technologies for efficient project management.
• Stakeholder management in project execution.
• The psychology of project management.
• Cross-cultural considerations in global projects.
• Managing virtual teams in projects.
• Project failure: Analysis and lessons learned.

Human Resources and Operations

• Managing diversity in operations management.
• The role of team dynamics in operational success.
• Talent management in operations.
• Employee motivation and performance in operations.
• Human factors in safety management.
• The importance of organizational culture in operations.
• Training and development in operations management.
• Employee engagement and its impact on operational efficiency.
• Managing remote work in operations.
• Labor relations and negotiations in operations.

Operations management remains an evolving and essential field in both academia and industry. The above topics reflect the breadth and depth of areas one could explore. Each subject offers unique insights and challenges, enabling students to apply theoretical concepts to real-world scenarios. These topics are designed to inspire critical thinking and provide a starting point for those embarking on research in operations management. Whether you are looking for a topic that aligns with your interests or seeking to address current issues in the field, this comprehensive list offers diverse paths to contribute to the body of knowledge in operations management.

Operations Management and the Range of Research Paper Topics

Operations management is a vital aspect of business that deals with the design, administration, and optimization of business processes. It plays a crucial role in ensuring that an organization operates efficiently and effectively. From manufacturing to services, operations management transcends various sectors and industries. In this article, we’ll delve into the multifaceted world of operations management, explore its significance, and elucidate the range of research paper topics it offers.

Introduction to Operations Management

Operations management is all about the planning, oversight, and control of processes that transform inputs such as materials, labor, and technology into outputs like goods and services. It’s a dynamic field that requires a blend of analytical thinking, problem-solving, and practical skills.

Operations managers focus on improving efficiency, reducing costs, maintaining quality, and ensuring that products or services are delivered on time. The scope of operations management is broad, encompassing areas like:

• Production Management : Deals with the creation of goods and services.
• Supply Chain Management : Focuses on the flow of materials from suppliers to customers.
• Quality Control : Ensures products meet specified quality standards.
• Logistics : Concerned with the movement, storage, and flow of goods.
• Project Management : Involves planning and overseeing projects to ensure they are completed on time and within budget.

Significance of Operations Management

Operations management is at the heart of any organization, impacting several critical areas:

• Efficiency : By optimizing processes and eliminating waste, operations management helps in utilizing resources more efficiently.
• Cost Reduction : Through continuous improvement and innovation, costs can be reduced, leading to higher profitability.
• Customer Satisfaction : By ensuring quality and timely delivery, operations management plays a key role in customer satisfaction.
• Competitive Advantage : Organizations that excel in operations management often have a competitive edge in the market.

The Ever-Evolving Nature of Operations Management

The field of operations management continues to evolve, driven by technological advancements, globalization, environmental concerns, and changing consumer preferences. Topics such as sustainability, automation, digital transformation, and global supply chain challenges are becoming increasingly relevant.

Range of Research Paper Topics

Given the diverse and complex nature of operations management, the range of research paper topics is vast and can be categorized into several areas:

• Production Management : From lean manufacturing to the use of artificial intelligence, research can focus on how to make production more efficient and adaptable.
• Supply Chain Management : Topics could include risk management, ethical considerations, green practices, and the influence of e-commerce on traditional supply chains.
• Quality Control and Assurance : Research in this area could explore methodologies like Six Sigma, continuous improvement, and the relationship between training and quality control.
• Logistics Management : With the growing importance of e-commerce and sustainability, research in logistics management is thriving.
• Service Operations Management : This includes the design and management of processes that create and deliver services, with potential research focusing on customer experience, technology, and innovation.
• Strategic Operations Management : Research topics can explore how operations strategy aligns with business goals and contributes to competitive advantage.
• Sustainability and Environmental Considerations : This is an emerging area focusing on how operations management can contribute to a more sustainable future.
• Technology and Innovation Management : From Industry 4.0 to digital transformation, this category looks at how technology is reshaping operations management.
• Project Management : Topics might include agile methodologies, stakeholder management, risk mitigation, and cross-cultural considerations in global projects.
• Human Resources and Operations : This could include topics like managing diversity, team dynamics, employee motivation, and training in operations management.

Operations management is a vibrant and multifaceted field with a wide array of research possibilities. From traditional manufacturing to modern service industries, from small businesses to multinational corporations, operations management is at the core of organizational success.

The broad range of topics reflects the evolving nature of the field and the challenges and opportunities that come with it. For students seeking to contribute to this essential area of business, these topics offer a rich and diverse avenue for exploration and innovation.

By understanding and engaging with these various aspects, scholars, practitioners, and students can appreciate the importance of operations management in today’s global economy and contribute to its future development. Whether through academic research or practical application, operations management remains a critical field that continues to shape the way businesses operate and thrive.

How to Choose Operations Management Research Paper Topics

Choosing the right topic for a research paper in operations management is a critical step that can significantly impact the quality and relevance of your work. It can be both an exciting and daunting task, given the wide array of topics available in this dynamic field. In this section, we’ll provide an introductory paragraph, 10 practical tips, and a concluding paragraph to guide you in selecting the ideal operations management research paper topic.

Operations management is a multifaceted field that encompasses various aspects of business processes, from production to logistics, supply chain to quality control. As such, it offers a wide range of intriguing research paper topics. The right topic not only aligns with your interests and academic goals but also has the potential to contribute to the broader field of operations management. Here are some tips to assist you in making an informed choice.

10 Tips for Choosing Operations Management Research Paper Topics

• Identify Your Interests : Start by listing areas within operations management that intrigue you the most. Passion for the subject can fuel your research and make the process more enjoyable.
• Understand the Scope : Consider the breadth and depth of the topic. A topic that’s too broad may be unmanageable, while a too narrow focus may lack sufficient material for research.
• Check for Relevance : Ensure that the topic aligns with current industry trends and challenges. A relevant topic will have a greater impact and may open opportunities for further study or career advancement.
• Consult Academic Sources : Look through academic journals, textbooks, and other scholarly publications in operations management to discover prevailing research themes and gaps in the literature.
• Consider Practical Implications : If possible, choose a topic that has practical applications in real-world scenarios. This connection between theory and practice can make your research more compelling.
• Assess Available Resources : Evaluate the resources you have at your disposal, including access to data, software, labs, or industry experts. Some topics might require specialized tools or contacts.
• Seek Guidance from Advisors : Consult with professors, mentors, or industry professionals who have expertise in operations management. Their insights can help refine your topic and provide direction.
• Evaluate Your Skill Set : Reflect on your skills and expertise in the subject area. Selecting a topic that complements your strengths will facilitate a smoother research process.
• Consider Ethical Implications : Ensure that the chosen topic adheres to ethical standards, especially if it involves human subjects, sensitive data, or controversial issues.
• Think about Future Opportunities : Your research paper can be a stepping stone for further studies, publications, or career opportunities. Consider how the chosen topic might align with your long-term goals.

Concluding Thoughts

Choosing a research paper topic in operations management is a delicate balance between your interests, the academic and industry relevance, the feasibility of research, and alignment with ethical standards. By adhering to these tips, you can select a topic that not only resonates with your passion and capabilities but also contributes to the field of operations management.

Remember that the right topic is a catalyst that can ignite your creativity and analytical abilities, leading to a meaningful and rewarding research experience. Whether you’re exploring sustainable supply chain practices or innovative quality control techniques, your choice of topic is the foundation upon which your entire research project is built. Make it a strong, informed one, and you’ll set yourself up for success in the vibrant world of operations management.

How to Write an Operations Management Research Paper

Writing a research paper in operations management is a systematic process that requires careful planning, in-depth research, and coherent presentation. This endeavor involves not only an understanding of the operations management concepts but also the ability to analyze, evaluate, and apply them in various real-world contexts. Below, you will find an introductory paragraph, 10 essential tips, and a concluding paragraph to guide you through the process of writing an operations management research paper.

Introduction

Operations management is a complex field that integrates various aspects of production, quality control, logistics, and supply chain management. Writing a research paper on a topic within this discipline demands a clear understanding of both theoretical principles and practical applications. The task may seem overwhelming, but with the right approach and adherence to specific guidelines, you can craft a paper that stands out in quality and relevance.

10 Tips for Writing an Operations Management Research Paper

• Choose the Right Topic : Refer to the previous section for tips on selecting a relevant and engaging topic that aligns with your interests and the broader field of operations management.
• Conduct Thorough Research : Utilize reputable academic sources such as journals, textbooks, and industry reports. Gather sufficient data and insights that relate to your chosen topic.
• Create a Strong Thesis Statement : Your thesis should clearly articulate the main idea or argument of your paper. It serves as the guiding star for your entire research.
• Develop an Outline : Before diving into writing, create a detailed outline that maps out the structure of your paper. It should include an introduction, literature review, methodology, findings, discussion, conclusion, and references.
• Write a Compelling Introduction : Start your paper with an engaging introduction that provides background on the topic, states the problem, and introduces the thesis statement.
• Include a Literature Review : Summarize existing research on the topic, highlighting key theories, models, and empirical findings. This section establishes the context for your study.
• Explain Your Methodology : Describe the research design, methods, and tools you used to collect and analyze data. Be meticulous in explaining how you ensured the reliability and validity of your study.
• Present Findings Clearly : Organize and present your research findings in a logical manner. Use charts, graphs, and tables where necessary to visualize the data.
• Discuss the Implications : In the discussion section, interpret the results, compare them with existing research, and explore the implications for operations management practice and future research.
• Edit and Revise : Spend ample time revising and proofreading your paper. Consider seeking feedback from peers, mentors, or professional editing services to ensure clarity, coherence, and correctness.

Writing a research paper in operations management is a rewarding yet challenging task. It requires a fusion of technical knowledge, analytical thinking, and writing skills. By following the tips outlined above, you’ll be well-equipped to craft a paper that is not only academically rigorous but also relevant to the dynamic and multifaceted world of operations management.

