Identify
Explore
Discover
Discuss
Summarise
Describe
Last, format your objectives into a numbered list. This is because when you write your thesis or dissertation, you will at times need to make reference to a specific research objective; structuring your research objectives in a numbered list will provide a clear way of doing this.
To bring all this together, let’s compare the first research objective in the previous example with the above guidance:
Research Objective:
1. Develop finite element models using explicit dynamics to mimic mallet blows during cup/shell insertion, initially using simplified experimentally validated foam models to represent the acetabulum.
Checking Against Recommended Approach:
Q: Is it specific? A: Yes, it is clear what the student intends to do (produce a finite element model), why they intend to do it (mimic cup/shell blows) and their parameters have been well-defined ( using simplified experimentally validated foam models to represent the acetabulum ).
Q: Is it measurable? A: Yes, it is clear that the research objective will be achieved once the finite element model is complete.
Q: Is it achievable? A: Yes, provided the student has access to a computer lab, modelling software and laboratory data.
Q: Is it relevant? A: Yes, mimicking impacts to a cup/shell is fundamental to the overall aim of understanding how they deform when impacted upon.
Q: Is it timebound? A: Yes, it is possible to create a limited-scope finite element model in a relatively short time, especially if you already have experience in modelling.
Q: Does it start with a verb? A: Yes, it starts with ‘develop’, which makes the intent of the objective immediately clear.
Q: Is it a numbered list? A: Yes, it is the first research objective in a list of eight.
1. making your research aim too broad.
Having a research aim too broad becomes very difficult to achieve. Normally, this occurs when a student develops their research aim before they have a good understanding of what they want to research. Remember that at the end of your project and during your viva defence , you will have to prove that you have achieved your research aims; if they are too broad, this will be an almost impossible task. In the early stages of your research project, your priority should be to narrow your study to a specific area. A good way to do this is to take the time to study existing literature, question their current approaches, findings and limitations, and consider whether there are any recurring gaps that could be investigated .
Note: Achieving a set of aims does not necessarily mean proving or disproving a theory or hypothesis, even if your research aim was to, but having done enough work to provide a useful and original insight into the principles that underlie your research aim.
Be realistic about what you can achieve in the time you have available. It is natural to want to set ambitious research objectives that require sophisticated data collection and analysis, but only completing this with six months before the end of your PhD registration period is not a worthwhile trade-off.
Each research objective should have its own purpose and distinct measurable outcome. To this effect, a common mistake is to form research objectives which have large amounts of overlap. This makes it difficult to determine when an objective is truly complete, and also presents challenges in estimating the duration of objectives when creating your project timeline. It also makes it difficult to structure your thesis into unique chapters, making it more challenging for you to write and for your audience to read.
Fortunately, this oversight can be easily avoided by using SMART objectives.
Hopefully, you now have a good idea of how to create an effective set of aims and objectives for your research project, whether it be a thesis, dissertation or research paper. While it may be tempting to dive directly into your research, spending time on getting your aims and objectives right will give your research clear direction. This won’t only reduce the likelihood of problems arising later down the line, but will also lead to a more thorough and coherent research project.
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Home » Research Paper – Structure, Examples and Writing Guide
Table of Contents
Definition:
Research Paper is a written document that presents the author’s original research, analysis, and interpretation of a specific topic or issue.
It is typically based on Empirical Evidence, and may involve qualitative or quantitative research methods, or a combination of both. The purpose of a research paper is to contribute new knowledge or insights to a particular field of study, and to demonstrate the author’s understanding of the existing literature and theories related to the topic.
The structure of a research paper typically follows a standard format, consisting of several sections that convey specific information about the research study. The following is a detailed explanation of the structure of a research paper:
The title page contains the title of the paper, the name(s) of the author(s), and the affiliation(s) of the author(s). It also includes the date of submission and possibly, the name of the journal or conference where the paper is to be published.
The abstract is a brief summary of the research paper, typically ranging from 100 to 250 words. It should include the research question, the methods used, the key findings, and the implications of the results. The abstract should be written in a concise and clear manner to allow readers to quickly grasp the essence of the research.
The introduction section of a research paper provides background information about the research problem, the research question, and the research objectives. It also outlines the significance of the research, the research gap that it aims to fill, and the approach taken to address the research question. Finally, the introduction section ends with a clear statement of the research hypothesis or research question.
The literature review section of a research paper provides an overview of the existing literature on the topic of study. It includes a critical analysis and synthesis of the literature, highlighting the key concepts, themes, and debates. The literature review should also demonstrate the research gap and how the current study seeks to address it.
The methods section of a research paper describes the research design, the sample selection, the data collection and analysis procedures, and the statistical methods used to analyze the data. This section should provide sufficient detail for other researchers to replicate the study.
The results section presents the findings of the research, using tables, graphs, and figures to illustrate the data. The findings should be presented in a clear and concise manner, with reference to the research question and hypothesis.
The discussion section of a research paper interprets the findings and discusses their implications for the research question, the literature review, and the field of study. It should also address the limitations of the study and suggest future research directions.
The conclusion section summarizes the main findings of the study, restates the research question and hypothesis, and provides a final reflection on the significance of the research.
The references section provides a list of all the sources cited in the paper, following a specific citation style such as APA, MLA or Chicago.
You can write Research Paper by the following guide:
Note : The below example research paper is for illustrative purposes only and is not an actual research paper. Actual research papers may have different structures, contents, and formats depending on the field of study, research question, data collection and analysis methods, and other factors. Students should always consult with their professors or supervisors for specific guidelines and expectations for their research papers.
Research Paper Example sample for Students:
Title: The Impact of Social Media on Mental Health among Young Adults
Abstract: This study aims to investigate the impact of social media use on the mental health of young adults. A literature review was conducted to examine the existing research on the topic. A survey was then administered to 200 university students to collect data on their social media use, mental health status, and perceived impact of social media on their mental health. The results showed that social media use is positively associated with depression, anxiety, and stress. The study also found that social comparison, cyberbullying, and FOMO (Fear of Missing Out) are significant predictors of mental health problems among young adults.
Introduction: Social media has become an integral part of modern life, particularly among young adults. While social media has many benefits, including increased communication and social connectivity, it has also been associated with negative outcomes, such as addiction, cyberbullying, and mental health problems. This study aims to investigate the impact of social media use on the mental health of young adults.
Literature Review: The literature review highlights the existing research on the impact of social media use on mental health. The review shows that social media use is associated with depression, anxiety, stress, and other mental health problems. The review also identifies the factors that contribute to the negative impact of social media, including social comparison, cyberbullying, and FOMO.
Methods : A survey was administered to 200 university students to collect data on their social media use, mental health status, and perceived impact of social media on their mental health. The survey included questions on social media use, mental health status (measured using the DASS-21), and perceived impact of social media on their mental health. Data were analyzed using descriptive statistics and regression analysis.
Results : The results showed that social media use is positively associated with depression, anxiety, and stress. The study also found that social comparison, cyberbullying, and FOMO are significant predictors of mental health problems among young adults.
Discussion : The study’s findings suggest that social media use has a negative impact on the mental health of young adults. The study highlights the need for interventions that address the factors contributing to the negative impact of social media, such as social comparison, cyberbullying, and FOMO.
Conclusion : In conclusion, social media use has a significant impact on the mental health of young adults. The study’s findings underscore the need for interventions that promote healthy social media use and address the negative outcomes associated with social media use. Future research can explore the effectiveness of interventions aimed at reducing the negative impact of social media on mental health. Additionally, longitudinal studies can investigate the long-term effects of social media use on mental health.
Limitations : The study has some limitations, including the use of self-report measures and a cross-sectional design. The use of self-report measures may result in biased responses, and a cross-sectional design limits the ability to establish causality.
Implications: The study’s findings have implications for mental health professionals, educators, and policymakers. Mental health professionals can use the findings to develop interventions that address the negative impact of social media use on mental health. Educators can incorporate social media literacy into their curriculum to promote healthy social media use among young adults. Policymakers can use the findings to develop policies that protect young adults from the negative outcomes associated with social media use.
References :
Appendix : The survey used in this study is provided below.
Social Media and Mental Health Survey
Thank you for your participation!
Research papers have several applications in various fields, including:
Research papers are typically written when a person has completed a research project or when they have conducted a study and have obtained data or findings that they want to share with the academic or professional community. Research papers are usually written in academic settings, such as universities, but they can also be written in professional settings, such as research organizations, government agencies, or private companies.
Here are some common situations where a person might need to write a research paper:
The purpose of a research paper is to present the results of a study or investigation in a clear, concise, and structured manner. Research papers are written to communicate new knowledge, ideas, or findings to a specific audience, such as researchers, scholars, practitioners, or policymakers. The primary purposes of a research paper are:
Research papers have several characteristics that distinguish them from other forms of academic or professional writing. Here are some common characteristics of research papers:
Research papers have many advantages, both for the individual researcher and for the broader academic and professional community. Here are some advantages of research papers:
Research papers also have some limitations that should be considered when interpreting their findings or implications. Here are some common limitations of research papers:
Researcher, Academic Writer, Web developer
Introduction.
Research objectives play a crucial role in any research study. They provide a clear direction and purpose for the research, guiding the researcher in their investigation. Understanding research objectives is essential for conducting a successful study and achieving meaningful results.
In this comprehensive review, we will delve into the definition of research objectives, exploring their characteristics, types, and examples. We will also discuss the relationship between research objectives and research questions, as well as provide insights into how to write effective research objectives. Additionally, we will examine the role of research objectives in research methodology and highlight the importance of them in a study. By the end of this review, you will have a comprehensive understanding of research objectives and their significance in the research process.
