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• 01 Apr 2024
• In Practice

Navigating the Mood of Customers Weary of Price Hikes

Price increases might be tempering after historic surges, but companies continue to wrestle with pinched consumers. Alexander MacKay, Chiara Farronato, and Emily Williams make sense of the economic whiplash of inflation and offer insights for business leaders trying to find equilibrium.

• 22 Mar 2024
• Research & Ideas

Open Source Software: The $9 Trillion Resource Companies Take for Granted

Many companies build their businesses on open source software, code that would cost firms $8.8 trillion to create from scratch if it weren't freely available. Research by Frank Nagle and colleagues puts a value on an economic necessity that will require investment to meet demand.

• 27 Feb 2024
• Cold Call Podcast

How Could Harvard Decarbonize Its Supply Chain?

Harvard University aims to be fossil-fuel neutral by 2026 and totally free of fossil fuels by 2050. As part of this goal, the university is trying to decarbonize its supply chain and considers replacing cement with a low-carbon substitute called Pozzotive®, made with post-consumer recycled glass. A successful pilot project could jump start Harvard’s initiative to reduce embodied carbon emissions, but it first needs credible information about the magnitude and validity of potential carbon reductions. Harvard Business School professor emeritus Robert Kaplan and assistant professor Shirley Lu discuss the flow of emissions along the supply chain of Harvard University’s construction projects, the different methods of measuring carbon emissions, including the E-liability approach, and the opportunity to leverage blockchain technology to facilitate the flow of comparable and reliable emissions information in the case, “Harvard University and Urban Mining Industries: Decarbonizing the Supply Chain.”

• 02 Jan 2024

10 Trends to Watch in 2024

Employees may seek new approaches to balance, even as leaders consider whether to bring more teams back to offices or make hybrid work even more flexible. These are just a few trends that Harvard Business School faculty members will be following during a year when staffing, climate, and inclusion will likely remain top of mind.

• 12 Dec 2023

COVID Tested Global Supply Chains. Here’s How They’ve Adapted

A global supply chain reshuffling is underway as companies seek to diversify their distribution networks in response to pandemic-related shocks, says research by Laura Alfaro. What do these shifts mean for American businesses and buyers?

• 25 Apr 2023

How SHEIN and Temu Conquered Fast Fashion—and Forged a New Business Model

The platforms SHEIN and Temu match consumer demand and factory output, bringing Chinese production to the rest of the world. The companies have remade fast fashion, but their pioneering approach has the potential to go far beyond retail, says John Deighton.

• 21 Apr 2023

The $15 Billion Question: Have Loot Boxes Turned Video Gaming into Gambling?

Critics say loot boxes—major revenue streams for video game companies—entice young players to overspend. Can regulators protect consumers without dampening the thrill of the game? Research by Tomomichi Amano and colleague.

• 11 Apr 2023

A Rose by Any Other Name: Supply Chains and Carbon Emissions in the Flower Industry

Headquartered in Kitengela, Kenya, Sian Flowers exports roses to Europe. Because cut flowers have a limited shelf life and consumers want them to retain their appearance for as long as possible, Sian and its distributors used international air cargo to transport them to Amsterdam, where they were sold at auction and trucked to markets across Europe. But when the Covid-19 pandemic caused huge increases in shipping costs, Sian launched experiments to ship roses by ocean using refrigerated containers. The company reduced its costs and cut its carbon emissions, but is a flower that travels halfway around the world truly a “low-carbon rose”? Harvard Business School professors Willy Shih and Mike Toffel debate these questions and more in their case, “Sian Flowers: Fresher by Sea?”

• 28 Mar 2023

The FDA’s Speedy Drug Approvals Are Safe: A Win-Win for Patients and Pharma Innovation

Expediting so-called breakthrough therapies has saved millions of dollars in research time without compromising drug safety or efficacy, says research by Ariel Stern, Amitabh Chandra, and colleagues. Could policymakers harness the approach to bring life-saving treatments to the market faster?