Remember, writing a research paper is a process that demands time, effort, and perseverance. Be patient with yourself and stay committed to excellence at every stage of the journey. The final product – a well-researched, well-written paper – is a testament to your intellectual curiosity, hard work, and contribution to the ever-evolving field of operations management. Whether you’re a seasoned researcher or a student just starting out, these guidelines are designed to empower you to write with confidence and integrity in the domain of operations management.

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The field of operations management is multifaceted, combining elements of logistics, supply chain management, quality control, and production. Creating a research paper that captures all these aspects can be a daunting task. iResearchNet, a leading academic writing service provider, is here to help students craft impeccable operations management research papers. With a team of expert degree-holding writers, state-of-the-art research methods, and top-notch customer support, iResearchNet offers a full suite of services tailored to your needs. Below, you will find an introductory paragraph, details of our 13 standout features, and a concluding paragraph.

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Thirteen years of Operations Management Research (OMR) journal: a bibliometric analysis and future research directions

Mohamed m. dhiaf.

1 Faculty of Business Administration, Emirates College of Technology – ECT, Abu Dhabi, UAE

Osama F. Atayah

2 College of Business, Abu Dhabi University, Abu Dhabi, UAE

Nohade Nasrallah

3 Faculty of Management & Economics, Notre Dame University, Beirut, Lebanon

Guilherme F. Frederico

4 School of Management, Federal University of Paraná – UFPR, Curitiba, Brazil

Associated Data

All data are included in the article.

The journal of Operations Management Research (OMR) is a rigorous journal that started its publication in 2008. It publishes short, focused research studies that advance both the theory and practice of operations management. Considering the relevant OMR’s contribution to the field of Operations Management in the last years, this study provides an overall assessment of the journal performance by conducting a retrospective review. To elaborate on OMR's temporal development in terms of publications, authors, affiliated institutions and countries, citation patterns, and conceptual structure, we extract publications from Scopus database for the period 2008–2020. We rely on bibliometric techniques in addition to bibliographic coupling, keyword analysis, and content analysis. 166 documents were analyzed using RStudio, VOSviewer, and Microsoft Excel. Findings emphasize OMR’s steady productivity growth (3.24%). Narrowly, Olhager J. is the most productive authors while Kalchschmidt M. and Stentoft J. are the most influential authors (H-index of 4). Furthermore, USA contributes to the highest number of publications while UK is the most influential country in terms of citations. Cranfield School of Management, UK stands as the top cited university. The analysis of the thematic evolution concludes to three main clusters: "Manufacturing and Supply chain Performance", "Six Sigma and Lean Management", and "Reshoring, Backshoring and Offshoring". This study recommends to further investigate the implications of the fourth industrial revolution and the sequels of COVID-19.

Introduction

Operations research and management science has amplified in the scientific community since the official establishment of the Operations Research Society of America (ORSA) in 1952, the Operational Research Society (ORS) of the United Kingdom in 1953 and The Institute of Management Sciences (TIMS) in 1953 (Merigó et al.  2019 ). Originally, it sought to embrace the industrial and manufacturing methods and procedures (Buffa  1980 ), and has then expanded to permeate service systems and a myriad of functional organizational areas such as marketing, accounting, purchasing/ logistics, information management, engineering and human resources (Craighead and Meredith  2008 ). In as much, the field evolved with time and merged from relatively high reliance on mathematical techniques to more sophisticated ones. Such diversely and wealthy discipline contributed to OR journals’ surge that attracted many scholars and analysts. Among them, Operations Management Research (OMR) that was launched as a hybrid and transformative journal aiming to publish original, high-quality research that are shorter and more sharply focused than existing Operational Management (OM) ones. The journal started publication in 2008 in Springer New York L.L.C, United States and provided relevant contributions to the field of operations management, being one of the most outlets sought by researchers. Its expansive and wealthy scope engendered many multidisciplinary approaches and robust conceptual integration such as: "Industrial and Manufacturing Engineering", "Management of Technology and Innovation", "Management Sciences and Operations Research" and "Strategy and management". Its current editors are Matteo Kalchschmidt, and Daniel A. Samson.

As OM is a functional field with enormous strides in the last decades, OMR flourished in both publication activities and citations. Though it is a young journal, it succeeded to be included in Thomson Reuters ISI Web of Knowledge in 2012 and managed to position itself well among the operations management journals, promoting research that is relevant to both practitioners and researchers (Olhager and Shafer  2018 ). This reflects its impactful presence and continual prominence as it is targeted by prolific researchers who kept on promoting its excellent intellectual base and disseminating knowledge. According to SCImago Journal Rank (SJR), this journal is ranked 1.313 and has an H-index of 18. The best quartile for this journal is Q1 and its best quartile by subject area is Industrial and Manufacturing Engineering (Q1), Management of Technology and Innovation (Q1), Management Sciences and Operations Research (Q1) and Strategy and management (Q1). OMR has total citations of 3,033. Its overall rank is 2,855 and its impact factor is 5.95.

Factually, the publication quest to target top ranking journals is a major sticking point where common metrics are used to assess overall journal quality and find the suitable niche. Journal Impact Factor (JIF) is one of the most used metrics. It is a measure of the frequency with which the average article in a journal has been cited in a particular year. However, many journals/authors self-cite their own articles. Actuality, journal quality is assessed by three main factors: citation analysis, peer analysis (reviewer selection criteria), and circulation and coverage (international audience and electronic copies). Yet besides the fact that such criteria should be collectively considered, scholars should account for other factors such as: relative thickness and frequency of publication, co-authorship, thematic trends and co-occurrence, and acceptance/rejection rates.

In 2019 editorial, 14 hot topic areas were recommended in operations and supply chain management (O/SCM) that deserve scholars’ attention and practical work (Samson and Kalchschmidt  2019 ). Much more, in 2020 editorial, Samson ( 2020 ) drew on major changes in O/SCM context and the consequential disruptions caused by COVID-19 as the pandemic effect has touched macro and microeconomic levels. New mitigating strategies are proposed to reduce reliance on long international supply chains that lead to reconsider reshoring and other risk mitigation strategies. Thus, the journal transcendental and reversal changes are worth commenting and based to our knowledge, prior studies to assess OMR’s performance are not conducted yet. In parallel, the emergence of scientific databases such as Scopus and Web of Science has facilitated the acquisition of large data to pursue a complete bibliometric analysis in addition to the powerful software such as VOS and Biblioshiny (R studio) that permit to objectively perform quantitative analysis to decipher and map the cumulative scientific knowledge.

To cope with the above gaps, we conduct this study to analyze OMR history, benchmark it and highlight its performance. In this sense other studies were previously conducted in order to understand the journal development and provide readers with rich and valuable information (Rialp et al.  2019 ; Wang et al.  2019 ; Kumar et al.  2020a , b ). Also, some specific areas have been thoroughly explored such as: Entrepreneurship (Moya-Clemente et al.  2021 ; Servantie et al.  2016 ); Knowledge Management (Zha et al.  2020 ; Noor et al.  2020 ); Operations Management (Pilkington and Meredith  2018 ; Laengle et al.  2017 ; Liao et al.  2019 ; Caputo et al.  2019 ; Schulz and Nicolai  2015 ; Oliveira et al.  2018 ).

Therefore, our analysis aims to highlight OMR’s high reputation and its extensive contribution where we draw on its influential impact, relevant insights, ongoing changes, future direction, and innovative methodologies. It is an in-depth analysis of the annual citation structure and trend along with an inter-temporal analysis its focus and attempts to anticipate potential developments and new research paths. It provides insights into the journal's past, present and future trends by offering a retrospective analysis of the journal's content (Laengle et al.  2017 ). It addresses the following research questions: (1) What is OMR publications and citations trend over the last 13 years? (2) Which are OMR top-cited papers? (3) Who are the most productive and influential authors, institutions, and countries? (4) What are OMR thematic patterns? (5) Which are the persistent, hot, and cold topics?

We aim to achieve the following objectives: (1) shed light on OMR major theoretical and empirical contributions, (2) identify top and most influential scholars in terms of journals, countries and affiliations, (3) describe co-authorship relations and collaboration status in order to highlight their preponderance to curb citation trends, (4) benchmark OMR to build its competitive capabilities in terms of technology, quality, delivery, and productivity (Dertouzos et al.  1989 ; Hines et al.  1998 ), (5) provide hot topics and develop future directions in OR by revealing the current research trends and frontiers of various disciplines through keywords and co-citation analyses, (6) map and visualize results to have more intuitive presentation, and (7) pinpoint OMR critical issues and motivational strategies to further improve its ranking and visibility. Thus, this paper provides a comprehensive and broad review that encourages the scientific community and researchers in OR to engage in further discussions.

As the methodological support, we have used VOSviewer, R studio, and Microsoft Excel to derive insightful metrics to assess OMR influential impact, benchmark it relatively to peer journal in the same field and analyze its performance and temporal development.

The organization of this paper is as follows. Section  2 introduces the study's methodology, while Sect.  3 reports the results, which are divided into three major parts (publication analysis, citation analysis and network analysis). Section 4  provides the content analysis of each cluster. Section 5  provides a future agenda and Sect. 6  concludes to a summary of findings.

Methodology

The bibliometric analysis is gaining popularity as it is commonly used to analyze and evaluate the performance of scientific community in specific knowledge area (Barber and Mancall  1978 ; Bookstei  1979 ; Ferrante  1978 ). It becomes a fundamental tool to weigh the research impact and depict patterns of scientific contribution. To track the evolution of OMR and to identify yearly trends, we apply quantitative analysis to facilitate our interpretation. The bibliometric analysis leverages the authors' ability to manage, analyze, and extract insights from massive data, including intellectual structure, influential actors and contributors, as well as authors (Zagos and Brad  2012 ), affiliations (Boardman  2008 ), countries (Rey-Martí et al.  2016 ) sources, (Loh and Venkatraman  1992 ), and references (Tang et al.  2020 ). In addition, bibliometric approach is helpful to accurately identify the quantitative and qualitative indicators to develop reliable and relevant knowledge structure (Fagerberg et al., 2012 ).