A research objective is defined as a clear and concise statement that outlines the specific goals and aims of a research study. These objectives are designed to be specific, measurable, achievable, relevant, and time-bound (SMART), ensuring they provide a structured pathway to accomplishing the intended outcomes of the project. Each objective serves as a foundational element that summarizes the purpose of your study, guiding the research activities and helping to measure progress toward the study’s goals. Additionally, research objectives are integral components of the research framework , establishing a clear direction that aligns with the overall research questions and hypotheses. This alignment helps to ensure that the study remains focused and relevant, facilitating the systematic collection, analysis, and interpretation of data.
Characteristics of research objectives include:
By incorporating these characteristics into research objectives, researchers can ensure that their study is focused, achievable, and contributes to the body of knowledge in their field.
Research objective can be broadly classified into general and specific objectives. General objectives are broad statements that define the overall purpose of the research. They provide a broad direction for the study and help in setting the context. Specific objectives, on the other hand, are detailed objectives that describe what will be researched during the study. They are more focused and provide specific outcomes that the researcher aims to achieve. Specific objectives are derived from the general objectives and help in breaking down the research into smaller, manageable parts. The specific objectives should be clear, measurable, and achievable. They should be designed in a way that allows the researcher to answer the research questions and address the research problem.
In addition to general and specific objectives, research objective can also be categorized as descriptive or analytical objectives. Descriptive objectives focus on describing the characteristics or phenomena of a particular subject or population. They involve surveys, observations, and data collection to provide a detailed understanding of the subject. Analytical objectives, on the other hand, aim to analyze the relationships between variables or factors. They involve data analysis and interpretation to gain insights and draw conclusions.
Both descriptive and analytical objectives are important in research as they serve different purposes and contribute to a comprehensive understanding of the research topic.
Here are some examples of research objectives in different fields:
1. Objective: To identify key characteristics and styles of Renaissance art.
This objective focuses on exploring the characteristics and styles of art during the Renaissance period. The research may involve analyzing various artworks, studying historical documents, and interviewing experts in the field.
2. Objective: To analyze modern art trends and their impact on society.
This objective aims to examine the current trends in modern art and understand how they influence society. The research may involve analyzing artworks, conducting surveys or interviews with artists and art enthusiasts, and studying the social and cultural implications of modern art.
3. Objective: To investigate the effects of exercise on mental health.
This objective focuses on studying the relationship between exercise and mental health. The research may involve conducting experiments or surveys to assess the impact of exercise on factors such as stress, anxiety, and depression.
4. Objective: To explore the factors influencing consumer purchasing decisions in the fashion industry.
This objective aims to understand the various factors that influence consumers’ purchasing decisions in the fashion industry. The research may involve conducting surveys, analyzing consumer behavior data, and studying the impact of marketing strategies on consumer choices.
5. Objective: To examine the effectiveness of a new drug in treating a specific medical condition.
This objective focuses on evaluating the effectiveness of a newly developed drug in treating a particular medical condition. The research may involve conducting clinical trials, analyzing patient data, and comparing the outcomes of the new drug with existing treatment options.
These examples demonstrate the diversity of research objectives across different disciplines. Each objective is specific, measurable, and achievable, providing a clear direction for the research study.
Research objectives and research questions are essential components of a research project. Research objective describe what you intend your research project to accomplish. They summarize the approach and purpose of the project and provide a clear direction for the research. Research questions, on the other hand, are the starting point of any good research. They guide the overall direction of the research and help identify and focus on the research gaps .
The main difference between research questions and objectives is their form. Research questions are stated in a question form, while objectives are specific, measurable, and achievable goals that you aim to accomplish within a specified timeframe. Research questions are broad statements that provide a roadmap for the research, while objectives break down the research aim into smaller, actionable steps.
Research objectives and research questions work together to form the ‘golden thread’ of a research project. The research aim specifies what the study will answer, while the objectives and questions specify how the study will answer it. They provide a clear focus and scope for the research project, helping researchers stay on track and ensure that their study is meaningful and relevant.
When writing research objectives and questions, it is important to be clear, concise, and specific. Each objective or question should address a specific aspect of the research and contribute to the overall goal of the study. They should also be measurable, meaning that their achievement can be assessed and evaluated. Additionally, research objectives and questions should be achievable within the given timeframe and resources of the research project. By clearly defining the objectives and questions, researchers can effectively plan and execute their research, leading to valuable insights and contributions to the field.
Writing research objective is a crucial step in any research project. The objectives provide a clear direction and purpose for the study, guiding the researcher in their data collection and analysis. Here are some tips on how to write effective research objective:
1. Be clear and specific
Research objective should be written in a clear and specific manner. Avoid vague or ambiguous language that can lead to confusion. Clearly state what you intend to achieve through your research.
2. Use action verbs
Start your research objective with action verbs that describe the desired outcome. Action verbs such as ‘investigate’, ‘analyze’, ‘compare’, ‘evaluate’, or ‘identify’ help to convey the purpose of the study.
3. Align with research questions or hypotheses
Ensure that your research objectives are aligned with your research questions or hypotheses. The objectives should address the main goals of your study and provide a framework for answering your research questions or testing your hypotheses.
4. Be realistic and achievable
Set research objectives that are realistic and achievable within the scope of your study. Consider the available resources, time constraints, and feasibility of your objectives. Unrealistic objectives can lead to frustration and hinder the progress of your research.
5. Consider the significance and relevance
Reflect on the significance and relevance of your research objectives. How will achieving these objectives contribute to the existing knowledge or address a gap in the literature? Ensure that your objectives have a clear purpose and value.
6. Seek feedback
It is beneficial to seek feedback on your research objectives from colleagues, mentors, or experts in your field. They can provide valuable insights and suggestions for improving the clarity and effectiveness of your objectives.
7. Revise and refine
Research objectives are not set in stone. As you progress in your research, you may need to revise and refine your objectives to align with new findings or changes in the research context. Regularly review and update your objectives to ensure they remain relevant and focused.
By following these tips, you can write research objectives that are clear, focused, and aligned with your research goals. Well-defined objectives will guide your research process and help you achieve meaningful outcomes.
Research objectives play a crucial role in the research methodology . In research methodology, research objectives are formulated based on the research questions or problem statement. These objectives help in defining the scope and focus of the study, ensuring that the research is conducted in a systematic and organized manner.
The research objectives in research methodology act as a roadmap for the research project. They help in identifying the key variables to be studied, determining the research design and methodology, and selecting the appropriate data collection methods .
Furthermore, research objectives in research methodology assist in evaluating the success of the study. By setting clear objectives, researchers can assess whether the desired outcomes have been achieved and determine the effectiveness of the research methods employed. It is important to note that research objectives in research methodology should be aligned with the overall research aim. They should address the specific aspects or components of the research aim and provide a framework for achieving the desired outcomes.
The research objectives of a study play a crucial role in guiding the research process, ensuring that the study is focused, purposeful, and contributes to the advancement of knowledge in the field. It is important to note that the research objectives may evolve or change as the study progresses. As new information is gathered and analyzed, the researcher may need to revise the objectives to ensure that they remain relevant and achievable.
In summary, research objectives are essential components in writing an effective research paper . They provide a roadmap for the research process, guiding the researcher in their investigation and helping to ensure that the study is purposeful and meaningful. By understanding and effectively utilizing research objectives, researchers can enhance the quality and impact of their research endeavors.
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Writing engaging introduction in research papers : tips and tricks.
The research objective of a research proposal or scientific article defines the direction or content of a research investigation. Without the research objectives, the proposal or research paper is in disarray. It is like a fisherman riding on a boat without any purpose and with no destination in sight. Therefore, at the beginning of any research venture, the researcher must be clear about what he or she intends to do or achieve in conducting a study.
How do you define the objectives of a study? What are the uses of the research objective? How would a researcher write this essential part of the research? This article aims to provide answers to these questions.
Definition of a research objective.
A research objective describes, in a few words, the result of the research project after its implementation. It answers the question,
The research objective provides direction to the performance of the study.
The uses of the research objective are enumerated below:
The research design serves as the “blueprint” for the research investigation. The University of Southern California describes the different types of research design extensively. It details the data to be gathered, data collection procedure, data measurement, and statistical tests to use in the analysis.
The variables of the study include those factors that the researcher wants to evaluate in the study. These variables narrow down the research to several manageable components to see differences or correlations between them.
Specifying the data collection procedure ensures data accuracy and integrity . Thus, the probability of error is minimized. Generalizations or conclusions based on valid arguments founded on reliable data strengthens research findings on particular issues and problems.
In data mining activities where large data sets are involved, the research objective plays a crucial role. Without a clear objective to guide the machine learning process, the desired outcomes will not be met.
A research objective must be achievable, i.e., it must be framed keeping in mind the available time, infrastructure required for research, and other resources.
Before forming a research objective, you should read about all the developments in your area of research and find gaps in knowledge that need to be addressed. Readings will help you come up with suitable objectives for your research project.
The following examples of research objectives based on several published studies on various topics demonstrate how the research objectives are written:
Finally, writing the research objectives requires constant practice, experience, and knowledge about the topic investigated. Clearly written objectives save time, money, and effort.
Evans, K. L., Rodrigues, A. S., Chown, S. L., & Gaston, K. J. (2006). Protected areas and regional avian species richness in South Africa. Biology letters , 2 (2), 184-188.
Yeemin, T., Sutthacheep, M., & Pettongma, R. (2006). Coral reef restoration projects in Thailand. Ocean & Coastal Management , 49 (9-10), 562-575.