• 31 Jan 2023

Addressing Racial Discrimination on Airbnb

For years, Airbnb gave hosts extensive discretion to accept or reject a guest after seeing little more than a name and a picture, believing that eliminating anonymity was the best way for the company to build trust. However, the apartment rental platform failed to track or account for the possibility that this could facilitate discrimination. After research published by Professor Michael Luca and others provided evidence that Black hosts received less in rent than hosts of other races and showed signs of discrimination against guests with African American sounding names, the company had to decide what to do. In the case, “Racial Discrimination on Airbnb,” Luca discusses his research and explores the implication for Airbnb and other platform companies. Should they change the design of the platform to reduce discrimination? And what’s the best way to measure the success of any changes?

• 29 Nov 2022

How Much More Would Holiday Shoppers Pay to Wear Something Rare?

Economic worries will make pricing strategy even more critical this holiday season. Research by Chiara Farronato reveals the value that hip consumers see in hard-to-find products. Are companies simply making too many goods?

• 18 Oct 2022

Chewy.com’s Make-or-Break Logistics Dilemma

In late 2013, Ryan Cohen, cofounder and then-CEO of online pet products retailer Chewy.com, was facing a decision that could determine his company’s future. Should he stay with a third-party logistics provider (3PL) for all of Chewy.com’s e-commerce fulfillment or take that function in house? Cohen was convinced that achieving scale would be essential to making the business work and he worried that the company’s current 3PL may not be able to scale with Chewy.com’s projected growth or maintain the company’s performance standards for service quality and fulfillment. But neither he nor his cofounders had any experience managing logistics, and the company’s board members were pressuring him to leave order fulfillment to the 3PL. They worried that any changes could destabilize the existing 3PL relationship and endanger the viability of the fast-growing business. What should Cohen do? Senior Lecturer Jeffrey Rayport discusses the options in his case, “Chewy.com (A).”

• 12 Oct 2022

When Design Enables Discrimination: Learning from Anti-Asian Bias on Airbnb

Airbnb bookings dropped 12 percent more for hosts with Asian names than other hosts during the early months of the COVID-19 pandemic, says research by Michael Luca. Could better design deter bias, particularly during times of crisis?

• 22 Aug 2022

Can Amazon Remake Health Care?

Amazon has disrupted everything from grocery shopping to cloud computing, but can it transform health care with its One Medical acquisition? Amitabh Chandra discusses company's track record in health care and the challenges it might face.

• 12 Jul 2022

Can the Foodservice Distribution Industry Recover from the Pandemic?

At the height of the pandemic in 2020, US Foods struggled, as restaurant and school closures reduced demand for foodservice distribution. The situation improved after the return of indoor dining and in-person learning, but an industry-wide shortage of truck drivers and warehouse staff hampered the foodservice distributor’s post-pandemic recovery. That left CEO Pietro Satriano to determine the best strategy to attract and retain essential workers, even as he was tasked with expanding the wholesale grocery store chain (CHEF’STORE) that US Foods launched during the pandemic lockdown. Harvard Business School Professor David E. Bell explores how post-pandemic supply chain challenges continue to affect the foodservice distribution industry in his case, “US Foods: Driving Post-Pandemic Success?”

• 05 Jul 2022
• What Do You Think?

Have We Seen the Peak of Just-in-Time Inventory Management?

Toyota and other companies have harnessed just-in-time inventory management to cut logistics costs and boost service. That is, until COVID-19 roiled global supply chains. Will we ever get back to the days of tighter inventory control? asks James Heskett. Open for comment; 0 Comments.

• 05 May 2022

Why Companies Raise Their Prices: Because They Can

Markups on household items started climbing years before the COVID-19 pandemic. Companies have realized just how much consumers will pay for the brands they love, says research by Alexander MacKay. Closed for comment; 0 Comments.

• 31 Mar 2022

Navigating the ‘Bermuda Triangle’ in Professional Services

Not all companies need to scale. Ashish Nanda explores a crucial choice that leaders of professional services firms face as their organizations grow. Open for comment; 0 Comments.

• 28 Feb 2022

How Racial Bias Taints Customer Service: Evidence from 6,000 Hotels

Hotel concierges provide better service to white customers than Black and Asian customers, says research by Alexandra Feldberg and colleague. They offer three strategies to help companies detect bias on the front line. Open for comment; 0 Comments.

• 10 Feb 2022

Why Are Prices So High Right Now—and Will They Ever Return to Normal?

And when will sold-out products return to store shelves? The answers aren't so straightforward. Research by Alberto Cavallo probes the complex interplay of product shortages, prices, and inflation. Open for comment; 0 Comments.