Specifically, in the domain of journals' performance analysis, the bibliometric analysis has been commonly used in several studies to analyze different journals in various discipline; for instance Finance (Baker et al.  2021 ; Linnenluecke et al.  2020 ; Paule-Vianez et al.  2020 ); Accounting (Martínez-Blasco et al.  2016 ; Muehlmann et al.  2015 ; Yamaguchi et al.  2015 ); Management (Mishra et al.  2018 ), and Economic (Goyal et al.  2021 ).

To analyze OMR's performance, we consulted the Scopus database and extracted 166 published documents over 13 years (2008 to 2020). They consist of 149 articles, 16 editorials, and 1 erratum (Table ​ (Table1). 1 ). Despite that this data was manually refined and cleaned to avoid the risk of redundant author and affiliations names, it is still possible to have some duplicated data due to various types of writings and spelling. The study employs several bibliometric indicators to provide a comprehensive view and insightful analysis. First, the VOSviewer was employed to carry the mapping analysis, bibliographic coupling, keyword co-occurrences analysis, and co-authorship. VOSviewer is an efficient software that constructs and visualizes bibliometric networks for scientific actors, such as authors, journals, affiliation, citations, countries and other aspects (Chygryn et al.  2020 ; Evans  2019 ; Ferasso et al.  2020 ). Second, R studio has been applied to analyze several aspects, including the conceptual structure, productivity, most influential scientific actors, Lotka's law, and topics trend.

OMR overview between 2008 and 2020

Along with descriptive analysis, the study identifies the thematic structure of the journal using bibliometric coupling analysis. Kessler ( 1963 ) proposes that documents citing an identical third document tend to form a bibliographic couple and that bibliographic couples discuss similar intellectual themes (Martyn  1964 ).

The count of publications is the measure of productivity and the count of citations is the measure of influence (Kumar et al.  2020a , b ). Specifically, we have used "biblioshiny" package, which consists of tools designed for quantitative studies in the field of both bibliometric and scientometrics. This package is helpful in converting the dataset into R format, carrying accurate bibliometric analysis, and developing matrices for various aspects, including co-citation, scientific actors’ collaboration, words analysis, and multiple correspondence analyses. Third and finally, Microsoft Excel was used to develop editable figures and tables, and to verify several tests such as citations, journal productivity, and affiliation production.

Bibliometric data and analysis provide information on the scientific orientation and dynamism of a journal, and on its impact on both the national and the international community (Okubo  1997 ). It uses numerous parameters such as: (1) performance analysis and (2) sciences mapping. The performance analysis serves to depict journal constituents’ performance (authors, affiliations, and countries) while the sciences mapping addresses constituents’ relationship. The below part provides a thorough analysis of the journal productivity, performance, benchmarking, and citation analyses.

Performance analysis and benchmarking

Benchmarking is an important strategic tool that reflects a qualitative orientation toward journals performance. It identifies future research to achieve a more systematic and quantitative analysis conducive to sharing a base of knowledge. Over the past 13 years, OMR productivity noticeably grew and contributed to 166 articles in the operations management discipline with an emphasis on contemporary issues that enhanced the industry efficiencies. It has an H-index of 28 which indicates that 28 papers have each received 28 citations or more. This measure is useful because it considers both the quality and the quantity of a set of publications. OMR earned 3,033 citations over 13 years and averaged 18.27 citations per document. The journal realized 3.24% annual growth rate over its lifespan which implicates a steady growth in the number of publications. Its collaboration index is 2.54 which suggests that each author cooperates with more than 2 authors to contribute a research work in the journal. It showcases the need for technical and scientific cooperation between scholars from different countries, universities, and backgrounds.

Distribution of publications over years

In this section, we provide a detailed overview of OMR temporal activity to measure the volume and impact of OMR research over prolonged periods of time as a means of identifying trends. Figure ​ Figure1 1 visualizes the yearly publication trend. At its early beginning (2008–2012), the journal witnessed a volatile productivity depicted in a peak in 2010 (18 publications) and a trough in 2009 (8 publications). Over the subsequent 5 years (2013–2017), the publication trend was steady with an average of 11 publications per year. The last three years were remarkable as OMR productivity rate jumped by 71.43% (2018–2019) and then by 83.33% (2019–2020). The last year (2020) was exceptional with a striking increase in OMR productivity that led to the publication of the third issue. Moreover, the average publication per issue ranges between 5 and 8. Two exceptions are worth commenting. In 2010, the average publication per issue was the highest (9) while in 2018 this average was at its lowest level (3.5). 52% of total publication took place in the first issue publication while the remaining 48% are published in the second one.

OMR's yearly publication by issue between 2008 and 2020

Most relevant scientific actors (authors, affiliations, & countries)

The below section gathers a variety of dimensional indicators that analyze the number of publications, citations, and authors production over time. Table ​ Table2 2 shows the top 20 authors in terms of different metrics. The number of publications measures the academic contributions of OMR, the standard forms of influence and impact such as the h-index, g-index, and m-index (Egghe and Rousseau  2006 ; Hirsch  2005 ). Broadly, the h-index (h) indicates h number of publications cited at least h times, the g-index (g) accounts for the g number of highly cited publications receiving at least g 2 citations. While, h-index is independent of the date of an academic's career, the m-index aims at weighing the period of academic endeavor so to reduce the bias in favor of scientists with longer careers. Thus, if  n  = number of years since the first published paper of the scientist, the m-quotient = h-index/ n . We also account for citation per publication as the number of publications is a sign of condense productivity while the number of citations is an indicator of impactful influence. Olhager J. ranks first with 7 documents while Shafer S. ranks second with 5 documents. 5 publications for Olhager J. and Shafer S. are 5 editorials which explicate the low number of citations. The paper “Manufacturing backshoring: a systematic literature review” for Stentoft J., Olhager J., Heikkilä J., Thoms L. published in 2016 earned 65 citations at the time we extracted the data. Although Holmstrm J, Kalchschmidt M. and Stentoft J. contributed to 4 papers each, they earned the highest H-index of 4. Distinctly, the top 20 authors approximately contributed to 36% of total publications.

Top 20 Authors in terms of publications and citations

This table ranks the 20 most prolific OMR authors in terms of citations between 2008 and 2020. where H-Index = number of papers (N) with N citations or more, G-Index = number of articles that have accumulated g 2 number of citations, M-index = H-index per active year for the author and PY_start = start of the publication year

From another perspective, Neely A. stands as the most influential scholar among the top 20 authors with 719 citations and 359.50 average citation per document. Vinelli A., Stentoft J. and Holmstrm J. ranked in the next positions with total citations of 122,121, and 116, respectively.

Moreover, Fig. ​ Fig.2 elaborates 2  elaborates on the top 20 author's production over time where we closely notice that Olhager J. published its first paper “Internal and external suppliers in manufacturing networks-An empirical analysis” in 2008, while the bigger node implicates higher contribution that stands for 2012 editorials. Shafer S. extensively contributed to OMR in 2012 and 2018 while Holmstrm J. contributed to OMR since 2012. Notably, the year 2012 retraces a wealth of contribution from the top twenty authors. As for Stentoft J. who stands one of the most influential scholars, he started publication in 2014 and ended in 2016 with highly cited articles (bigger and darker node) which reflect his highest H-index of 4.

The top 20 OMR authors' production over time

Furthermore, Lotka's law is used to determine the frequency of publication by authors in any given field (Talukdar  2015 ) (Table ​ (Table3). 3 ). It predicts that minority of authors usually publish the majority of articles and that a high frequency rate is an indicator of authors’ satisfaction to repeat their experience and publish with the same journal. The relative frequency distribution of author productivity is hyperbolic or follows an inverse square function, such that the minority of the authors are publishing the majority of the articles. More specifically, it states that the number of authors making “n” contribution is about 1/n 2 on those making one “C”. The deviation of the observed function from the predicted inverse square function acts as a metric for the inequality in productivity of the field.

The frequency of publication by authors

This table shows the frequency of publications by OMR authors between 2002 and 2020

88.46% of OMR active authors published 1 single paper. Based on Lotka’s law, the relative proportion of the authors that have published 2 papers should have been 22.12% while 8.90% of OMR scholars have published 2 papers. Though there is negative deviation, Pao ( 1985 ) proposed a generalization to find both values (C and n), known as distribution of generalized inverse power or Lotka’s inverse-power law.

On another scale, Table ​ Table4 4 highlights the top 10 universities in terms of publications. Wake Forest University, USA ranked at the top with 12 articles followed by Lund University, Sweden and The Pennsylvania State University, USA that equally contributed to 7 publications each. Out of the top 10 performing universities, 60% are American and 20% are Swedish. Interestingly, Cranfield School of Management, UK is ranked as top cited university with 727 (23.97% of total citations) followed by the University of Cambridge, UK that earned 724 citations (23.87% of total citations). The top 10 universities have total 2,308 citations (76.10% of total citations). Out of the top 10 cited universities, 20% of the universities (UK) secured 62.86% citation while the remaining universities hold 37.13% citations. It is worth mentioning that USA stands as the leading contributor in terms of publications whereas UK is the leading in terms of citations. In their bibliometric analysis of the “International Journal of Logistics Research and Applications”, Wang et al. ( 2019 ) found that Cranfield School of Management is the most productive and influential university. This finding is grounded by the fact that the latter university is playing a pivotal role in the publication field in the areas of operation management and supply chain.