© 2020 March 23 P. A. Regoniel Updated 17 November 2020 | Updated 18 January 2024
5 tips on how to make a mind map for research purposes, the pinnacle of knowledge: an in-depth look at google scholar, what is market research, about the author, patrick regoniel.
Dr. Regoniel, a hobbyist writer, served as consultant to various environmental research and development projects covering issues and concerns on climate change, coral reef resources and management, economic valuation of environmental and natural resources, mining, and waste management and pollution. He has extensive experience on applied statistics, systems modelling and analysis, an avid practitioner of LaTeX, and a multidisciplinary web developer. He leverages pioneering AI-powered content creation tools to produce unique and comprehensive articles in this website.
Last Updated: April 30, 2024 Fact Checked
This article was co-authored by Marissa Levis . Marissa Levis is an English Teacher in the Morris County Vocational School District. She previously worked as an English director at a tutoring center that caters to students in elementary and middle school. She is an expert in creating a curriculum that helps students advance their skills in secondary-level English, focusing on MLA formatting, reading comprehension, writing skills, editing and proofreading, literary analysis, standardized test preparation, and journalism topics. Marissa received her Master of Arts in Teaching from Fairleigh Dickinson University. There are 9 references cited in this article, which can be found at the bottom of the page. This article has been fact-checked, ensuring the accuracy of any cited facts and confirming the authority of its sources. This article has been viewed 128,800 times.
A research proposal is a detailed outline for a significant research project. They’re common for class assignments, capstone papers, grant applications, and even job applications in some fields, so it's possible you'll have to prepare one at some point. The objectives are a very important part of a research proposal because they outline where the project is headed and what it will accomplish. Developing objectives can be a little tricky, so take some time to consider them. Then work on wording them carefully so your readers understand your goals. With clear objectives, your research proposal will be much stronger.
Aug 9, 2022
Mar 20, 2023
Relyn Jungco
May 15, 2022
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Table of Contents
Before conducting a study, a research proposal should be created that outlines researchers’ plans and methodology and is submitted to the concerned evaluating organization or person. Creating a research proposal is an important step to ensure that researchers are on track and are moving forward as intended. A research proposal can be defined as a detailed plan or blueprint for the proposed research that you intend to undertake. It provides readers with a snapshot of your project by describing what you will investigate, why it is needed, and how you will conduct the research.
Your research proposal should aim to explain to the readers why your research is relevant and original, that you understand the context and current scenario in the field, have the appropriate resources to conduct the research, and that the research is feasible given the usual constraints.
This article will describe in detail the purpose and typical structure of a research proposal , along with examples and templates to help you ace this step in your research journey.
A research proposal¹ ,² can be defined as a formal report that describes your proposed research, its objectives, methodology, implications, and other important details. Research proposals are the framework of your research and are used to obtain approvals or grants to conduct the study from various committees or organizations. Consequently, research proposals should convince readers of your study’s credibility, accuracy, achievability, practicality, and reproducibility.
With research proposals , researchers usually aim to persuade the readers, funding agencies, educational institutions, and supervisors to approve the proposal. To achieve this, the report should be well structured with the objectives written in clear, understandable language devoid of jargon. A well-organized research proposal conveys to the readers or evaluators that the writer has thought out the research plan meticulously and has the resources to ensure timely completion.
A research proposal is a sales pitch and therefore should be detailed enough to convince your readers, who could be supervisors, ethics committees, universities, etc., that what you’re proposing has merit and is feasible . Research proposals can help students discuss their dissertation with their faculty or fulfill course requirements and also help researchers obtain funding. A well-structured proposal instills confidence among readers about your ability to conduct and complete the study as proposed.
Research proposals can be written for several reasons:³
Research proposals should aim to answer the three basic questions—what, why, and how.
The What question should be answered by describing the specific subject being researched. It should typically include the objectives, the cohort details, and the location or setting.
The Why question should be answered by describing the existing scenario of the subject, listing unanswered questions, identifying gaps in the existing research, and describing how your study can address these gaps, along with the implications and significance.
The How question should be answered by describing the proposed research methodology, data analysis tools expected to be used, and other details to describe your proposed methodology.
Here is a research proposal sample template (with examples) from the University of Rochester Medical Center. 4 The sections in all research proposals are essentially the same although different terminology and other specific sections may be used depending on the subject.
If you want to know how to make a research proposal impactful, include the following components:¹
1. Introduction
This section provides a background of the study, including the research topic, what is already known about it and the gaps, and the significance of the proposed research.
2. Literature review
This section contains descriptions of all the previous relevant studies pertaining to the research topic. Every study cited should be described in a few sentences, starting with the general studies to the more specific ones. This section builds on the understanding gained by readers in the Introduction section and supports it by citing relevant prior literature, indicating to readers that you have thoroughly researched your subject.
3. Objectives
Once the background and gaps in the research topic have been established, authors must now state the aims of the research clearly. Hypotheses should be mentioned here. This section further helps readers understand what your study’s specific goals are.
4. Research design and methodology
Here, authors should clearly describe the methods they intend to use to achieve their proposed objectives. Important components of this section include the population and sample size, data collection and analysis methods and duration, statistical analysis software, measures to avoid bias (randomization, blinding), etc.
5. Ethical considerations
This refers to the protection of participants’ rights, such as the right to privacy, right to confidentiality, etc. Researchers need to obtain informed consent and institutional review approval by the required authorities and mention this clearly for transparency.
6. Budget/funding
Researchers should prepare their budget and include all expected expenditures. An additional allowance for contingencies such as delays should also be factored in.
7. Appendices
This section typically includes information that supports the research proposal and may include informed consent forms, questionnaires, participant information, measurement tools, etc.
8. Citations
Writing a research proposal begins much before the actual task of writing. Planning the research proposal structure and content is an important stage, which if done efficiently, can help you seamlessly transition into the writing stage. 3,5
Key Takeaways
Here’s a summary of the main points about research proposals discussed in the previous sections:
Q1. How is a research proposal evaluated?
A1. In general, most evaluators, including universities, broadly use the following criteria to evaluate research proposals . 6
Q2. What is the difference between the Introduction and Literature Review sections in a research proposal ?
A2. The Introduction or Background section in a research proposal sets the context of the study by describing the current scenario of the subject and identifying the gaps and need for the research. A Literature Review, on the other hand, provides references to all prior relevant literature to help corroborate the gaps identified and the research need.
Q3. How long should a research proposal be?
A3. Research proposal lengths vary with the evaluating authority like universities or committees and also the subject. Here’s a table that lists the typical research proposal lengths for a few universities.
Arts programs | 1,000-1,500 | |
University of Birmingham | Law School programs | 2,500 |
PhD | 2,500 | |
2,000 | ||
Research degrees | 2,000-3,500 |
Q4. What are the common mistakes to avoid in a research proposal ?
A4. Here are a few common mistakes that you must avoid while writing a research proposal . 7
Thus, a research proposal is an essential document that can help you promote your research and secure funds and grants for conducting your research. Consequently, it should be well written in clear language and include all essential details to convince the evaluators of your ability to conduct the research as proposed.
This article has described all the important components of a research proposal and has also provided tips to improve your writing style. We hope all these tips will help you write a well-structured research proposal to ensure receipt of grants or any other purpose.
References
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Published on September 24, 2022 by Jack Caulfield . Revised on March 27, 2023.
The introduction to a research paper is where you set up your topic and approach for the reader. It has several key goals:
The introduction looks slightly different depending on whether your paper presents the results of original empirical research or constructs an argument by engaging with a variety of sources.
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Step 1: introduce your topic, step 2: describe the background, step 3: establish your research problem, step 4: specify your objective(s), step 5: map out your paper, research paper introduction examples, frequently asked questions about the research paper introduction.
The first job of the introduction is to tell the reader what your topic is and why it’s interesting or important. This is generally accomplished with a strong opening hook.
The hook is a striking opening sentence that clearly conveys the relevance of your topic. Think of an interesting fact or statistic, a strong statement, a question, or a brief anecdote that will get the reader wondering about your topic.
For example, the following could be an effective hook for an argumentative paper about the environmental impact of cattle farming:
A more empirical paper investigating the relationship of Instagram use with body image issues in adolescent girls might use the following hook:
Don’t feel that your hook necessarily has to be deeply impressive or creative. Clarity and relevance are still more important than catchiness. The key thing is to guide the reader into your topic and situate your ideas.
The AI-powered Citation Checker helps you avoid common mistakes such as:
This part of the introduction differs depending on what approach your paper is taking.
In a more argumentative paper, you’ll explore some general background here. In a more empirical paper, this is the place to review previous research and establish how yours fits in.
After you’ve caught your reader’s attention, specify a bit more, providing context and narrowing down your topic.
Provide only the most relevant background information. The introduction isn’t the place to get too in-depth; if more background is essential to your paper, it can appear in the body .
For a paper describing original research, you’ll instead provide an overview of the most relevant research that has already been conducted. This is a sort of miniature literature review —a sketch of the current state of research into your topic, boiled down to a few sentences.
This should be informed by genuine engagement with the literature. Your search can be less extensive than in a full literature review, but a clear sense of the relevant research is crucial to inform your own work.
Begin by establishing the kinds of research that have been done, and end with limitations or gaps in the research that you intend to respond to.
The next step is to clarify how your own research fits in and what problem it addresses.
In an argumentative research paper, you can simply state the problem you intend to discuss, and what is original or important about your argument.
In an empirical research paper, try to lead into the problem on the basis of your discussion of the literature. Think in terms of these questions:
You can make the connection between your problem and the existing research using phrases like the following.