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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At iResearchNet, we understand the challenges students face when tasked with writing a research paper on a complex subject such as operations management. That’s why we have designed our services to offer a customized solution that caters to your unique requirements. Whether you need assistance with topic selection, research, writing, or formatting, our team of professional writers and researchers is ready to provide comprehensive support.

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iResearchNet is committed to empowering students in their academic journey by providing top-tier writing services tailored to the complex world of operations management. Our comprehensive approach, attention to detail, and dedication to excellence set us apart in the industry.

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Empirical analysis of the market structure of the Hungarian bank market

• Open access
• Published: 27 April 2024

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• Veronika Szádoczkiné Varga   ORCID: orcid.org/0000-0002-6836-5706 1 &
• Zoltán Madari 2  

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This paper analyzes the market structure of the Hungarian bank market and the effects of the financial crisis of 2008 on it. With a static and a dynamic panel model we estimate the elasticity of total revenues with respect to changes in input prices so that we can determine the market structure based on the Panzar and Rosse methodology for the period between 2003 and 2020. We test the input price elasticity according to the balance sheet and profit and loss data of the top 13 companies. The results show that the Hungarian bank market was a monopolistic competition or a monopoly market in long run equilibrium. To see the effects of the financial crisis we use three subsamples, furthermore we estimate and test the H-statistic with a fixed effect model. During the examined period the level of factor price elasticity increased in time.

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1 Introduction

Examining the market structure of a sector is an interesting question both from the consumer protection and the supervision point of view. The stability of the Systematically Important Financial Institutions is a crucial issue due to the financial crises. Difficulties in the banking sector are quickly spreading to other markets. Therefore, it is an important question to examine the market structure of the banking sector, as well as to analyze the impact of a financial crisis. Financial markets are often mentioned as an example of the perfect competition market.

This paper focuses on the Hungarian bank sector. After the change of regime more private banks appeared in the country, while joining the European Union was also a huge step for Hungary. We examine whether the market can be considered perfectly competitive. Furthermore, taking into account the evolution of the market structure over time, we separately examine the period before the financial crisis of 2008, the years of the crisis, and the years after that. This study addresses the question about the development of the market structure of the Hungarian bank sector before and after the financial crisis of 2008. The objective of this research is to determine whether the monopoly or the perfect competition case fits better the factor price elasticity calculated by the balance sheet data between 2003 and 2020.

The determination of the market structure is based on the Panzar and Rosse methodology (Panzar and Rosse 1987 ). The model gives a testable hypothesis about factor price elasticity which allows us to distinguish between cases of monopoly, monopolistic competition, and perfect competition. We estimate and test the input price elasticity according to the balance sheet data of the top 13 companies in the time-period between 2003 and 2020. We create three subperiods to determine the development of the market structure before and after the crisis.

We estimate factor price elasticity with a static and a dynamic panel model to test the market structure and create subperiods to see the changes after the crisis. The results show that the H-statistic increased during the three periods in Hungary. Before the crisis the bank sector was a monopoly market, during the crisis (2008–2012) the market was not in long run equilibrium. Examining the whole period we used a static and a dynamic estimation, the structure of the Hungarian bank market differs significantly from the perfect competition case. Between 2003 and 2020 the bank market was in long-run equilibrium and the market structure was a monopolistic competition or a monopoly market.

The rest of the paper is organized as follows. First, Sect.  2 contains a literature review, and Sect.  3 presents the methodology of the Panzar and Rosse model and the dynamic panel model approach. Section  4 describes the dataset, while Sect.  5 contains the main results of the paper. Finally, Sect.  6 discusses the findings, and Sect.  7 concludes.

2 Literature review

The Panzar and Rosse method is often used to determine the market structure of a bank market. Most studies use static panel estimation, although according to Goddard and Wilson ( 2009 ) the dynamic panel model should be used to estimate the Panzar–Rosse H-statistic as the static model can cause bias. Apergis ( 2015 ) also uses a static Panzar and Rosse model to determine the changes in the market structure caused by the crisis with three subperiods in the case of emerging market economies. The results show that after the crisis a lower level of competition can be seen between 2000 and 2012.