Top 10 universities

This table ranks the top 10 institutions affiliated in terms of publications between 2008 and 2020

On a country level, USA contributed to the highest number of publications (68) followed by Italy (20), Sweden (15) and United Kingdom (15) as showed in Table ​ Table5. 5 . The top 4 ranking countries contributed to 71.08 % of total OMR publications. From the citation perspective as UK topped the list with 1,003 citations that account for 33.06 % of total citations. USA secured the second place (859) with 24% of total citations while Italy earned the third position (294). UK and USA contributed to more than 61% of total citations.

Top 15 countries in terms of total publications

This table ranks the top 15 countries affiliated with OMR authors, in terms of publications between 2008 and 2020

In their bibliometric analysis of “The International Journal of Production Research”, Wang and Sun ( 2019 ) emphasized how the journal gained prominence over time. More than 99 countries/regions contributed to shift the journal ’dependency from its top ten authors to a wider array of authors. This fact has increased the journal ability to widen its scope of research and to expand the number of its authors.

Thus, OMR should initiate new policies to attract scholars from Asian countries to amplify its productivity and magnify its diversity. More specifically the journal should coordinate with international universities to conduct conferences and roundtables with market analysts and policy makers. The editorial board can play a significant role in setting future guidelines and strategies for OMR path such as increasing the thickness of publication, widening the scope along with suggesting formal and informal market campaign. Samson ( 2020 ) suggested hot topics to be investigated in the future such as: the disruptive effect of COVID-19, the impact of ageing population and its impact on the labor market, supply chain and OM habits, the inequality among OECD and developing countries, modern slavery, the low interest rates, industry 4.0 and digitalization effect, risk and resilience, CSR/ESG and many interesting topics. This drew attention on the effort put by editors to attract top scholars and the need to assess and study the new and disruptive changes in O/SCM trends and in other related functional areas.

Citation analysis

Figure  3 shows the citation growth of the journal measured by the yearly citation and the accumulated citation indicators. The yearly citation swings from a high of 943 citations in 2008 to a low level over the last 3-year periods where it plummeted to 30, 55, and 45 in 2018, 2019 and 2020, respectively. Notably, OMR publications were specifically influential in 2016 with 422 earned. Though the accumulated yearly citations displayed an uptrend steady growth, the last three years record the lowest number of citations as such trends have been common across journals due to the time specific nature of citations (Baker et al.  2020 ).

OMR citation growth between 2008 and 2020

Table ​ Table6 6 provides an in-depth analysis of the yearly citation trend. Out the 166 total publications, 140 documents or 84.3% received at least one citation. Only 26 articles were not cited out of which there are 5 editorials and 22 articles published in 2020–2021. In the past few years, the total cites per publication (TC/TP) and total cites per cited publication (TC/TCP) were experiencing a downward trend. For example, in 2008, TC/TP was 62.9 which sharply dropped during the last three years and reached the lowest level (2 citations) in 2020. Similarly, TC/TCP depicted 67.4 in 2008 which fell to 5 in the last two years 2019–2020. Indeed, the citations of the latest publications need time to accrue. Moreover, it is discernable that 86 articles or 61.43% were cited only from 1 to 9 times, 47 articles 33.57% received 10–49 citations, 6 articles received 50–99 citations while only one article received more than 100 citations. Figure  4 provides a clear reflection on the trend of published articles versus cited ones. It compares the number of total publications (TP) with the number of cited publications (TCP) where at the beginning (2008–2011), both indicators co-walked at approximately the same pace which indicates the equilibrium between OMR productivity and influential impact. The gap widened in 2020 where the number of cited articles (9) shrunk to 40.9% of total published ones (22). This latter finding is very normal since citations of recent publications need time to accrue.

Annual number of OMR citations between 2008 and 2020

This table shows the annual number of OMR publications, cited publication and citations between 2008 and 2020, where TP = total publications, TCP = total cited publications, TC = total citations, TC/TP = cites per publication and TC/TCP = cites per cited publication. The remaining columns show the number of articles with at least 100, 50, 10 and 1 citation(s), respectively

The numbers in bold represent the total by column. 84.30% is the percentage TCP out of TP. 0.71%, 4.29%, 33.57% and 61.43% are percentages of publications with citations out of TCP

Gap between total publications and total cited publications between 2008 and 2020

To investigate OMR influential publications, we extract the top 15 papers in terms of yearly citations presented in Table ​ Table7. 7 . Neely’s publication in 2008 titled “Exploring the financial consequences of the servitization of manufacturing” earned the highest citations per year. The second document (Holmström et al.  2016 ) titled “The direct digital manufacturing (r)evolution: definition of a research agenda” achieved the 2nd rank while the yearly citation of (Pont et al.  2009 ) publication “Interrelationships among lean bundles and their effects on operational performance” earned 87 citations. In fact, there is some specific references that are most frequently cited by OMR articles. The most cited article is “Building Theories from Case Study Research Published by: Academy of Management Stable” (6 citations) published by the “The Academy of Management Review” (Eisenhardt  1989 ). Likewise, “Offshoring, reshoring and the manufacturing location decision” published by the “Journal of Supply Chain Management” (Ellram  2013 ) was also cited 6 times in OMR.

The 15 most cited OMR articles by year between 2008 and 2020

This table lists the 15 most cited OMR articles between 2008 and 2020 with the published year and the number of citations received

Table  8 show most frequently cited journals by OMR articles. The most cited journal is the Journal of Operations Management (Q1 and ABDC rating of A*) from which 560 articles were cited by OMR documents. Other impactful journals are the International Journal of Production Economics and The International Journal of Operations & Production Management from which 280 and 224 articles were respectively cited by OMR authors. This fact pinpoints the wealth of resources, and the high-quality journals OMR authors are relying on.

Top cited journals

This table lists the 15 most cited journals by OMR’s articles between 2008 and 2020, with their impacts. Cite score measures the average citation per published document; SNIP (Source Normalized Impact Per Paper) measures citations weighted by subject field; SJR (SCImago Journal Rank) measures citations weighted by prestige; Quartile is according to SCImago; ABDC provides the ranking based on Australian Business Deans Council

ABDC ranking has four categories: A*(highest), A, B, C (lowest)

Network analysis

The network analysis is extracted from VOSviewer software. It aims at developing the connectivity between keywords based on their occurrences. A total of 576 author's keywords appears in OMR 166 documents with an average of 3.47 per document. Figure  5  shows that keywords are classified into three clusters. The red cluster represents the impact of operation strategy and the purchasing on the sustainability and firm performance, and it is investigated through empirical studies and surveys. The second cluster in the blue one which discusses the supply chain management in the reshoring, offshoring and backshoring and is mainly investigated in case studies and surveys. Finally, the green cluster tackles the supply chain and performance with regards to the six sigma, lean, and city logistics. It is mainly investigated in the healthcare and hospitals. To capture deeper insights, we employ VOSviewer to visualize the time span for each topic, the lighter nodes indicate more recent topics. Figure  6  clearly demonstrates that the oldest topics are emphasized in the areas of six sigma, purchasing, quality, and performance. In 2013–2016, topics such as “the manufacturing supply chain management and operational performance” gained popularity. While the most recent topics focus on the reshoring, offshoring, operations strategies, city logistics, and sustainability. OMR structural and temporal themes serves to highlight persistent research themes, cold and hot ones. “Supply chain”, “Operations management”, “Operational performance”, “Efficiency” and “Strategy” are persistent themes. The journal calls for the emphasis of reshoring and relocating as many multi- and transnational companies are rethinking about the supply chain system in disruptive times. Many analyses are worth studying such as top management resilience and agility in times of constraints and major events.

Author keywords network of OMR articles

Author keyword timeline of OMR article

Likewise, the countries co-authorship was developed. Figure  7  presents that USA is the journal lead contributor within the red network that includes Sweden, Finland, Canada, and India. While UK is at the center of the green network that includes China, Pakistan, Oman, and Singapore. Lastly, Italy is at the center of the blue network, which highlights the influential collaboration with other countries such as Australia, Spain, Denmark, and Germany. The country collaboration network conveys a clear message on the necessity to account for other countries. Figure  8  presents countries’ network by seniority. The oldest network stands as Hong Kong, Switzerland, Taiwan, and Japan while the recent collaborative network includes India, Pakistan, Ghana, and United Arab Emirates. Nowadays, there is a surge in research productivity coming from these latter countries and OMR should strategize the next moves to capture wider areas that might positively curb its publication and lead to improve its benchmarking on the ABDC list.

Network of countries affiliated with OMR

Network of countries affiliated to OMR per year

Content analysis

This section offers a general overview for the nature of topics discussed in OMR over its life. It includes keywords analysis, topics trend, conceptual structure, and cluster discussion.

Keywords analysis/topics trend

R studio is used to visualize the words cloud, based on the authors’ keywords in OMR published documents. The most frequent keywords (KW) are supply chain, reshoring, manufacturing, and empirical studies (Fig. ​ (Fig.9). 9 ). The yearly keywords trends retrace the themes path over the period 2008–2020. While the hottest years for some specific themes are very apparent, the years of loss for some other topics are also depicted. For instance, the research in “Reshoring” theme soared between 2015–2017 which matches the claim of Samson in his 2020 editorial note to revisit the reshoring topic and address the ensuing impact of COVID-19 disruption.

OMR Keywords cloud

Conceptual structure and clusters overview

We advanced our analysis and use R studio to reveal the main OMR clusters through conceptual structure. Figure  10  segregates the topics into three main groups: “Manufacturing and supply chain performance” (pink), “Six sigma and lean management” (blue) and “Reshoring, backshoring and offshoring” (green).