Although has been studied in detail, insufficient attention has been paid to . | You will address a previously overlooked aspect of your topic. |
The implications of study deserve to be explored further. | You will build on something suggested by a previous study, exploring it in greater depth. |
It is generally assumed that . However, this paper suggests that … | You will depart from the consensus on your topic, establishing a new position. |
Now you’ll get into the specifics of what you intend to find out or express in your research paper.
The way you frame your research objectives varies. An argumentative paper presents a thesis statement, while an empirical paper generally poses a research question (sometimes with a hypothesis as to the answer).
The thesis statement expresses the position that the rest of the paper will present evidence and arguments for. It can be presented in one or two sentences, and should state your position clearly and directly, without providing specific arguments for it at this point.
The research question is the question you want to answer in an empirical research paper.
Present your research question clearly and directly, with a minimum of discussion at this point. The rest of the paper will be taken up with discussing and investigating this question; here you just need to express it.
A research question can be framed either directly or indirectly.
If your research involved testing hypotheses , these should be stated along with your research question. They are usually presented in the past tense, since the hypothesis will already have been tested by the time you are writing up your paper.
For example, the following hypothesis might respond to the research question above:
The final part of the introduction is often dedicated to a brief overview of the rest of the paper.
In a paper structured using the standard scientific “introduction, methods, results, discussion” format, this isn’t always necessary. But if your paper is structured in a less predictable way, it’s important to describe the shape of it for the reader.
If included, the overview should be concise, direct, and written in the present tense.
Full examples of research paper introductions are shown in the tabs below: one for an argumentative paper, the other for an empirical paper.
Are cows responsible for climate change? A recent study (RIVM, 2019) shows that cattle farmers account for two thirds of agricultural nitrogen emissions in the Netherlands. These emissions result from nitrogen in manure, which can degrade into ammonia and enter the atmosphere. The study’s calculations show that agriculture is the main source of nitrogen pollution, accounting for 46% of the country’s total emissions. By comparison, road traffic and households are responsible for 6.1% each, the industrial sector for 1%. While efforts are being made to mitigate these emissions, policymakers are reluctant to reckon with the scale of the problem. The approach presented here is a radical one, but commensurate with the issue. This paper argues that the Dutch government must stimulate and subsidize livestock farmers, especially cattle farmers, to transition to sustainable vegetable farming. It first establishes the inadequacy of current mitigation measures, then discusses the various advantages of the results proposed, and finally addresses potential objections to the plan on economic grounds.
The rise of social media has been accompanied by a sharp increase in the prevalence of body image issues among women and girls. This correlation has received significant academic attention: Various empirical studies have been conducted into Facebook usage among adolescent girls (Tiggermann & Slater, 2013; Meier & Gray, 2014). These studies have consistently found that the visual and interactive aspects of the platform have the greatest influence on body image issues. Despite this, highly visual social media (HVSM) such as Instagram have yet to be robustly researched. This paper sets out to address this research gap. We investigated the effects of daily Instagram use on the prevalence of body image issues among adolescent girls. It was hypothesized that daily Instagram use would be associated with an increase in body image concerns and a decrease in self-esteem ratings.
The introduction of a research paper includes several key elements:
and your problem statement
Don’t feel that you have to write the introduction first. The introduction is often one of the last parts of the research paper you’ll write, along with the conclusion.
This is because it can be easier to introduce your paper once you’ve already written the body ; you may not have the clearest idea of your arguments until you’ve written them, and things can change during the writing process .
The way you present your research problem in your introduction varies depending on the nature of your research paper . A research paper that presents a sustained argument will usually encapsulate this argument in a thesis statement .
A research paper designed to present the results of empirical research tends to present a research question that it seeks to answer. It may also include a hypothesis —a prediction that will be confirmed or disproved by your research.
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Well-developed and focused research objectives go a long way in ensuring the success of a research project. The objectives even steer and shape the way the resulting research paper is structured . This article explains how to develop and frame your research objectives. Before that, let’s clarify the difference between research aims and objectives.
Let’s look at how you should move from the problem statement to the objectives. (See figure below.)
First, you come up with a problem statement describing the problem that needs solving (a controversy, knowledge gap , aspect that can be improved/enhanced, etc.), along with the rationale , which justifies the study.
You then formulate the central question of the study that is to be answered based on the study findings.
The research hypothesis is the formal prediction that is to be tested.
So, where do the objectives fit in?
The research objectives specify how the research question will be answered; in other words, the objectives determine what is to be measured to test the hypothesis .
Progression from a problem statement in a broad area of study to the development of the research objectives
Here is an example containing multiple research objectives, beginning with a problem statement.
Biodiversity losses are accelerating in the forests of northeast India. While measures are being taken by diverse players to stem these losses, there has been no formal study to quantify or compare the effects of the steps taken.
How do interventions by government research institutes compare with actions by indigenous people in managing plant biodiversity loss in the forests of Meghalaya, India?
Actions and interventions by indigenous people and local communities are more effective than interventions by government research institutes in plant genetic resource conservation in the forests of Meghalaya.
To investigate and compare the impact of the effectiveness of plant genetic resource conservation measures by research institutes and those by indigenous people and local communities in the forests of Meghalaya. To examine the ecological outcomes of specific conservation practices followed by local communities in Meghalaya. To identify specific plant species rescued by local communities in Meghalaya in the last few years.
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Eduardo Correa, Diego M Conti, David Gozal, Carlos O’Connor-Reina, Preventive medicine in OSA—A systematic review and a call to action, Sleep , 2024;, zsae164, https://doi.org/10.1093/sleep/zsae164
The purpose of this systematic review is to evaluate the modifiable risk factors associated with OSA and analyze extant publications solely focused on prevention of the disease.
Studies focused on prevention strategies for OSA and modifiable risk factors were eligible for inclusion. A detailed individual search strategy for each of the following bibliographic databases was developed: Cochrane, EMBASE, MEDLINE, PubMed and LILACS. The references cited in these articles were also crosschecked and a partial grey literature search was undertaken using Google Scholar. The methodology of selected studies was evaluated using the 14-item Quality Assessment Tool for Diagnostic Accuracy Studies.
Search resulted in 720 publications examining risk factors and prevention of OSA, as well as lifestyle modifications. Of these, a thorough assessment of the abstracts and content of each of these manuscripts led to the rejection of all but four papers, the latter being included in this systematic review. In contrast, a search regarding ‘Therapeutics’ showed that 23,674 articles on OSA were published, clearly illustrating the imbalance between the efforts in prevention and those focused on therapeutics.
Notwithstanding the importance and benefits of technological advances in medicine, consideration of the needs of people with OSA and its consequences prompts advocacy for the prevention of the disease. Thus, despite the economic interests that focus only on diagnosis and treatment, strategies preferentially aimed at overall avoidance of OSA emerge as a major priority. Thus, public and healthcare provider education, multidimensional prevention, and early diagnosis of OSA should be encouraged worldwide.
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Here's how to start using our objective generator for research:
In this article, you will find the definition of the research objective, its importance to the scientific paper, and practical tips that’ll help you to create a great one for your research.
A research objective is a crucial part of your paper that points to purpose of the study. Creating an effective research objective is a challenging task, even for experienced researchers.
Not sure how to do it?
No worries!
Our research objective generator can help you out.
Here are its main advantages:
🆓 No payments | No direct or hidden fees with this research objectives maker. |
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🚅 No registration | Start using the thesis objective generator right away! |
⭕ No limits | Generate as many research objectives as you need. |
🎯 Tunable | Just add the details about the purpose of the study. |
What is a research objective.
The research objective specifies how you are going to achieve your goal . It’s like a roadmap of your study that summarizes its aims, describes the results you expect to accomplish at the end, and helps you to keep track of your research.
The objective should always appear in the introduction of your paper or scientific article, right after the problem or thesis statement .
Find below the list of reasons why a research objective is important.
A good research objective is important because it:
To write a strong and clear research objective, follow our three-step guide.
It’s the first and the most crucial step. Begin with looking through the literature and studies in your area to determine the gaps in existing research . Then, consider how your project can fill these gaps and formulate the primary aim and objective.
So, you have the primary objective that states what you intend to achieve through your research. Now, it’s time to break it down. Think about what smaller steps you can take to attain your goal. These will be your specific, narrow objectives.
The SMART format will help you to achieve your goals by keeping them clear and easy to understand.
A SMART objective is:
Specific | Make your objectives precise and ensure that everything is clear. |
---|---|
Measurable | Ensure your objectives are clear. There should be a way to measure the results. |
Achievable | Be realistic with your goals and check whether you have the means and resources to achieve them. |
Relevant | Your objectives must be consistent with your overall research in order to keep you on track throughout the project. |
Time-based | Define a for the whole project. Then, select several smaller ones for the specific objectives to follow your progress. |
To formulate effective research objectives, follow these actionable tips:
Finally, let’s take a look at some practical examples and review them:
This research aims to analyze the reasons for teenage smoking, identify the change in the smoking rates among teenagers for the last year, and map out actionable solutions to address the issue.
This research aims to assess the influence of flipped classroom learning method on student scores and compare the scores of students exposed to flipped classroom learning method versus the direct instruction method.
This research aims to study the influence of deforestation on the animal species in the sample region in the past 5 years, evaluate the species extinction risk in the area, and propose practical solutions to the problem.
These objectives include strong action verbs (identify, analyze, assess, compare, map out, etc.), time limits, and measurable outcomes. Based on them, the researcher can decide on the methods to use and the variables to measure.
Now, you know everything to create a perfect objective for your research. Go for it! Or you can save your time and use our objective maker for research. It’ll generate a well-formulated research objective just in a few seconds!