The market structure of the Hungarian bank sector has been already studied, however, with another methodology. Hosszú and Dancsik ( 2018 ) estimate the efficiency of Hungarian banks with stochastic frontier analysis and data envelopment analysis models and calculate the Lerner index for both the household and the corporate credit market. The two segments show a mixed picture, on the corporate credit market the competition is stronger. According to the article, the Hungarian banking sector is homogeneous from the perspective of cost efficiency, but it is heterogeneous in terms of profit efficiency. The crisis had a positive effect on cost efficiency. After 2008, the profit efficiency deteriorated at first, but by rationalizing activities it improved after.

The Hungarian National Bank also has working papers, which deal with the question of the market structure. Móré and Nagy ( 2003 ) uses structural methods (structure-conduct-performance and relative market power hypotheses) to measure the competition in the pricing behavior and profitability of Central and Eastern European banks. They use the data of individual banks of eight Central and Eastern European countries (Estonia, Lithuania, Latvia, the Czech Republic, Slovakia, Poland, Hungary, and Slovenia) from the period of 1998–2001. According to the results, the market concentration has no positive correlation with either the net interest margin or Return on Assets. This implies that in more concentrated markets, banks did not earn higher profits. But the results show that dominant banks of the countries in the region earned extra profit and caused a welfare loss by exploiting their pricing advantage arising from the relative market power and by behaving in a manner that limited competition. Móré and Nagy ( 2004 ) focuses only on the Hungarian sector between 1996 and 2003, using the Bresnahan model, which is a non-structural approach of measuring competition. Based on the results, the degree of competition in the loan and deposit markets is between perfect competition and the Cournot equilibrium. On the other hand, the consumer credit market is characterized by a lower degree of competition, between Cournot equilibrium and perfect collusion. Molnár et al. ( 2007 ) compares the level of the profit margin in the Hungarian market with the theoretical values of Bertrand orphans and collusion. The comparison of the observed data between 2003 and 2005 with the theoretical models show that the competitive market is limited, the degree of competition in the other segments can be considered low.

Aczél et al. ( 2016 ) studied the reason of the high level of the average spread of Hungarian home loans above the three-month interbank interest rate before 2016. They found that the higher premium is currently primarily caused by the high proportion of products with fixed interest rates beyond one year, the relatively large stock of non-performing loans, credit losses, high operating costs, demand-side peculiarities, and the low degree of competition observed in certain regions. According to the results of the used panel model, the distribution of branches plays an important role in the evolution of mark-ups. The findings indicate that the Hungarian population chooses from a well-defined, narrow circle of banks when making loan decisions. This is due to the territorial distribution of the banks’ branch network, the banks’ business model, and the taste patterns observed in the society. These constraints and patterns give banks opportunities to price their products according to oligopolistic competition.

Although the literature of the Hungarian market structure is abundant, the impact of the crisis on it is rarely studied. González ( 2016 ) investigated the impact of the global financial crisis and the level of market competition of banks on corporate investment across countries. Lyons ( 2009 ) concentrated on bank bailouts and government money. Várhegyi ( 2010 ) focuses on the Hungarian bank market and studies the effect of international and Hungarian regulations based on the Herfindhal-Hirschman index, the concentration ratio, the market share of the companies, and balance sheet data.

In the literature, the Panzar and Rosse method is often used in the bank sector. Table 1 summarizes the results of these studies for different time periods and different countries. In most countries the bank sector is a monopolistic competition in long run equilibrium. In Canada in 1982, in Italy between 1998 and 2004, and in Nepal between 2010 and 2019 the hypothesis of the perfect competition cannot be rejected. On the other hand, there are bank markets with monopoly structure where the decision of the companies does not depend on the rivals, which implies huge market power, e.g., in Italy between 1986 and 1989, in Japan between 1986 and 1988, in the case of small banks in France and Germany between 1992 and 1996, in Japan, UK, US between 1998 and 2004, and in Bosnia and Herzegovina and Syria between 2005 and 2016.

Some research also includes Hungary. Drakos and Konstantinou ( 2005 ) focuses on post socialist countries between 1992 and 2000, Weill ( 2013 ) studies the countries of the European Union (EU) and Andrade ( 2017 ) collects data from the whole European Union, but it does not contain results about the individual countries. These studies use static models, OLS, or fixed effect panel estimations, and according to these results the European Union market and the Hungarian market was in long run monopolistic competition before 2010.