Conceptual Structure Map of OMR major themes

Cluster 1: manufacturing and supply chain performance

The first cluster is named “Manufacturing and Supply Chain Performance”. It consists of 101 OMR publications accredited with 2,108 citations. It ranks first in terms of publications and citations (Fig. ​ (Fig.11). 11 ). As shown in the figure above, the cluster is a conglomerate of three sub-clusters that present views on performance, manufacturing, supply chain and operations management. Therefore, the topics represented in the cluster tend to focus on central models such as: assessing performance, managing supply chain, and manufacturing and operations management practices. Neely ( 2008 ) article titled "Exploring the financial consequences of the servitization of manufacturing" is the most cited (719) with an average cites per year (359.50). The author seeks to fill the gap in the literature by presenting empirical evidence on the range and extent of servitization based on database of 10,028 firms incorporated in 25 different countries. Holmström et al. ( 2016 ) article ranks second (92 citations). Their work offers a wealth of opportunities for product and process innovation and is often touted to 'revolutionize' today's manufacturing operations and its associated supply chains structures. As such, we conclude that direct digital manufacturing will increasingly challenge operations management researchers to question established practices such as scheduling, batch sizing and inventory management in low-volume, high-variety contexts. Furthermore, an increasing adoption of direct digital manufacturing will drive structural shifts in the supply chain that are not yet well understood. We summarize these challenges by defining the research agenda at factory, supply chain, and operations strategy levels. Rolland et al. ( 2010 ) article occupies the third slot with 63 citations in the cluster. The study proposes a decision-support system for disaster response and recovery using hybrid meta-heuristics. Decision-support systems used in disaster management must cope with the complexity and uncertainty involved with the scheduling and assignment of differentially skilled personnel and assets to specific tasks. Other important works in the cluster include Choi & Hwang ( 2015 ) article, cited 56 times, and Kuhn and Sternbeck ( 2013 ) article, cited 55 times.

Cluster 1: Keyword cloud and yearly growth

Cluster 2: six sigma and lean management

The second cluster is under the “Six Sigma and Lean Management”. It consists of 42 OMR articles published between 2008 and 2020 (cited 617 times) (Fig. ​ (Fig.12). 12 ). It ranks second in the number of publications and citations. Major covered topics are process improvement, operations strategy, six sigma hospital, lean management in healthcare, supply chain management, and service quality. Chakravorty ( 2009 ) article is the most influential in the cluster (87 citations). His paper is titled "Six Sigma failures: An escalation model" in which he describes a Six Sigma failure in an electrical components company. The research contributes to both practice and theory as it provides a new direction to academic research and has the potential to impact the theory of Six Sigma. It practically uncovers important factors for the successful implementation of the Six Sigma and it theoretically reflects on its definition by pinpointing its commonalities and divergences. Langabeer et al. ( 2009 ) article is the second most influential work in the cluster (73 citations). This article is a cross-sectional analysis that relies on mixed research methods (survey questionnaire and semi-structured interviews). It aims to investigate the implementation of two quality-improvement methods (Lean and Six Sigma) in the context of hospitals. The research concludes to important findings about the impact of such methods on goal and value attainments in the healthcare industry. Robinson and Hsieh ( 2016 ) article is the third highly cited work in the cluster (42 citations). The study contributes to the emerging literature on reshoring by taking a value-driven enquiry into the renewal of supply chain strategy. It enhances the understanding of reshoring as a changing business model. An iconic British high-end clothing brand, Burberry, is the chosen case study to explore its recent move towards reshoring while accounting for different metrics such as: change in leadership, business model and evolving supply chain strategy from 1997 to early 2016. These findings suggest that the renewal of supply chain strategy through reshoring and increasing control can enhance the firm value and competitiveness. The other influential works subsumed in the cluster include Lifvergren et al. ( 2010 ) article and Zeng et al. ( 2013 ) which are cited 38 times each, respectively. The first study presents key applications not earlier described in prior Six Sigma healthcare fields. However, the second paper proposes a conceptual framework to study the relationships among three dimensions of supply chain quality management (SCQM)—in-house quality management practices (internal QM), quality interaction with suppliers on the upstream side of supply chain (upstream QM), and quality interaction with customers on the downstream side of supply chain (downstream QM)—and their impact on two metrics of quality performance (conformance quality and customer satisfaction).

Cluster 2: Keyword cloud and yearly growth

Cluster 3: reshoring, backshoring and offshoring

The last cluster is called “Reshoring, Backshoring, and Offshoring”. This cluster consists of 23 OMR articles cited 308 times, thus ranking third in terms of both publications and citations (Fig. ​ (Fig.13). 13 ). Key topics deliberated in the cluster include reshoring, backshoring, location decisions, and captive offshoring. Stentoft et al. ( 2016 ) article is the most cited work (65 times) where they conduct a systematic review of all prior research related to backshoring of manufacturing and conclude to provide a research agenda for further research. Bals et al. ( 2016 ) essay is the next most influential work (cited 64 times). It aims to clarify the decision-making processes related to two distinct phenomena of reshoring and insourcing and present a conceptual framework of all theoretically possible reshoring and insourcing decisions. Ashby ( 2016 ) article, cited 42 times, is the third most influential work in the cluster. He attempts to explore the effect of sustainability on reshoring strategy by studying the case of a UK-based clothing SME. Other important works in the cluster include Joubioux and Vanpoucke ( 2016 ) article and Zhai et al. ( 2016 ) article, cited 31 times each, respectively.

Cluster 3: Keyword cloud and yearly growth

Future agenda

This section addresses themes publication gap. Emergent topics such as the implication and consequences of the fourth industrial revolution and the influences of COVID 19 on the business practices and operation style are not yet extensively investigated in OMR. To address this issue, we have separately carried two queries on the Scopus database (Industry 4.0 and COVID 19).

Considering the introduction of industry 4.0 in 2013 by the German government, this subject has received significant attention from scholars, practitioners, policymakers, and governments. Industry 4.0 advent has fostered several emerging technologies such as: Machine Learning (ML), Data Sciences, Cloud Computing, Robotic Systems, Artificial Intelligence (AI), and Internet of Things (IoT) (Dalenogare et al.  2018 ). These novel technologies prompt dramatic disruptions in the manufacturing and business practices: management style, business environment, marketing, labor market, competition environment, and customer behavior (Maresova et al.  2018 ; Moll and Yigitbasioglu  2019 ). Accordingly, several studies are carried to explore and investigate the potential opportunities and challenges in different knowledge domains in the fourth industrial revolution era such as: Economy (Goryachikh et al.  2020 ); Sustainability and Accounting (Meseguer-Sánchez et al.  2021 ); Industrial performance (Dalenogare et al.  2018 ); Business practices (Maresova et al.  2018 ) and Industrial practices (Zhang and Chen  2020 ).

Nevertheless, OMR traces shy attempts in this knowledge area. We carry a query 1 on the industrial revolution terminologies from the Scopus database and limit our search to the OMR documents. The query concludes to 10 documents as shown in Table ​ Table9 9 .

OMR articles related to Industry 4.0

We also carried a query to depict the contribution in the COVID 19 knowledge area. 2 The query revealed eight papers distributed equally over 2020 and 2021. Novel coronavirus COVID-19 carried serious consequences to several aspects including human being and industries. The Oxford business group examined the consequences of COVID-19 on the supply chain industry. The outcomes documented that the sector was severely affected due to a drop in population mobility and quarantine policy which led to a sharp change in consumers behavior. Also, countries lockdown and airports and borders’ closure placed a significant strain on the global supply chains (Elnahass et al.  2021 ; Shen et al.  2020 ). Interestingly, Yadav et al. ( 2020 ) examined the rule of Internet of Things (IoT) in overriding disasters such as COVID 19 (Table ​ (Table10). 10 ). They found that (IoT) enhances the efficiency of information sharing by supporting top management and leveraging their ability to develop accurate and timely decisions.

OMR articles related to COVID 19

We thus recommend OMR to encourage more publications relevant to the fourth industrial revolution and to the sequels of COVID-19.

This study provides an in-depth analysis of OMR’s publications, annual citation structure and trend, and its intellectual evolution over the period 2008–2020. We have conducted a bibliometric analysis where we drew on OMR influential impact, ongoing changes, future direction, and innovative methodologies. We were motivated by the fact that there is lack of discussion on hot topics and future directions based on keywords analysis.

We have used VOSviewer, R studio, and Microsoft Excel to derive insightful metrics to benchmark OMR relatively to peer journal in the same field and analyze its performance and temporal development. VOSviewer was used to carry out the mapping analysis, bibliographic coupling, keyword co-occurrences analysis, and co-authorship while R studio was applied to analyze the conceptual structure, productivity, most influential scientific actors, Lotka's law, and topics trend.

Over the years, OMR published 166 articles Out of which, 140 documents or 84.3% received at least one citation. OMR has an H-index of 28 and earned 3,033 citations over 13 years with 18.27 as average citations per document. The journal achieved a steady productivity growth (3.24%) over its lifespan.

Olhager J. was the most productive authors with 7 documents while Kalchschmidt M. and Stentoft J. were the most influential authors with an H-index of 4 and total citations of 116 and 121, respectively. Vinelli A. earned the highest citation per publication (40.67).

Cranfield School of Management, UK was ranked as top cited university with 727 (23.97% of total citations). The top 10 universities had a total 2,308 citations (76.10% of total citations). Out of the top 10 cited universities, 20% of the universities (UK) secured 62.86% citation while the remaining universities hold 37.13% citations.

On a country level, USA contributed to the highest number of publications (68) followed by Italy (20), Sweden (15) and United Kingdom (15). The top 4 ranking countries contributed to 71.08% of total OMR publications. From the citation perspective, UK topped the list with 1,003 citations that account for 33.06% of total citations. USA secured the second place (859) with 24% of total citations while Italy earned the third position (294). UK and USA contributed to more than 61% of total citations.

Bibliographic coupling of articles reveals three major thematic clusters: Manufacturing and supply chain performance, reshoring, offshoring and backshoring and the six sigma and lean management. OMR achieved excellence and remarkably positioned itself in the academic and scientific world. It constantly attracted prolific researchers and kept on disseminating knowledge and enlightening researcher to explore the vast field of contemporary management. In the end, we again relied on bibliometric techniques and prepared two queries to investigate two hot topics that are: Industry 4.0 and COVID-19. In addition, the inclusion of risk metric, the practicality to include preset operational buffers and the implication of top- management resilience and agility in pandemic and extreme events would constitute a main focal area for future research.