If you need to write a thesis statement, try our thesis maker .
❓ how do you write a smart objective in research.
The first step to writing a SMART objective for your research is to determine your principal aim and consider how you will achieve it. Next, write down your objective and ensure it fits the following criteria: specific, measurable, achievable, relevant, and time-based.
A SMART research objective is specific, measurable, achievable, relevant, and time-based. It means that the objective must include a precise, realistic task that is concise with the rest of the research, suppose some definite results, and be finished by a specific time.
Research objectives show how you will address the aim of your research, for example: "This research aims to indicate the connection between a sedentary lifestyle and muscular atrophy, evaluate the health risks of a sedentary lifestyle, and map out practical solutions to the issue."
To check whether your goals are SMART, ask the following questions:
Updated: Jun 5th, 2024
Multi-rollers statics analysis of planetary roller screw mechanism based on explicit expression, effects of eccentric errors on sliding velocity and accumulative wear depth of planetary roller screw mechanism, three-dimensional parametric contact analysis of planetary roller screw mechanism and its application in grouping for selective assembly, incorporating gradient information into dimension perturbation mutation for high-dimensional expensive optimization, a study of precision grinding of micro-pitch internal thread for planetary roller screw mechanism, study on multi-level progressive optimization of planetary roller screw mechanism, time-varying contact characteristics of the planetary roller screw mechanism considering nonuniform surface waviness, an efficient method for designing high-performance planetary roller screw mechanism with low contact stress, surrogate-assisted moea/d for expensive constrained multi-objective optimization, uncertainty analysis and reliability improvement of planetary roller screw mechanism using active learning kriging model, related papers.
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Published on 30.7.2024 in Vol 26 (2024)
Authors of this article:
1 School of Information, Renmin University of China, Beijing, China
2 School of Smart Governance, Renmin University of China, Suzhou, China
Wei Xu, PhD
School of Information, Renmin University of China
59 Zhongguancun Street
Haidian District
Beijing, 100872
Phone: 86 01082500904
Email: [email protected]
Background: Online health communities have given rise to a new e-service known as online medical consultations (OMCs), enabling remote interactions between physicians and patients. To address challenges, such as patient information overload and uneven distribution of physician visits, online health communities should develop OMC-oriented recommenders.
Objective: We aimed to comprehensively investigate what paradigms lead to the success of OMC-oriented recommendations.
Methods: A literature search was conducted through e-databases, including PubMed, ACM Digital Library, Springer, and ScienceDirect, from January 2011 to December 2023. This review included all papers directly and indirectly related to the topic of health care–related recommendations for online services.
Results: The search identified 611 articles, of which 26 (4.3%) met the inclusion criteria. Despite the growing academic interest in OMC recommendations, there remains a lack of consensus among researchers on the definition of e-service–oriented recommenders. The discussion highlighted 3 key factors influencing recommender success: features, algorithms, and metrics. It advocated for moving beyond traditional e-commerce–oriented recommenders to establish an innovative theoretical framework for e-service–oriented recommenders and addresses critical technical issues regarding 2-sided personalized recommendations.
Conclusions: This review underscores the essence of e-services, particularly in knowledge- and labor-intensive domains such as OMCs, where patients seek interpretable recommendations due to their lack of domain knowledge and physicians must balance their energy levels to avoid overworking. Our study’s findings shed light on the importance of customizing e-service–oriented personalized recommendations to meet the distinct expectations of 2-sided users considering their cognitive abilities, decision-making perspectives, and preferences. To achieve this, a paradigm shift is essential to develop unique attributes and explore distinct content tailored for both parties involved.
Technology innovations have brought the medical industry into the digital, networked, and intelligent era of the medical internet. Combined with the impact of the pandemic, telemedicine increasingly prevails. A growing number of patients have started to seek contactless counseling from physicians through online health communities (OHCs). Meanwhile, physicians are providing the public with health care posts, free medical consultations, and even paid customized service [ 1 - 3 ]. HaoDF, one of the leading OHC platforms in China, has gathered >890,000 physicians from 10,000 hospitals across the country up to March 2023. It offers patients the service of telehealth or web-based live chat (ie, online medical consultation [OMC]). Telehealth offers greater convenience to patients than the in-person visits previously available. However, it worsens the problem of information overload, as there are too many candidates for users to choose from, which exacerbates the level of hesitation [ 4 ]. Patients face challenges in selecting suitable physicians due to limited medical knowledge and cognitive abilities. An OMC-oriented recommendation system is crucial to provide patients with professional, accurate, and responsible referrals, ensuring that they connect with qualified and suitable physicians.
Most existing studies of physician recommendations are in the wrong direction regardless of their diverse methodologies, such as collaborative filtering (CF), demographic statistics, or association rules. Previous research has overlooked the fact that OHCs serve both patients and physicians (ie, a 2-sided market scenario). Figure 1 illustrates an OHC jointly formed by patients and physicians. When a market is 2-sided, there are cross-network externalities, which means that the number of users on one side will affect the number of users on the other side and the overall transaction volume on both sides [ 5 ]. An OMC recommendation is a service that an OHC offers to both sides of users. A type of online service such as OMC (ie, an e-service) is an emerging field of internet business under the knowledge economy. As opposed to e-commerce, an e-service is composed of consultees and consultants rather than users and commodities. The offered item is an intangible service rather than a tangible one, but it has to meet the different needs, expectations, and preferences of 2-sided users. Furthermore, medical consultations are knowledge- and labor-intensive services that demand high levels of professionalism and energy investment [ 6 ]. The energy limits of physicians vary, and each physician can receive consultations only to a certain extent. In addition, patients lack the professional knowledge to distinguish the candidates, so they need recommendations that can be interpreted. Thus, it is impossible to transplant an e-commerce recommendation model to solve OMC recommendation cases. Research on recommendation systems suffers from a “blind side” that is the lack of research focusing on service-oriented applications, requiring academicians to develop new attributes and research new content. OMC services demonstrate the typical characteristics of online knowledge services, which represent the emerging trend of the “Internet+” economy. In the context of the knowledge economy, research on service recommendations is particularly pertinent, and now is an excellent time to start. As far as we know, no comprehensive research has been conducted in academic circles on service recommendations. Personalized service recommendation is a new topic yet to be clearly defined and fully explored.
Despite several reviews on health care recommender systems focusing on patient interests [ 7 - 9 ], there remains a gap in service-oriented recommendations. Our systematic review aimed to fill this void by concentrating solely on 2-sided recommendations. By providing the latest review of this domain, we aimed to gather comprehensive evidence for evaluating current studies, identifying successful paradigms and approaches in service-oriented recommendations, and informing public health interventions and policy making. This will leverage 2-sided recommendation technologies to enhance the well-being of both patients and physicians in the emerging OMC service industry.
This review was conducted according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines ( Multimedia Appendix 1 [ 10 ]) [ 11 ].
As the OMC service–oriented recommendation system spans multiple disciplines such as health care, business information systems, and computer science, the authors conducted separate literature searches in databases from each field. These included 1 medical-focused database (PubMed); 1 computer-focused database (ACM Digital Library); and 2 multidisciplinary full-text databases (SpringerLink and ScienceDirect) from 2 leading publishing groups, Springer and Elsevier, respectively. Our search was tailored to the review topic followed by an analysis of text words found in titles, abstracts, and keywords used in retrieved papers. The electronic search was conducted in December 2023 using keyword combinations in the title, abstract, and keywords fields to ensure comprehensive coverage. Keywords were selected and classified into 4 categories: OMC (subject of the study), recommendation (objective of the study), OHC (fields of the study), and excluded keywords—queries 1 to 4. To emphasize the recent advancements, an additional query 5 set a time limit from January 1, 2001, to November 1, 2023. The overall search strategy was 1 AND 2 AND 3 AND (NOT 4) AND 5. Table 1 presents the hierarchical search query and all keywords.
Following the keyword search, a reference list search (ie, backward reference search) and a cited reference search (ie, forward reference search) were conducted on the full-text articles that met the study selection criteria. Using the results of the backward and forward reference searches, the same study selection criteria were applied to further screen and evaluate articles. We repeated these procedures on all newly identified articles until no additional relevant articles were found.
Search number | Search | Keywords |
1 | Title | |
2 | Title | |
3 | Title/abstract/keywords | |
4 | Title/abstract/keywords | |
5 | Time range |
The titles and abstracts of identified articles were independently screened by 2 researchers (HJ and ZM) to determine inclusion in the full review. Figure 2 illustrates the paper selection process. If either or both reviewers selected the paper for further evaluation, it was included for full assessment. Articles were considered for analysis if they met at least one of the following criteria: (1) OHC-oriented physician recommendations, (2) coding or documenting of patient preferences, (3) motivations or perceptions of physicians involved in OHCs, (4) implementation of a recommender system for medical services, and (5) recommendation acceptance and interface design in the domain of medical-related recommender systems. Disagreements were resolved with a third reviewer (WX) until consensus was reached. In addition, articles must have met the following three criteria to be considered for analysis: (1) published in peer-reviewed journals or conference proceedings, excluding research articles without detailed research designs or results; (2) written in English; and (3) published between 2011 and 2023 to align with the recent emergence of OHCs over the last decade.