There are several structural and non-structural models to determine the market structure of a sector. The data request of the non-structural Panzar and Rosse method is relatively small, it gives a testable hypothesis of profit maximizing companies in different market structures. Its advantage is that there is no need for explicit information about the structure of the market. Only the sum of the factor price elasticities of the reduced form revenue equation (denoted by H) should be estimated with the help of revenues, and factor prices of the companies calculated from the balance sheet data. It allows to distinguish between the monopoly, monopolistic competition, and perfectly competitive market structures.

The reduced form revenue equation is the following:

where \(R(y,z)\) is the reduced form revenue function, \(y\) is the decision variable, and \(z\) are further exogenous variables which influence the revenue function. \(C(y,w,t)\) is the cost function, where \(w\) is the vector of exogenous factor prices, and \(t\) is the vector of additional exogenous variables that influence the costs.

The testable expression is the sum of the factor price elasticities of the reduced form revenue equation:

where * means the profit maximizing values.

The factor price elasticity is unique in the case of perfectly competitive companies in long-run equilibrium (H = 1). In the neoclassical monopoly model the elasticity is nonpositive ( \(H\le 0\) ). In this case the decision of a company does not depend on the rivals’ decisions. For a monopolistic competitor in long run equilibrium the factor price elasticity is between 0 and 1 (0 < H < 1) (Table  2 ).

To test the long run equilibrium empirically the Return on Assets (ROA) can be estimated with the same independent variables used in the estimation of the factor price elasticity. In long run competitive equilibrium the return rates are not correlated with the input prices so the sum of the factor price elasticities here should be zero.

Goddard and Wilson ( 2009 ) showed that the dynamic rather than a static formulation of the revenue equation should be used to identify the Panzar–Rosse H-statistic, because the fixed effect estimator of the H-statistic can be biased towards zero. We use static and dynamic panel estimations as well when the sample size makes it possible. The static approach means that the model specification does not contain autoregressive, lagged variables. The dynamic approach uses the autoregressive specification of the dependent variable as an explanatory variable. When the lagged value of the dependent variable correlates with the error term, the fixed effect model is not appropriate to solve the problem of endogeneity: the parameters could be biased in that situation. Arellano and Bond ( 1991 ) used Generalized Method of Moments (GMM) estimation in which they use first differences to eliminate the individual effects. They solve the endogeneity problem by using all the lagged values of the dependent variables as instruments. The method is also called one-step GMM panel estimation.

Another important improvement is that Bikker et al. ( 2012 ) shows that only an unscaled revenue equation gives unbiased estimation. Thus, the dependent variable of interest income should not be scaled, and the model should not contain the total asset as a control variable. In most models the level of total assets is a control variable, also in Drakos and Konstantinou ( 2005 ) and Weill ( 2013 ). Our model does not contain the level of total assets as an independent variable to avoid bias.

In our analysis we focus on public companies. In 2020 a total of 34 bank corporations are in the market according to the supervisor’s (Hungarian National Bank) collection. During the data collection we ignore mortgage banks, savings banks, and some further banks with special focus (i.e., export–import, development). We used the data of 13 banks Footnote 1 from between 2003 and 2020, all of them operated during the whole period and there was no fusion between these companies until 2022. The loan portfolio of the 13 banks covered 85% of the bank market loan portfolio in 2020 if we do not consider the mortgage banks and saving banks. Memic ( 2015 ) used the data of 73% of the bank sector in Bosnia and Herzegovina, Kasman and Turgutlu ( 2008 ) collected the data of non-life insurance companies in Turkey that covered 80% of the market’s industry assets. Although Hosszú and Dancsik ( 2018 ) showed that the strength of the competition differs between the household and the corporate credit market, we cannot calculate the used variables separately for these two segments. Thus, we analyze the sector together.

We estimated the factor price elasticity according to a balanced panel dataset with 13 cross-section observations and 17 time periods. As a main source, the statistical report of the Hungarian National Bank (Aranykönyv) was used. To collect more details, we also used the Annual Reports (the balance sheet data, financial reports, and annual reports). With the help of collected values (Total Assets, Cash, Loans, Total deposits, Borrowings, Equity, Operating expenses, Net profit, Interest income, Interest expenses, Personal expenses, General and administrative expenses, Number of employees, Number of branches) we defined the dependent variable as the interest income (Interest Income, source: Aranykönyv). As factor prices the following variables were defined in a way that is often used in the literature as well:

unit price of labor (PL: personnel expenses/number of employees, source: Annual Report),

unit price of business services (PK: operating costs/number of branches, source: Annual Report)

unit price of capital (PF: interest expenses/total deposit + total credit, source: Aranykönyv).