To better benchmark OMR, we concluded to the below remarks where OMR is invited to:

• Revisit its scope as a means to more include related functional areas.
• Continue to adhere to rigorous scholarly peer reviewing process.
• Increase its publication thickness while maintaining its inherent quality of research.
• Intensify its formal and informal marketing campaign.
• Encourage scholars and countries collaborations.
• Cooperate with government policymakers, market analysts, and researchers to foster the practical emphasis of existing management theories that should be amalgamated to account for the recent disruptive events.
• Foster forum discussions on the journal platform.
• Strategize the next moves to capture wider areas that might positively curb its publication and lead to improve its benchmarking on the ABDC list.

Authors' contributions

All authors have equally contributed to this article.

There was no funding for this research.

Availability of data and material

Declarations.

We state that this article is not under consideration at any other journal and if it gets accepted, we fully consent in publish in in Operations Management Research (OMR)—Springer.

There is no any kind of conflict and competing interests.

1 Query: ((SOURCE-ID (15,700,154,705)) AND TITLE-ABS-KEY("emerging information technolog*" OR "emerging technolog*" OR "Forth industr*" OR "industr* 4.0" OR "intelligenc*" OR "intelligent" OR "industry 4.0" OR "information system*" OR "machine learnin*" OR "deep learning*" OR "deep mining*" OR "fuzzy" OR "Fuzzy logic" OR "Big*data" OR "bigdata" OR "data*mining" OR "block*chain" OR "Blockchain" OR "Distributed Ledger Technology" OR "collaborative databases" OR "Natural Language Processing" OR "cognitive technologies" OR "augmented reality" OR "automat*" OR "Smart contracts" OR "busines* intelligen*" OR "cloud" OR "cloud*computing" OR "cognitive*" OR "Disruptive technology" OR "decision*support*" OR "digita*" OR "disruptive*technolog*" OR "electronic* accounting*" OR "electronic data interchang*" OR "EDI" OR "expert system*" OR "grid comput*" OR "image process*" OR "image recognit*" OR "industrial revolution*" OR "integrated application*" OR "integrated system*" OR "internet of things" OR "IOT" OR "neural network*" OR "neuro" OR "quantum comput*" OR "robotic*" OR "robots" OR "smart contract*" OR "text mining*")).

2 Query: ((SOURCE-ID (15,700,154,705)) AND TITLE-ABS-KEY ( "epidemic" OR "covid-19" OR "coronavirus" OR "corona-virus" OR "corona virus" OR "pandemic" OR "covid19")).

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Guilherme F. Frederico, Email: [email protected] .

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This paper is in the following e-collection/theme issue:

Published on 14.8.2024 in Vol 26 (2024)

This is a member publication of University of Toronto

Leadership for AI Transformation in Health Care Organization: Scoping Review

Authors of this article:

• Abi Sriharan 1, 2 , MSc, DPhil   ; 
• Nigar Sekercioglu 2 , PhD   ; 
• Cheryl Mitchell 3 , PhD   ; 
• Senthujan Senkaiahliyan 2 , MHSc   ; 
• Attila Hertelendy 4 , PhD   ; 
• Tracy Porter 5 , PhD   ; 
• Jane Banaszak-Holl 6 , PhD  

1 Krembil Centre for Health Management and Leadership, Schulich School of Business, York University, Toronto, ON, Canada

2 Institute for Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada

3 Gustavson School of Business, University of Victoria, Victoria, ON, Canada

4 College of Business, Florida International University, Florida, FL, United States

5 Department of Management, Cleveland State University, Cleveland, OH, United States

6 Department of Health Services Administration, School of Health Professions, University of Alabama Birmingham, Birmingham, OH, United States

Corresponding Author:

Abi Sriharan, MSc, DPhil

Krembil Centre for Health Management and Leadership

Schulich School of Business

York University

MB Room G315

4700 Keele St

Toronto, ON, M3J 1P3

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Background: The leaders of health care organizations are grappling with rising expenses and surging demands for health services. In response, they are increasingly embracing artificial intelligence (AI) technologies to improve patient care delivery, alleviate operational burdens, and efficiently improve health care safety and quality.

Objective: In this paper, we map the current literature and synthesize insights on the role of leadership in driving AI transformation within health care organizations.

Methods: We conducted a comprehensive search across several databases, including MEDLINE (via Ovid), PsycINFO (via Ovid), CINAHL (via EBSCO), Business Source Premier (via EBSCO), and Canadian Business & Current Affairs (via ProQuest), spanning articles published from 2015 to June 2023 discussing AI transformation within the health care sector. Specifically, we focused on empirical studies with a particular emphasis on leadership. We used an inductive, thematic analysis approach to qualitatively map the evidence. The findings were reported in accordance with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analysis extension for Scoping Reviews) guidelines.

Results: A comprehensive review of 2813 unique abstracts led to the retrieval of 97 full-text articles, with 22 included for detailed assessment. Our literature mapping reveals that successful AI integration within healthcare organizations requires leadership engagement across technological, strategic, operational, and organizational domains. Leaders must demonstrate a blend of technical expertise, adaptive strategies, and strong interpersonal skills to navigate the dynamic healthcare landscape shaped by complex regulatory, technological, and organizational factors.

Conclusions: In conclusion, leading AI transformation in healthcare requires a multidimensional approach, with leadership across technological, strategic, operational, and organizational domains. Organizations should implement a comprehensive leadership development strategy, including targeted training and cross-functional collaboration, to equip leaders with the skills needed for AI integration. Additionally, when upskilling or recruiting AI talent, priority should be given to individuals with a strong mix of technical expertise, adaptive capacity, and interpersonal acumen, enabling them to navigate the unique complexities of the healthcare environment.

Introduction

Artificial intelligence in health care: overview.

Artificial intelligence (AI) technologies have gained significant momentum in health care, presenting a transformative potential across clinical processes, operational efficiency, decision-making, and workforce optimization [ 1 - 3 ]. The global AI market is projected to shift from US $14.6 billion in 2023 to a formidable estimate of US $102.7 billion by 2028 [ 4 ], unveiling a dynamic transformation of unprecedented scale. This investment, coupled with the engagement of nontraditional health care players such as Microsoft, Google, and Amazon and the convergence of technological prowess and health care innovation signaled by generative AI, will place the trajectory of AI in health care in a state of exponential growth [ 5 ].

Current investments in health care AI predominantly center on bolstering data capacity, enhancing computational power, and advancing methodological innovations in AI. This includes developing and testing AI models and algorithms tailored for precision medicine, drug discovery, clinical decision-making support, public health surveillance, operational optimization, and process improvement [ 6 , 7 ]. Notably, between August 2022 and July 2023, there were over 150 submissions of drug and biological applications incorporating AI and machine learning components to the US Food and Drug Administration, encompassing a wide array of therapeutic domains and developmental stages [ 8 ].

Yet the seamless integration of AI technologies into health care organizational settings presents a multifaceted challenge for health care leaders. This challenge arises from several factors, including the complex nature of AI models, the rapid pace of technological advancement, the imperative of regulatory adherence, ethical concerns surrounding data security and privacy, the risk of perpetuating racial and ethnic biases in data, the necessity of prioritizing human-centric approaches to patient care, and the intricate clinical workflows that must be navigated [ 9 - 15 ]. Furthermore, health care leaders are facing critical and intricate strategic decisions. They must discern which AI solutions merit investment while weighing the merits of in-house development against strategic partnerships with external vendors. Selecting the right vendors and defining the scope of collaboration is pivotal, as is devising a sustainable funding strategy to support both initial development and continuous innovation. Furthermore, they must confront the crucial question of whether to bring in new AI talent or bolster the expertise of their current workforce through upskilling. Each of these decisions will shape the trajectory of health care organizations as they navigate this transformative era. A report by Bain in 2023 revealed that although 75% of surveyed health system executives recognize AI’s potential to reshape the health care industry, only 6% have established concrete strategies related to AI [ 16 ].

The lack of strategy and strategic failures in AI integration not only have financial consequences for organizations but also erode trust among patients, providers, and organizations [ 17 ]. A prominent example is the collaboration between MD Anderson and IBM Watson, aimed at leveraging IBM Watson’s cognitive capabilities to combat cancer. This ambitious endeavor, however, incurred a substantial financial toll of over US $62 million for MD Anderson because of setbacks in clinical implementation [ 18 ].

Despite a growing body of AI literature, including toolkits such as Canada Health Infoway’s “Toolkit for AI Implementers” [ 19 ] and guidance from the US Department of Health and Human Services’ AI Task Force [ 20 ] and the UK National Strategy for AI in Health and Social Care [ 21 ], there is still insufficient scholarly attention on how leadership behavior guides AI transformation in health care. Existing reviews focus on AI in medical education [ 22 , 23 ], workforce impact [ 24 ], applications in clinical medicine [ 13 , 25 ], barriers to implementation [ 26 , 27 ], and ethical considerations [ 28 , 29 ]. However, no systematic mapping of empirical literature has clarified our understanding of leadership or identified gaps in research. Understanding leadership behavior is crucial for health care organizations considering AI because effective leadership shapes the strategic direction, adoption, and successful implementation of AI technologies.

Research Aim

To address this research gap and to establish a future research agenda this scoping review study aims to address two primary questions: (1) What role does leadership play in AI transformation within health care? and (2) What approaches can health care organizations use to empower their leaders in facilitating AI transformation?

Research Approach

This review follows scoping review methodology [ 30 ] to identify and analyze the current literature and report results following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Scoping Reviews; Multimedia Appendix 1 ) guidelines [ 31 ].

Key Definitions

In the context of this study, AI refers to combination of machine learning algorithms, large language models, robotics, and natural language processing systems designed to mimic human cognitive functions, enabling machines to perform tasks autonomously or with minimal human intervention.