To ensure the quality of the articles, we applied a GRADE (Grading of Recommendations, Assessment, Development, and Evaluations) framework. The purpose of this initiative is to help individuals make informed decisions using evidence systematically and transparently [ 12 ]. The GRADE Evidence to Decision frameworks have been illustrated and are useful in making and using health-related recommendations and decisions [ 13 ]. They identify 4 levels of evidence for each study: very low, low, moderate, and high. GRADE criteria examine the risk of bias, imprecision, inconsistency, indirectness, and publication bias in evaluating the quality of evidence of a study [ 14 ]. In the scenario of OMC recommendation design, those studies start at high quality of evidence if they use both offline data sets and online data streams for randomized controlled experiments. In contrast, observational studies begin at a lower quality of evidence due to residual confounding. Referring to previous studies [ 15 ], only moderate and high-quality articles were selected to avoid low-quality articles.
The electronic database search yielded 599 studies, with an additional 12 studies identified from Google Scholar through reference list and cited reference searches for each obtained study. After removing book chapters and deduplicating entries, 49.3% (301/611) of the studies remained. The review process involved excluding 71.1% (214/301) of ineligible studies after screening titles and abstracts as they did not meet the criteria specific to the theme of physician recommendations, the OMC domain, peer-reviewed sources, or English language. This left 87 studies for full-text review, of which 5 (6%) reports were found to have no full texts and were subsequently removed. The full texts of the remaining 94% (82/87) of the studies were assessed for bias risks and qualitatively analyzed. Ultimately, 21 high-quality studies with adequate outcome data were selected for quantitative analysis. In addition, 5 studies were identified through citation searching and included in the quantitative analysis.
Table 2 summarizes the features and discoveries of each of the 26 studies. The number of publications increased over time. This indicates that this field is receiving more and more attention from scholars and practitioners due to the global prosperity of OHCs. This review comprised studies from 6 countries across Asia, Europe, and North America, most of which were low-income countries. The largest source of articles was China, followed by Portugal and India. This reflects that, in countries with less developed offline health networks, online medical services are of particular benefit, as illustrated by the growth of OMCs in China. There were various recommendation algorithms used: analytic hierarchy process, CF, content-based filtering, decision tree, neural network, matrix factorization, and regression analysis. Although research has been conducted using a variety of methods, CF, matrix factorization, and analytic hierarchy process are the top 3 most commonly used ones, with 38% (10/26) of the studies involving their use entirely or in part. However, over the last 5 years, the research methods of graph-based deep learning have become increasingly popular. The most widely used method of data engineering in the field is text analysis, such as latent Dirichlet allocation (LDA) and word2vec, followed by knowledge graphs (KGs).
Of the 26 studies included, 13 (50%) used data directly from OHCs as their source, whereas 7 (27%) used data indirectly from the official websites of hospitals or health care centers. Depending on the sources, the data sets could be classified as online or offline. There are numerous OHCs that produce massive amounts of heterogeneous, multimodal, and high-dimensional raw data continuously [ 16 , 17 ]. Online data generated by these OHCs support medical diagnosis and decision-making. Offline data are usually collected from various medical institutions and typically stored in health care information systems. They are exported once permission has been granted [ 18 - 22 ]. In addition, many studies collected primary data through questionnaires directed at patients or physicians [ 23 ]. Depending on the objects described, the data sets could be classified as patient, physician, and institution data, as shown in
Table 3 . The data quality of physician profiles, such as educational background, professional experience, disciplines, and expertise, was high and well defined. The patients were OHC users and service consumers, but they were nonexperts or laypersons in the medical field who usually chat on the web without any restrictions or limitations, which results in poor-quality data from their consultations. This makes data processing and feature extraction quite complicated and challenging.
Study | Date | Country | Study aim | Method | Data sources |
Huang et al [ ] | December 2012 | China | Using patient preferences and physician performance to recommend doctors | CF and AHP | Official appointment platform for the Shanghai Medical League |
Jiang and Xu [ ] | December 2014 | China | Combining the relevance and quality of doctors in an integrated recommender system | Semantic similarity computing and AHP | OHCs : HaoDF, XYWY, Ask39, and 51daifu |
Gong et al [ ] | September 2015 | China | Using medical social networks and a medical data set to recommend doctors | Time-constraint probability factor graph and random walk with restart | Clinic experiments at the Chinese Academy of Sciences |
Narducci et al [ ] | May 2015 | Italy | Delivering a semantic recommender system based on social networks | Similarity computing and CF | — |
Guo et al [ ] | July 2016 | China | Identifying KOLs using health care data mining for any specific disease | Unsupervised aggregation approach | Medical journal papers |
Zhang et al [ ] | January 2017 | China and United States | Using topic modeling and emotional offset to recommend doctors | Matrix factorization, LDA , and sentiment analysis | Yelp |
Sridevi and Rajeshwara [ ] | August 2018 | India | A personalized physician recommender | Similarity computation and combined ratings | — |
Han et al [ ] | October 2018 | Portugal | Establishing a mechanism for matching patients with family doctors | Hybrid matrix factorization and latent representation | Consultation histories of a leading European health care provider |
Waqar et al [ ] | January 2019 | Pakistan | Combining content-based and collaborative and demographic filtering to create a hybrid physician recommender | Content-based filtering, CF, similarity measure, and AHP | Survey data from 3 hospitals in Islamabad, Pakistan |
Pan et al [ ] | January 2019 | China | Personalizing physician selections based on patient preferences and illness conditions | Dynamic assortment planning and upper confidence bound | Simulation data |
Xu et al [ ] | June 2019 | China | Recommendations based on doctors’ reputation scores and similarities with patients’ demands | Truth discovery, modified Paillier cryptosystem, and Dirichlet distribution | Simulation data |
Ye et al [ ] | August 2019 | China | Picking doctors using signaling theory with patient needs | Binary long short-term memory, LDA, regression, and AHP | OHCs: HaoDF and XYWY |
Yang et al [ ] | February 2020 | China | Enhancing physician recommendations based on patient preferences | Intuitionistic fuzzy sets and Bonferroni mean | OHC: HaoDF |
Wen et al [ ] | April 2020 | China | Providing real-time personalized recommendations by optimizing limited physician resources | Adjusted exponential inventory balancing | Simulation data |
Mondal et al [ ] | October 2020 | India | Modeling patient-physician relationships to recommend doctors | Multilayer graph data model | Records from health centers and hospitals |
Yan et al [ ] | October 2020 | China | Fusing review text and physician information to improve medical consultation recommendations | Convolutional neural network and probabilistic matrix factorization | OHC: HaoDF |
Meng and Xiong [ ] | January 2021 | China | To propose a hybrid physician recommendation model based on OHCs | Eigenvector, word2vec, and LDA | OHC: Chunyu |
Peito and Han [ ] | January 2021 | Portugal | Developing a content-based matchmaking system for patients and doctors | Pretrained Poincaré embeddings and transfer learning | A data set of a European private health network |
Wang et al [ ] | January 2021 | China | Proposing a diversity-enhanced hierarchical physician recommendation approach | Matrix factorization and heuristics | OHC: HaoDF |
Ju and Zhang [ ] | August 2021 | China | Ontology-based recommendation of doctors based on disease text mining | Ontology and text mining | OHC: GuaHao |
Yuan and Deng [ ] | February 2022 | China | Using knowledge graphs and deep learning to recommend doctors based on OHCs | Knowledge graph and deep learning | OHC: HaoDF |
Lu et al [ ] | May 2022 | China | Recommending doctors through expertise learning in OHCs | Multi-head attention and pretrained BERT | OHC: Chunyu |
Chen et al [ ] | July 2022 | China | Considering patients’ risk preference in a probabilistic linguistic environment to recommend doctors | Probabilistic linguistic term set, TF-IDF , and word2vec | OHC: HaoDF |
Wang et al [ ] | August 2022 | China | Developing a model to predict patients’ preferences regarding medical consultations based on physician characteristics | LASSO , multilayer perceptron, decision tree, and Shapley Additive Explanations | OHC: HaoDF |
Wu et al [ ] | February 2023 | China | Making a decision-making method for online physician selection that considers correlation | Choquet integral, BERT, and 2-additive fuzzy measure | OHC: DXY |
Valdeira et al [ ] | August 2023 | Portugal | Physician recommendation with implicit feedback and limited patient information | Deep extreme classification with label features | Consultations of a European private health network |
a CF: collaborative filtering.
b AHP: analytic hierarchy process.
c OHC: online health community.
d Not applicable.
e KOL: key opinion leader.
f LDA: latent Dirichlet allocation.
g BERT: Bidirectional Encoder Representations from Transformers.
h TF-IDF: term frequency–inverse document frequency.
i LASSO: least absolute shrinkage and selection operator.
Category and features | Description | ||
ID, name, age, gender, geographic location, hospital, and department | Physician’s personal information | ||
Specialties, number of patients, and professional title | Professional experience and expertise | ||
Academic background, research achievements, and academic titles | Academic background | ||
Patient ratings, patient reviews, and patient satisfaction | Online and offline word of mouth | ||
Number of popular science articles | Physicians’ willingness to engage in science popularization. | ||
Historical records | Physicians’ historical consultations | ||
ID, gender, age, and location | Patients’ personal basic information | ||
Disease description and medical history | Disease information provided by the patient in advance | ||
Consulting records | Records of patient consultations with physicians | ||
Hospital grade and ranking | Hospital reputation |
An accurate acquisition of features enables an effective recommendation system, and feature engineering forms the foundation of personalized recommendation systems. Data engineering begins with raw data preprocessing. Duplicate or missing values can be handled by deleting them or using average values. Semistructured data, such as the demographics of physicians or patients, need to be converted into structured data by recognizing named entities and extracting information. When analyzing unstructured data, such as physician-patient consultation records, the content may be nonstandard, repetitive, short, and straightforward. Pycorrector, a third-party open-source library developed by Python, can be used to correct some common errors in oral expression [ 38 ]. Afterward, word separation, deactivation removal, normalization, and other procedures are performed. A word separation process extracts and vectorizes text features. Considering the specificity and professional nature of the medical field, the consultation records contain many medical professional words, and synonymous disease names must be substituted (eg, the term “trisomy 21” indicates a pediatric Down syndrome disorder). To ensure that professional terms are recognized during word segmentation, it is recommended to develop a dictionary based on medical ontologies. Furthermore, medical experts can be consulted to refine the dictionary by deleting terms outside the required domain [ 4 ]. Afterward, stop words should be removed to eliminate meaningless words or characters and reduce noise. For word segmentation, the most commonly used tools are Jieba and WordNet Lemmatizer in the Natural Language Toolkit library; for removing stop words, the most commonly used lexicons include the Harbin Institute of Technology stop word list, the Baidu stop word list, and stop words in the Natural Language Toolkit library.