Further control variables are the ratios:

number of branches to total number of branches (ratio of br),

loans to total assets (loans),

interbank deposits to total assets (interbank deposit),

equity to total assets (equity)

cash to total assets (liquidity).

The estimated equation is as follows.

The descriptive statistics by years (minimum, maximum, mean and standard deviation) of the interest income and the input prices are in the Appendix. The mean value of the interest income increased until 2008, however, after the crisis it decreased until 2014 (see Appendix Table  7 ). From 2008 to 2009 the unit price of inputs decreased, which is consistent with the results of Hosszú and Dancsik ( 2018 ), the cost efficiency is better after the crisis although the interest income decreased. In Fig.  1 one can see the growing level of interest income between 2003 and 2020 of the top five banks in Hungary. The market leader is OTP Bank. The curves are similar in all cases: after the crisis of 2008 the interest income decreased in every bank, that stopped around 2013, and a new rise started after 2015.

Interest income of the top 5 banks between 2003 and 2020 in million Ft

By forming subsamples we also examine the evolution of the H statistic over time, which lead to smaller sample sizes. Olivero et al. ( 2011 ) calculated H statistics according to 3 year long subsamples in Asian and American countries to test the robustness of the result about the whole continent. In several cases the estimation was made based on fewer than 50 observations (i.e., between 1997 and 1999 the sample contains from Bolivia 27, from Paraguay 18, from Peru 28, from Singapore 21, from Thailand 41 and from Uruguay 17 banks). Also, Kasman and Turgutlu ( 2008 ) built three year long subsamples on the Turkish insurance market, the sizes of the samples are the following: 66, 77, 47.

To see the changes of the H statistic we used the following subperiods: 2003–2007, 2008–2012 and 2013–2020. Hosszú and Dancsik ( 2018 ) studied the efficiency of the Hungarian bank sector before and after the crisis with different methods, they used the following grouping: 2001–2004 balanced growth, 2005–2008 excessive credit expansion and indebtedness in foreign currencies, 2009–2012 crisis years, 2013–2016 period of recovery. Apergis ( 2015 ) studied the effect of the crisis with the H statistic of Panzar and Rosse in emerging market economies, the subsamples were 2000–2007 and 2008–2011.

We estimated the factor price elasticity with a fixed effect and a dynamic GMM model. The dependent variable is the interest income. In the dynamic model the lagged value of the interest income is in the model as an independent variable as well. Taking the logarithm of the variables, the factor price elasticity is the sum of the estimated coefficients of the factor prices ( \({\beta }_{1}+{\beta }_{2}+{\beta }_{3}\) ).

Table 3 contains the estimated revenue equations and the associated H-statistics for the Hungarian banking system over the full sample in the time-period of 2003–2020. The table shows the coefficients of the significant variables. According to the AR(1) and AR(2) tests two lags are sufficient in the dynamic model. The Sargan over-identification test shows that over-identification can occur in the model because of the large number of instruments, so the results should be treated with reservations. We estimated a pooled OLS, a fixed effect, and a random effect model. According to the joint significance of differing group means test, the pooled OLS estimation is not adequate compared to the fixed effect model. According to the Hausman test the random effect estimation is not consistent, so we also decide in favor of the fixed effect model in the case of our dataset. The fixed effect model could treat the problem of unobserved effect (endogeneity) by a time-demeaned/within transformation (Wooldridge 2012 ).

To test the market structure, we have to test 2 parameter restrictions, which are simple linear parameter restrictions, so we implemented an F test and Chi-square test for the sum of coefficients. The estimated factor price elasticity is 0.52 in the fixed effect model and 0.15 in the dynamic model. In the case of monopoly, the elasticity is nonpositive (H0:H ≤ 0). We reject the null hypothesis in the case of the fixed effect model, and we cannot reject it in the case of the dynamic model. That means that the revenue function does not depend on the decisions of the rivals, the dynamic model suggest high market concentration. In the long run competitive equilibrium, the elasticity is unique (H0:H = 1). We reject the null hypothesis in both models, so there is no perfect competition between 2003 and 2020 in Hungary. The results of the two tests show that the bank market could be a monopolistic competition, because the factor price elasticity is between 0 and 1.