AI transformation refers to the systematic changes in clinical, operational, or organizational processes and business models due to the introduction of AI systems to optimize decision-making, automate tasks, improve patient outcomes, and drive organizational change. This involves identifying opportunities for AI-related innovation, integrating them into processes, and developing strategies to operationalize implementation while ensuring organizational readiness. This is essential for getting health care organizations AI-ready.

Further, in the context of this study, drawing from seminal management and leadership theories, we view leadership as an effective management practice [ 32 ]. However, we recognize that leadership roles in health care occur at the clinical, organizational, and systems levels of health systems. At the clinical level, leadership emerges through health care professionals who steer patient care and treatment decisions. At the organizational level, leadership involves middle managers such as unit heads and division leaders guiding health care institutions, administrative units, and personnel toward their goals. At the systems level, leadership encapsulates C-suite leadership responsible for navigating regulatory complexities and organizational and structural silos within complex health systems.

Eligibility Criteria

The following inclusion and exclusion criteria guided our study: (1) focused on AI in health care, (2) contained an evaluation of leadership, (3) were written in English, (4) were published in a peer-reviewed journal, (5) published between January 2015 and June 2023, and (6) used research.

Information Sources and Search Strategy

We adopted comprehensive search strategies for the following electronic databases focused on the health care and business literature: MEDLINE (via Ovid), PsycINFO (via Ovid), CINAHL (via EBSCO), Business Source Premier (via EBSCO), and Canadian Business & Current Affairs (via ProQuest). An academic librarian developed these search strategies with input from the research team. We initially conducted the search in Ovid MEDLINE. We then reviewed our search results using the Peer Review of Electronic Search Strategies tool [ 33 ], a checklist for comparing, among other things, the types of errors found in articles and the relative fit of articles to the research question before translating the search strategy into other databases using their command language. Our search was limited to articles published from January 2015 (from the first use of AI-powered chatbots in health care [ 34 ] to June 2023. We then ran searches in 4 databases and exported the final search results into the EndNote reference management software (Clarivate), and we removed duplicate articles manually. To capture any papers that may have been missed during the search process, we did forward and reverse citation searches of systematic review articles related to AI [ 35 ]. However, we did not find any additional articles that met our criteria. Finally, we imported search results to Covidence (Veritas Health Innovation), a review management software for abstract and title screening, full-text screening, and data charting.

Selection of Sources of Evidence and Data Charting

To minimize selection bias, 2 independent screeners reviewed the titles and abstracts of articles identified via the search against the eligibility criteria using Covidence. We identified articles that met the eligibility criteria for a comprehensive full-text screening. Two independent reviewers then evaluated the full texts against the eligibility criteria using Covidence. In discrepancies between the reviewers, a third reviewer served as the consensus reviewer and used Covidence to resolve conflicts between reviewer 1 and reviewer 2. Following the exclusion of irrelevant articles, we used a predefined data extraction form aligned with our research objectives and guiding questions for systematic data collection. Data extraction categories included data on study characteristics (eg, citations and country); methods (eg, aim, data collection methods, and methodological quality); study context (eg, leadership role, ie, clinical, organizational, or systems); leadership practices (ie, behavior, enablers, and barriers to leadership success); results (ie, main results and author conclusion); and an open-ended reviewer note (ie, capture any relevant information that might aid in the data analysis stage). The data abstraction form was piloted on a random sample of 4 included articles and modified based on feedback from the team. Full data abstraction began only after sufficient agreement had been obtained. Two reviewers independently extracted the data using Covidence, and a third reviewer assessed the data extraction for quality and consensus. Three authors then held a group discussion to resolve any conflicts.

Risk of Bias Assessment

The focus of scoping reviews is to provide a comprehensive overview of the available literature, identifying the extent, range, and nature of research on a particular topic rather than assessing the methodological quality of individual studies [ 35 ]. Therefore, we did not perform risk of bias evaluations on the articles included in compliance with the guidelines for scoping reviews.

Data Analysis and Synthesis

Our data analysis was guided by a thematic analysis process [ 36 ]. To ensure the accuracy of the emerging themes, we conducted our analysis collaboratively in reviewer pairs [ 35 ].

We initially analyzed the extracted data using an open-coding method guided by our research questions. Subsequently, we grouped the codes into categories based on the emerging patterns in the data, which we then synthesized into leadership functional domains, capacities, and context.

In the context of our analysis, functional domains refer to distinct areas of responsibility that a leader must effectively manage a task or a role. Capacity, on the other hand, pertains to the abilities—skills, competencies, or behaviors—that a leader must demonstrate to achieve desired goals. Context refers to the environment, conditions, and situational factors that shape and influence leadership practices and decisions.

Study Selection

As described in Figure 1 , the original searches generated 3541 articles published from January 2015 to June 2023. After removing 728 duplicate articles in EndNote, 2813 unique articles were uploaded to Covidence. A total of 2813 relevant studies were then screened using Covidence using the articles’ titles and abstracts. We determined that 97 articles met the criteria for a full-text review for eligibility screening. Within these 97 articles, 75 were excluded as they were opinion articles or commentaries without objective data. After conducting the full-text screening, we found that 22 articles met the final inclusion criteria.

Study Characteristics

Of the 22 studies identified for final inclusion in our review, 12 involved qualitative methods [ 37 - 48 ] such as interviews and case studies, whereas 4 studies involved mixed methods research [ 49 - 52 ] with a qualitative and quantitative strand. There were 3 narrative reports [ 53 - 55 ] based on document synthesis, and 3 studies involved quantitative methods [ 56 - 58 ] such as surveys. These articles focused on clinical, organizational, and systems leadership and came from Canada, China, Finland, Saudi Arabia, Sweden, the Netherlands, the United Kingdom, and the United States. The included papers addressed a broad array of AI applications in health care, including studies focused on improving workflows, quality of care, patient safety, resource optimization, and patient experience. From a clinical domain, researchers focused on primary care, health care systems, radiology, or global health. From a population perspective, the papers covered leadership from the perspective of primary care physicians, radiologists, nurses, nurse managers, public health professionals, global health professionals, health care entrepreneurs, and health care leaders. Table 1 provides a summary of study characteristics.

a N/A: not applicable.

Leadership Tasks Essential for AI Transformation in Health Care

We mapped the themes from the included studies across 4 functional domains of leadership task responsibility—technological (AI innovation), strategic (vision and alignment), operational (process and oversight), and organizational (culture and work environment).

The technological functional domain garnered the most significant attention in the literature. The core themes that emerged under the technological domain primarily focused on applying subject matter expertise and AI technical skills to effectively identify AI opportunities, as well as to foster an innovation mindset to develop, tailor, and seamlessly implement AI-driven solutions to address key AI opportunities within health care organizations.

Within the strategic functional domain, the literature underscored the importance of change management and communication as strategic tools for consensus and collaboration related to the AI transformation process. Another core theme that emerged focused on the critical importance of integrating AI solutions into the existing clinical care processes. This strategic alignment is essential for getting support from the staff and ensuring smooth operations of patient care outcomes while embracing the potential of AI solutions. Although the significance of talent strategy related to the recruitment and retention of AI technical expertise within organizations was mentioned, it was not widely seen across the included papers.

Table 2 provides a summary of how the technological and strategic functional domains map across the papers and provides key themes that emerged with the domain area.

Emerging evidence in the operational functional domain highlights leaders’ need to navigate ethical and risk management issues by establishing robust governance structures prioritizing patient data privacy and security while ethically integrating AI technologies within existing workflows. Additionally, the literature emphasizes that implementing AI in health care will require leaders to ensure new AI solutions comply with existing regulatory and control systems. The literature highlighted that leaders need to pay attention to process agility through continuous monitoring to ensure AI solutions can adapt to contextual changes.

Finally, the organizational functional domain emerges from the thematic analysis as a pivotal area for AI leadership. The literature emphasizes the importance of stakeholder engagement in building collaboration. Furthermore, it underscores the importance of decision makers’ sense-making to enhance their trust in AI opportunities and ensure that AI integration is supported by individuals across the organization. Further, the literature underscored the importance of organizational culture readiness to support physicians and nurses through protected time and incentive pay to engage, innovate, and adopt AI solutions. Table 3 provides a summary of how operational and organizational functional domains map across the papers.

Leadership Skills and Behaviors for Preparing Health Care Organizations for AI Transformation

We categorized the themes related to skills and behaviors into 3 essential capacities that a leader must demonstrate to achieve desired goals—technical capacity, adaptive capacity, and interpersonal capacity. Technical capacity encompasses (1) AI literacy, (2) subject matter knowledge, (3) change leadership skills, and (4) innovation mindset to identify AI innovation opportunities. The interpersonal capacity involves several vital facets such as (1) the ability to foster partnerships among diverse stakeholders, (2) the ability to comprehend diverse stakeholder perspectives and deftly influence adoption, (3) the ability to build trust and collaboration, (4) self-awareness and humility to assemble teams with complementary skills, and (5) the integrity and accountability to embody ethical principles. The adaptive capacity encompasses (1) the foresight and sense-making abilities to discern emerging technologies and their implications within the health care sphere; (2) the agility to identify and capitalize on transformative opportunities, swiftly adapting and aligning strategies with evolving contexts; and (3) systems thinking to enable an understanding of how elements interconnect and how changes in 1 area can reverberate throughout the entire system.

Contextual Factors Influencing Leadership in AI Transformation

The emerging themes from our review reveal that dynamic environmental and situational factors, including regulatory, technology, and organizational contexts, shape AI transformation within health care organizations. For instance, the regulatory context and frameworks related to health professions and health care organizations play a critical role in how AI can be integrated within the organizations. Similarly, the technology context such as the availability of AI technical talent, the retention of technical expertise, the dynamic nature of AI maturity, and the presence of incentives and technological resources for AI innovation or adoption will significantly influence a leader’s ability to effectively drive AI readiness. Finally, the organization context is a critical influence on leaders’ capacity for AI adoption and implementation. Organizations that promote and reward innovation and that have transparent communication practices shape leaders’ ability to pursue AI opportunities.