OMC recommendations also face data sparsity challenges. Domain specialization leads to data sparsity. An OMC is not a domain of fast-moving consumer goods but a professional service. Most people do not consult physicians regularly but rather initiate consultations only when they need one, such as when a condition arises. In most cases, patients will consult only 1 physician for a condition or disease. Once cured, they will not revisit the same physician; otherwise, they will try another physician. In other words, it is rare for a physician and patient to have multiple records of the same condition or disease. Despite OHCs having an extensive collection of physicians, most of those physicians are considered “silent” in the communities as, in most cases, patients pay attention only to those physicians who are well-known and highly regarded. It was only possible for patients to rate or write reviews for physicians they had consulted rather than for other physicians. All these factors contribute to data sparsity.
To alleviate data sparsity, either the model should be improved or more features should be mined. According to the literature [ 30 ], patients’ uncertain characteristics and preferences could be revealed through uncertainty languages, and fuzzy analysis could be used to improve recommender systems’ sparsity problem. KGs have been introduced to represent physician-patient interaction features in the physician recommendation problem, thereby alleviating data sparsity [ 4 ]. A sociosemantic approach was used to address the problem of data sparsity caused by user-based CF [ 43 ]. Son and Choi [ 44 ] used ordinal and binary ratings of experts to refine user opinions and mitigated data sparsity in hand-edited expert recommendations. Wang et al [ 37 ] proposed a matrix decomposition to handle sparse data and improve prediction accuracy.
Medicine is a very specialized field of science. Often, because of cognitive limitations, patients cannot express their conditions and medical histories in consultation content, and some are unable to even express their personal needs. Using topic models, unstructured texts are analyzed for their content to retrieve, classify, cluster, summarize, and find topics that have similarities or relevance. The most common topic modeling method, LDA, uses an unsupervised probabilistic model to generate topics [ 31 ]. Typically, LDA is used to extract topics from large data sets of documents by mining potential semantic relationships between them. Meng and Xiong [ 35 ] used all physician consultations as a corpus for LDA, as shown in Figure 3 , and each physician’s text-topic distribution was then used to train a model to retrieve the corresponding physician for a specific topic. Zhang et al [ 27 ] applied LDA to extract patients’ potential preferences and the characteristics of the physicians they consulted from patient reviews. LDA has some shortcomings. First, LDA lacks semantic contextual information when processing text because the commonly used bag-of-words model ignores it [ 34 ]. Second, LDA models perform poorly when text topics are too sparse to represent potential features; training LDAs tends to overfit if there are too many topics, so a fair number of topics must be selected to strike a balance between the degree of fit and simplicity. Finally, LDA models cannot handle labeled data on documents, causing uninterpretable topics to be generated. From various perspectives, scholars have proposed solutions to the aforementioned drawbacks. Ye et al [ 31 ] reduced the time complexity of LDA via Gibbs sampling and determined the optimal number of LDA topics based on the confusion level. Because the patient’s “initial inquiry” text is usually short and the corresponding topic vector representation is sparse, Liu [ 45 ] used a short text aggregation algorithm to represent the topic vector. Pan and Ni [ 46 ] used a labeled LDA model to generate probability distributions for health questions and topics and topics or words based on the text set of physicians’ answers to health questions.
Sentiment analysis identifies users’ attitudes and opinions on commodities or services from their review texts. In addition to medical topics, consultations and patient evaluations on OHCs include patient emotions and feelings. Using sentiment mining techniques, sentiment information can be extracted from text data. Text sentiment analysis can be divided into 2 main types: lexicon based and deep learning based. Sentiment dictionaries are the traditional tool for analyzing words and short texts’ sentiment tendencies [ 31 ]. These dictionaries describe not only the positive and negative sentiment attributes of words in static dictionaries but also the offsets of sentiment information of words in sentence frameworks. The China National Knowledge Infrastructure, the Information Retrieval Laboratory at Dalian University of Technology, and the Natural Language Processing and Social Humanities Computing Laboratory at Tsinghua University are 3 dictionaries commonly used for sentiment analysis of Chinese texts. On the basis of sentiment dictionaries, Zhang et al [ 27 ] used unsupervised learning methods to calculate the offset between patients’ comments and their sentiments and correct the original patient ratings. There is evidence that deep learning is superior in the analysis of long texts containing complex sentiments. To analyze positive and negative sentiments in patient reviews, Ye et al [ 31 ] used the binary long short-term memory method, which achieved better results than sentiment dictionary analysis. For sentiment polarity analysis in review texts, Wu and Sun [ 47 ] used the Bidirectional Encoder Representations from Transformers model, and for recommendation results, they applied the Wilson interval method. Due to the subjective nature of patient comments and the unreliability of sentiment ratings, sentiment mining methods have limitations. Data sources of uneven quality can also affect the accuracy of sentiment evaluations. The fuzzy analysis of the text can help address the uncertainty of text description [ 48 ]. The fuzzy analysis mainly applies fuzzy mathematical or fuzzy linguistic methods, which allow recommender systems to express uncertainty and obtain personalized features from patient comments. Intuitionistic fuzzy numbers serve as effective tools for dealing with fuzzy information (ie, describing the degree of neutrality in uncertain situations). Yang et al [ 32 ] converted raw data into intuitionistic fuzzy numbers to describe uncertainty information by combining the patient’s disease description with comments. Xu et al [ 48 ] examined data based on hesitant fuzzy language multi-criteria preference analysis to enhance patient preferences for physician recommendations.
OMCs recommend a service with suitable physicians according to the patients’ needs, an application scenario differing from that of item recommendation in e-commerce and rather resembling expert discovery in online question and answer (Q&A) communities or academic peer review. These recommendations have one thing in common: the recommended subject is not a product but rather a human, a competent and knowledgeable professional. A physician’s expertise can be inferred from their educational and professional background as well as historical consultations, similar to the history of expert responses in Q&A communities or the list of academic papers of a scholar. Patient comments and ratings for a physician are similar to the number of likes for a Q&A expert or citations of a scholar. It can be compared to assigning a competent academic reviewer to a new topic, finding a suitable expert to answer a new question, or recommending an appropriate physician based on graphic descriptions of the patient’s consultation.
Knowledge-intensive service recommendations are determined by matching large amounts of textual information between patients’ inquiries and physicians’ skill sets. In general, the better the information match, the more likely it is that the service recommendation will be successful. As part of content-based recommendations, physicians’ backgrounds and historical data are gathered, and textual topic techniques are used to mine their expertise, such as LDA, probabilistic latent semantic analysis, and so forth. Pan and Ni [ 46 ] modeled the textual topics of historical consultations and physician responses under each section, mined physician expertise using labeled LDA, and completed physician recommendations based on candidate physician expertise and pending inquiries.
Social network–based expert recommendations have grown in popularity and are derived from a classical algorithm of information retrieval (ie, PageRank). For expert recommendations, Wang et al [ 49 ] proposed a convolutional neural network for answering online expert questions that effectively reduces waiting time for the questioner and improves the quality of the answer. To alleviate the cold-start problem for new-coming patients, physician recommendation–related studies should consider patients with similar conditions in the OHC who exchange information and provide emotional support, as illustrated in Figure 4 . Recently, expert recommendation research has increasingly incorporated integrated models that combine features such as social networks and knowledge content. Xu et al [ 50 ] proposed a scholarly recommendation framework that integrates social network analysis and conceptual semantic analysis in 2 dimensions: social relationships among scholars and information about their expertise. Yang et al [ 51 ] used information about research relevance, personal social networks, and institutional connections to identify the most appropriate experts for collaboration on research. Xu et al [ 52 ] proposed a methodology for a collaborative recommendation that integrates expert expertise and social information in a complex heterogeneous network using heterogeneous network mining. It identifies valuable meta-paths through information gain and uses regularized optimization to generate personalized recommendations tailored to each scholar’s needs. Different recommendation algorithms have different strengths in comparison. Expert recommendations based on knowledge content are better suited for use in enterprises with high levels of information quality and clearly defined knowledge hierarchies. Information quality in OHCs is significantly lower than that in general organizations, and expert recommendations are greatly influenced by the structure of social networks [ 53 ]. Both of these features are present in the OMC service recommendations studied in this review.