This is true if the market is in long run equilibrium. To test this assumption we estimated the equation with the same control variables, but the dependent variable is the Return on Assets (ROA). In equilibrium the dependent variable should not be correlated with the input prices. Thus, we need to test the same hypothesis, for which we cannot reject that the input price elasticity is zero (H0:E = 0), that the market is in long run equilibrium between 2003 and 2020 (Table  4 ).

To determine the effect of the financial crisis we build three subsamples (2003–2007, 2008–2012, 2013–2020) and estimate the market structure in the same way. Because of the lower number of observations, we used the static panel model approach, so a fixed effect model was fitted. To be able to compare the results the estimated equations contain all the control variables. The results are presented in Table  5 .

We tested the hypothesis of the long run equilibrium in every subperiod, see the results in Table  6 .

Before the financial crisis (2003–2007) the Hungarian bank sector’s market structure significantly differs from perfect competition (H0:H = 1). The value of H-statistic is − 0.29. The hypothesis of the monopoly market (H0:H = 0) cannot be rejected on any usual significance levels, where the companies’ decisions are not influenced by the other companies of the sector.

In the years of the crisis (2008–2012) we get similar results, we cannot reject the hypothesis of monopoly and reject the perfect competition. The H-statistic (0.23) is significantly different from one (H = 1), but not significantly differs from zero (H = 0). It has a positive value, so it also allows the case of monopolistic competition. However, the hypothesis of monopolistic competition holds only if the market is in long run equilibrium. During this time-period the market is not in long run equilibrium according to the test in Table  6 .

The results of the post-crisis period (2013–2020) are not straightforward. The market is in long run equilibrium, but the factor price elasticity is 0.99. We cannot reject either of the two hypotheses.

6 Discussion

We analyze the market structure of the Hungarian bank sector. This is an important factor of the competitiveness analysis of the sector. However, measuring the competitiveness is a much more complex problem. Ábel and Polivka dealt with this question in 1997 (Ábel and Polivka 1997 ). They focused on the development of the sector after the regime change, the transaction costs, and the foreign-owned companies. Mester et al. ( 2017 ) mentioned that predictable taxation and the regulatory system are important features of competitiveness in the current banking system. Thus, the strong policy influences (state support for credit and extra taxation of banks) also have a great effect on the sector. The Hungarian National Bank developed two competitiveness indicators, which are based on 14 basic pillars to measure the competitiveness of the bank sector (Baksay and Horváth 2017 ). The indicators contain macroeconomic aspects (tax environment) as well as the profitability, so the factor price elasticity can be an important factor.

There are several structural and non-structural models to determine the market structure of a sector. Comparing the results of some of these methods could serve as a sense of robustness test. The structural approach includes the different market hypotheses, i.e., structure-conduct-performance paradigm, efficiency hypothesis and the concentration ratios: the k bank Concentration Ratio (CRk), the Herfindahl-Hirschman Index (HHI), the Hall-Tideman Index, the Rosenbluth Index, the Comprehensive Industrial Concentration Index, the Entropy measure, the Hannah and Kay Index (HKI), and the U Index (U) (Bikker and Haaf 2002b ). As non-structural models, the Iwata model ( 1974 ), Bresnahan model ( 1982 ), and Panzar and Rosse model ( 1987 ) are often used. The first two methods are based on the profit maximizing problem of the oligopolies. The Panzar and Rosse model uses the comparative static properties of the reduced-form revenue approach. The Bresnahan and Iwata model is rarely applied in the case of bank market analysis (Bikker and Haaf 2002b ), whereas the Panzar and Rosse method is widely used, see Table  1 . It is also used in connection with other markets, for instance the Hungarian insurance market (Varga and Madari 2023 ). The necessary data to carry out the analysis is relatively small, it is often used with small sample size in the literature, as we presented earlier.