Strategies for Empowering Health Care Leaders to Facilitate AI Transformation

For the technological domain, the included papers discussed approaches such as upskilling clinical experts with the necessary AI technical skills and ensuring the presence of specialized experts, such as computer scientists, to enable the subject matter experts to develop, test, and seamlessly integrate AI solutions. Further, the papers discussed collaborative strategies such as clinicians and computer scientists working together to effectively identify AI opportunities and develop, adopt, and implement AI solutions in clinical or operational areas.

For the strategic domain, organizational support was essential in supporting leaders to assess and identify AI opportunities that strategically align with organizational priorities and develop strategies to ensure AI transformation garners support from key stakeholders within the complex regulatory and environmental contexts. The literature also highlighted the competition for AI talent in health care and emphasized the significance of talent retention strategies to preserve the organization’s AI technical expertise.

Then, in the operational domain, the emphasis was on establishing governance structures to continuously monitor data quality, patient privacy, and patient care experiences and assess the feasibility and financial implications of AI transformation. These governance structures ensure effective oversight and management of AI initiatives within health care organizations.

Finally, for the organizational domain, the focus was on the pivotal role of organizational culture in AI leadership. Leaders require organizational support to cultivate an environment that fosters innovation and actively incentivizes clinical leaders, such as physicians and nurses, through protected time and incentive pay to innovate and adopt AI solutions. Transparent decision-making processes related to AI solutions are essential cultural elements that build trust in AI systems and promote collaboration among the diverse stakeholders involved in AI transformation within health care organizations.

Principal Findings

The purpose of a scoping review is not to draw definitive conclusions but to map the literature, identify emerging patterns, and develop critical propositions. As described in Figure 2 , analysis of current literature shows that leading organizations toward AI transformation requires multidimensional leadership. As such, health care organizations need to engage leaders in the technological, strategic, operational, and organizational domains to facilitate AI transformation in their organizations. Further, the reviewed papers suggest that individuals in AI-related leadership roles need to demonstrate (1) technical capacity to understand the technology and innovation opportunities, (2) adaptive capacity to respond to contextual changes, and (3) interpersonal capacity to navigate the human aspects of the AI transformation process effectively. Furthermore, our study illuminates that leaders in the AI-related leadership roles need to navigate regulatory context, the dynamic nature of changing technology context, and organization context.

Prior Research

Health care organizations are marked by multifaceted interdependencies among medical facilities, health care providers, patients, administrative units, technology, and the regulatory environment. Therefore, the leadership required for AI transformation—which includes identifying AI opportunities, implementing AI solutions, and achieving full-scale AI adaptation—is not a static role but a continuous and dynamic process. Effective leadership involves the capacity to continuously identify opportunities for AI transformation, influence the thoughts and actions of others, and navigate the complex dynamics of the health care setting and AI technology landscape simultaneously. However, the current literature has not fully articulated this multidimensionality, often focusing on leadership through a linear approach.

Further, multiple situational factors can shape AI transformation. First, the rapid growth of AI technologies introduces an element of uncertainty, making it challenging to anticipate the long-term impact and sustainability of specific AI solutions [ 6 ]. Second, AI implementation involves many stakeholders, from technical experts and domain specialists to clinicians, administrators, patients, vendors, and regulatory bodies. Each stakeholder group brings its unique perspectives, priorities, and control systems into the equation, necessitating leaders to navigate competing values, trade-offs, and paradoxes [ 27 ]. Third, once alignment is achieved, the integration of AI within an organization triggers a need for a cultural shift, altering work practices and decision-making processes [ 38 , 59 ]. Fourth, the effectiveness of AI solutions hinges on the availability of high-quality data for informed insights and decision-making. When implementing solutions originally developed within different contexts, local organizations must ensure data integrity and the solution’s adaptability to the organization’s unique context [ 18 ]. This challenge is compounded by emerging regulatory frameworks, which add a layer of complexity. Ensuring compliance and the responsible use of AI technologies has become a critical consideration [ 29 , 50 , 60 ]. Finally, introducing AI may provoke resistance from employees concerned about job displacement or disruptions to established workflows. This problem is further compounded when an organization transitions toward integrating multiple AI systems, as these changes can lead to periods of chaos and confusion [ 59 ].

Emerging key opinions and evidence from outside the health domain indicate that leaders must possess an understanding of data quality nuances, assess process risks, and manage AI as a new team member. Additionally, leaders should have a firm grasp of technology, articulate clear business objectives, define precise goals, uphold a long-term vision, prepare their teams for AI transformation, manage data resources effectively, and foster organizational collaboration [ 3 , 61 - 67 ].

Our findings on the leadership required for AI transformation in health care organizations reinforce this multidimensionality of leadership to effectively navigate the complexities of AI transformation and successfully leverage its potential to drive transformative change. Leaders must operate across different functional domains—technological, strategic, operational, and organizational—while demonstrating technical, adaptive, and interpersonal capacities.

Further, our findings show contingency leadership theories, complexity theory, and transformational leadership theory as relevant theoretical domains for further explaining the different facets of leadership behaviors needed to navigate the multidimensionality of leadership required for AI transformation.

Contingency theories suggest that leadership effectiveness depends on situational factors, which should be considered in future AI implementation studies in the context of AI adaptation and integration within health care organizations [ 68 , 69 ]. Complexity theory provides a framework for examining leadership behaviors in interconnected, dynamic environments where leaders must balance innovation and stability and demonstrate an adaptive approach to challenges, characterized by uncertainty and change [ 70 - 73 ]. Transformational leadership theory emphasizes motivating, empowering, and developing others by fostering trust and collaboration while challenging the status quo to drive organizational change and innovation [ 74 , 75 ]. These theories should be considered in future AI implementation studies within health care organizations.

Future research and training programs related to AI in health care should examine the leadership required for AI transformation through the lens of multidimensionality, providing insights into the interrelatedness of functional domains, leadership capacities, and contextual enablers and barriers, while exploring the key theoretical domains related to contingency, complexity, and transformational leadership to further understand the interpersonal dynamics shaping AI transformation in health care.

Limitations

Some limitations to our scoping review are worth noting. First, given the contextual variability in the included studies and the methodological variations, we could not establish firm correlations about specific leadership domains, capacities, and contextual factors; the effectiveness of leadership approaches; or the moderating effects of contextual factors. Consequently, we have presented only the overarching emergent themes.

Second, our study is limited by the significant variation in conceptual definitions of leadership and leadership competencies found in the current literature, which often lacks more standardized definitions or instruments for measurement. This variation caused conceptual inconsistencies. We addressed the inconsistencies by clearly defining what constitutes a functional domain, capacity, and context before our data analysis to address this. We iteratively coded the data into themes to ensure all relevant aspects were captured.

Third, our search strategy focused on MEDLINE-indexed journals, which may exclude some newer journals indexed in PubMed but not yet in MEDLINE. While this might limit the capture of the very latest advancements in digital health, it does not diminish the robustness of the review. Fourth, we retrieved only articles written in English, which possibly limited the comprehensiveness of our findings. Fifth, we looked at AI as a system and did not look at the relationship between the implementation of different types of AI tools and leadership behaviors which was beyond the scope of our review. Finally, our analysis used an inductive approach and was not informed by a predetermined theory to aid the mapping of the literature. This may have limited our analysis in capturing different elements of an umbrella theory.

Recommendations for Future Design and Research

Leading organizations toward AI transformation is an adaptive challenge influenced by a myriad of interwoven situational factors that create a dynamic and intricate environment. The body of literature related to AI in health care is rapidly expanding, and the recommendations imparted by this review, alongside the multidimensional leadership framework ( Figure 2 ), stand poised to guide research and practice to empower health care organizations in their AI transformation journey. Future research on AI transformation, which includes innovation identification, implementation, and scaling, can use this framework to understand the role of leadership in driving successful outcomes.

Further, future research must undergo methodological expansion by embracing qualitative and mixed methods approaches to illuminate the intricate temporal aspects of AI transformation and corresponding evolving leadership behaviors.

Conclusions

In summary, emerging evidence shows that multidimensional leadership plays crucial role in AI transformation in health care organization. Leaders must adeptly balance technology opportunities while demonstrating unwavering empathy for stakeholder needs and nimble adaptability to accommodating the ever-changing contextual landscape, which encompasses the regulatory frameworks, the evolution of technology, and the organization’s priorities.

Acknowledgments

This work is supported through a grant from the University of Toronto’s Connaught Global Challenges. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the study.

Data Availability

The main study data are the data extraction materials and quality ratings of included papers, most of which are included in the study tables. The data sets generated and analyzed during this study are available from the corresponding author on reasonable request.

Authors' Contributions

All authors were involved in conception and design of the study and approved the protocol. AS and NS were responsible for overseeing the search of databases and literature. AS, NS, and SS were involved in the screening of articles, data extraction and data verification, and analysis of data. All authors were involved in data interpretation, supported in the drafting of the paper, which was led by AS, and all authors supported in revising and formatting of the paper. All authors have provided final approval of the version of the paper submitted for publication, and all authors agree to be accountable for all aspects of the work.

Conflicts of Interest

None declared.

Updated PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) checklist.

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Abbreviations

Edited by T de Azevedo Cardoso, G Eysenbach; submitted 14.11.23; peer-reviewed by D Chrimes, TAR Sure, S Kommireddy, J Konopik, M Brommeyer; comments to author 20.02.24; revised version received 12.03.24; accepted 15.07.24; published 14.08.24.

©Abi Sriharan, Nigar Sekercioglu, Cheryl Mitchell, Senthujan Senkaiahliyan, Attila Hertelendy, Tracy Porter, Jane Banaszak-Holl. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 14.08.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research (ISSN 1438-8871), is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

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