Nevertheless, there are also some weaknesses of this model. The different size of the firms can cause problems (Bikker et al. 2012 ), Shaffer and Spierdijk ( 2015 ) showed that the H statistic can be negative or positive at any level of competition, and it was also suggested that the value of the statistic could be presented as a pass-through rate not a market power measure (Sanchez-Cartas 2020 ). We built in the further critics about the empirical application of the Panzar and Rosse model to the analysis. According to Goddard and Wilson ( 2009 ) the dynamic panel model estimation should be used, and Bikker et al. ( 2012 ) shows that only an unscaled revenue equation gives unbiased estimation, thus the model does not contain the total asset as a control variable.

According to Andrade ( 2017 ), studying the European Union countries together between 2004 and 2011, all countries had monopolistic competition. However, the operation of the bank market, especially in the household sector, is country specific so it is worth examining the countries individually. Drakos and Konstantinou ( 2005 ) estimate H statistic between 1992 and 2000 with fixed effects estimation and total assets is used as a control variable, they calculate that the market is under monopolistic competition. According to our dynamic and unscaled estimation the hypothesis of the monopoly market in Hungary between 2003 and 2020 cannot be rejected.

It is worth noting that the effect of the crisis in the most studied countries decreased the level of H statistics, i.e., the EU15 and EU27 average (Weill 2013 ) and in emerging markets (Apergis 2015 ). But in Hungary the H value increased between 2008 and 2009 from 0.65 to 0.77 (Weill 2013 ). We get a similar result with panel estimation. Between 2003 and 2007 the H static was negative (− 0.29), the monopoly market hypothesis cannot be rejected. During the years of the crisis (2008–2012) the competition increased (H = 0.23) and during the recovery period (2013–2020) the H value was 0.99. The Hungarian market tends to the perfect competition in the analyzed years.

The Panzar and Rosse method focuses on the whole market, but it would be interesting to study the household and corporation credit market separately. It is a statistical estimation, thus, it can contain some bias, although suggestions for improvement have been taken into account and the observations cover a large part of the market.

7 Conclusion

We examined the effect of the financial crisis of 2008 on the market structure of the Hungarian bank sector. Between 2003 and 2020 the H-statistics increased. Before and during the years of the crisis the market was a monopoly market. The market was not in long run equilibrium so further conclusions cannot be made. Extending the results of the papers that studied similar questions in this area we estimated H-statistic with a dynamic panel estimation in the whole time-period. Based on the Panzar and Rosse methodology we tested the hypothesis of the monopoly and the perfect competition markets, and we rejected the hypothesis of the perfect competition. According to the dynamic model we cannot reject the monopoly case. This research has shown that the Hungarian bank sector is a monopolistic competition market or a monopoly in long run equilibrium between 2003 and 2020.

The results are in some sense similar to the ones gained in previous research in the area with fixed effect estimation. Contrary to the result of Apergis ( 2015 ), who shows the decrease in the level of competition in emerging markets, the value of H-statistics in Hungary is constantly increasing during the examined three time periods. The main limitation of this study was the paucity of sample size, greater efforts are needed to extend the number of observations or to carry out the investigation on a regional level. Further control variables could also improve the results, using other revenues from fees and commissions for instance. Studying the impact of digital banking trends on the market structure can be also an interesting further research question.

Bank of China, Budapest Bank, CIB Bank, Commerzbank, ERSTE Bank, Kereskedelmi és Hitelbank, Magyar Cetelem Bank, Merkantil Váltó és Vagyonbefektető Bank, MKB Bank, OTP Bank, Raiffeisen Bank, Sberbank Magyarország, UniCredit Bank.

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Acknowledgements

Supported by the ÚNKP-21-3 New National Excellence Program of the Ministry for Innovation and Technology from the source of the National Research, Development and Innovation Fund.

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Institute of Operations and Decision Sciences, Corvinus University of Budapest, Budapest, Hungary

Veronika Szádoczkiné Varga

Institute of Data Analytics and Information Systems, Corvinus University of Budapest, Budapest, Hungary

Zoltán Madari

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Correspondence to Veronika Szádoczkiné Varga .

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Szádoczkiné Varga, V., Madari, Z. Empirical analysis of the market structure of the Hungarian bank market. Cent Eur J Oper Res (2024). https://doi.org/10.1007/s10100-024-00916-1

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Accepted : 05 April 2024

Published : 27 April 2024

DOI : https://doi.org/10.1007/s10100-024-00916-1

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