A KG is a structured semantic knowledge base that integrates heterogeneous information from multiple sources and represents rich entity relationships using complex networks, which facilitates the storage, processing, and communication of complex real-world knowledge. Medicine is a specialized scientific field, and vector representations of KGs enable algorithms to obtain embeddings of concepts, class hierarchies, entities, and relationships and, in turn, graph structures, paths, and subgraphs. Algorithms can achieve logical reasoning in vector space with the help of ontology embedding and rule learning. For the OHC platform to be credible, physicians must provide their real names, educational backgrounds, professional experience, and expertise so that their profiles can be verified. Using document clustering analysis, LDA topic segmentation, and feature extraction from physician historical consultations, a KG describing physician specialty and expertise can be constructed. Yuan and Deng [ 4 ] produced a more accurate and interpretable recommendation scheme based on the KG to overcome the problem of sparse data. It is common practice for existing studies to extract entities based on physician historical consultations; however, these data alone are not sufficient to represent physician professional specialties. For example, if an otolaryngologist has only received consultations related to the ear and nose for various reasons, then the system only measures their expertise in the ear and nose. However, in practice, they also have excellent expertise in laryngology, which the system cannot calculate. An appropriate recommendation system should be designed to recognize the differences between specific diseases and the expertise of various physicians within the same department. As shown in Figure 5 , the original scope of historical consultations should be extended to include new entity nodes such as specialized disciplines, physicians, and consultations. To optimize the network structure of the KG, we should analyze the semantic connotation of keywords, determine the semantic similarity between consulting cases and their attribution to specialized disciplines, and examine the professional areas of physicians and their evolution trends.
KG-based physician recommendations are a new trend in OMC service recommendations. By using logistic regression, plain Bayesian classification, and noise-immune gate Bayesian networks, Rotmensch et al [ 54 ] constructed a KG, and from the parameter training, a disease-symptom topological relationship graph was generated. Liu [ 45 ] used the k-means algorithm to cluster physicians and generalized goodness-of-fit metrics to evaluate and adjust the clustering results. By comparing the patient’s consultation content with the physician clustering center and the individual physician information in each category, a physician category and physician object that are more closely matched could be recommended. Xu et al [ 52 ] proposed a collaborative recommendation method for scholars based on heterogeneous network mining, combining expert expertise with social information, identifying valuable meta-paths through information gain, and providing personalized recommendations for each scholar through canonical optimization. On the basis of the similarity of consultation texts, Meng and Xiong [ 35 ] constructed a co-occurrence label network of physicians and calculated the centrality of the feature vector to recommend the most important physicians. Gong et al [ 19 ] proposed a hybrid multilayer architecture, iBole, of physician recommendations, mining physician-patient relationships using a time-constrained probabilistic factor graph model and recommending physicians based on random wandering. KG-based physician recommendations also have drawbacks. The OMC service faces more complicated application scenarios involving multiple entities and interentity relationships that reflect a physician’s knowledge or disease-symptom connection. It is difficult to integrate different attributes and relationships between attributes in traditional recommendation methods, and it is nearly impossible to visualize the relationship between each knowledge attribute and physicians. KG-based OMC service recommendations should use multisource heterogeneous information to mine physicians’ comprehensive expertise, take their profiles as basic professional descriptions, mine all their published articles using text semantics, and then combine their historical consultations with multimodal data to extract features using multimodal mining and LDA topic segmentation.
As medicine is such a specialized field of science, recommendations must be interpreted according to the patients’ cognitive capacity. It is difficult for patients to make autonomous judgments about the recommendations using their knowledge because they lack theories and relevant experience. Most of the existing research on recommendation systems is devoted to the professional accuracy of recommendation results. It casually ignores the interpretability of recommendation schemes and the lack of transparency in the system computation process [ 4 ]. In other words, the recommendation process and logic are not adequately explained to patients considering their cognitive capabilities. It is very critical for the recommendation system to be interpretable as it directly correlates with the level of trust of patients [ 55 ]. To provide patients with a reference for decision-making, we believe that a good recommendation system for OMC services must incorporate an interpretable and user-friendly recommendation algorithm. As a result, patient acceptance and recognition of the recommendation results will be enhanced, which will ultimately result in a higher acceptance rate of the recommended solution of the system. As a result of their limited cognitive abilities, many patients, in addition to not judging the recommendations, struggle to make their inquiries clear and complete, and in a few cases, they even cannot accurately articulate their personal needs. As an alternative to solving such difficult problems, multimodal data mining techniques may be considered, such as multimodal graphical topic modeling for patient description and consultation needs. Not only can key information from patient consultations be explored and labels can be extracted, but it is also possible to avoid creating too sparse input text variables by avoiding personalized verbal expressions and symptoms. Machine learning algorithms can easily process clustered documents when they are converted into vector distributions.
Recommendation algorithms can be interpreted in light of the rich semantic connections between physicians and patients in the KG [ 4 ]. Some studies have demonstrated that interpretable recommendation algorithms based on KGs enhance the level of patient trust. Using KG-based disease diagnosis algorithms, Wu and Sun [ 47 ] obtained initial disease alternative sets by querying the KG and using the KG embedding model; the KG-structured information was used to enrich the disease alternative set, enhancing the recommendation accuracy and facilitating the recommendation of potential diseases to the user. To identify the different roles of physician-patient interaction characteristics and individual physician characteristics in physician recommendations, Yuan and Deng [ 4 ] developed a deep learning model that can provide accurate and interpretable physician recommendation information by combining layer-by-layer association propagation techniques with deep neural networks. Considering the accuracy, diversity, and interpretability of KG-based recommendations resulting from information such as rich semantic relationships and item links within a network, we propose that interpretable recommendations should be built based on KG path inferences. The algorithm should adopt a knowledge-aware path recurrent network model, which generates path representations by combining the semantics of entities and relations, reasoning by using sequential dependency in paths to infer interaction between users and items, and incorporating a weighted pool into the process of inferring user preferences to differentiate between different contributions from different paths to provide interpretable recommendations.
Physician recommendations can be evaluated online or offline. Online evaluation involves measuring the effectiveness of the recommendation system by obtaining the target users’ evaluation of the recommended object, namely, the rating of the recommended physician by patients. Guo et al [ 26 ] asked 3 faculty members and 3 graduate students with medical backgrounds to judge candidate physicians based on their perception of their professional activities and reputation and use the mean of the ratings to rank them. Ye et al [ 31 ] recruited 18 students with experience in helping relatives choose a physician to consult on the web and asked them to assess the relevance of the physician in response to a given consultation question. Wu and Sun [ 47 ] used a questionnaire to assess the accuracy of a physician’s recommendation and validate the proposed recommendation algorithm, including whether the respondents had had a particular disease, had been treated in the area, and had approved of the physician. An online evaluation has several shortcomings, including a high implementation cost and the difficulty of excluding the characteristics of the group surveyed as well as personal subjective factors from the results. An offline evaluation involves feeding training set data into the system for training the recommendation model and calculating the recommendation results based on test data to measure the performance of the recommendation system. In most cases, machine learning models are trained through supervised learning, which means that the predicted output of the recommendation model is compared with the true value, and based on the difference, model training methods can be altered and parameters can be adjusted to facilitate the continuous optimization of the model [ 53 ]. There are different measurement criteria for the difference between the predicted output of the model and the true value. Offline evaluations are predominantly based on accuracy, which includes classification accuracy, prediction accuracy, and ranking accuracy.
The diversity and coverage of recommended physicians have also been used to evaluate the performance of recommendation algorithms. According to the literature [ 32 ], recommending only similar physicians results in a limited choice for patients and an imbalance in physician use. Patients will be more likely to engage with the recommender system if there is more diversity of recommended physicians. A measure of coverage refers to the proportion of recommended physicians to all physicians [ 56 ]. A low level of coverage indicates that a limited number of physicians are available to patients. Patients are likely to be less satisfied with a recommender system if the candidate pool is limited. However, diversity and coverage metrics are not currently heavily used for evaluating physician recommendation systems. Physician recommendations differ from traditional e-commerce recommendations in some respects. Patients should be recommended physicians with similar expertise or experience that matches their disease conditions rather than a greater variety and number of physicians. Increasing the diversity and coverage of physician recommendations is unfavorable to patient outcomes, thereby affecting the application of these 2 metrics in physician recommendations.
Personalized recommendation studies have previously focused on commodity recommendations based on “users versus items” and rarely considered service recommendations based on “users versus users.” This paper focused on human carriers who deliver OMC services, particularly when recommending professional services. The OMC service represents a new form of e-business under the knowledge economy as well as a new direction for the development of e-services. Figure 6 shows that a knowledge service–oriented recommendation differs from a traditional commodity-oriented recommendation from a system thinking perspective.
An independent service-oriented recommendation system requires a novel theoretical framework and its key techniques. Table 4 illustrates the comparison between e-commerce and e-service recommendations. First, earlier studies only considered the interests and preferences of the user, not the feelings of the providers recommended; the adoption of an OMC recommendation depends not only on the opinion of the consumer but also on the preference of the service provider. It is impossible to achieve even the so-called “best” recommendation scheme by focusing only on the needs of consumers and ignoring the individual preferences of service providers. Moreover, as the physician is more aware than the patient, they should have a higher priority in terms of decision-making [ 57 ]. Existing personalized recommendation systems have obvious flaws and weaknesses both theoretically and algorithmically even when designed specifically for consulting services. Although the recommended subjects in some expert recommendation system research, such as thesis review, project approval, and other scenarios, are also humans, the recommendation algorithm still focuses on the personalized characteristics of the demand side, analyzing only the professional skills of the experts rather than considering their preferences. These experts are just “tool men.” In the case of e-service recommendation applications such as OMCs, such a research perspective and research conclusions are not applicable. Due to the existence of intrinsic and extrinsic needs of 2-sided users, it is apparent that a new paradigm of personalized recommendation research must be based on a service-oriented approach.
The professional characteristics of the service require that the system provide consumers with explainable recommendations according to their cognitive levels. Medical diagnosis and treatment is a very specialized field. Most patients do not have a very clear understanding of it. The model should be capable of explaining the recommendation schemes so that patients can make informed decisions [ 4 ]. In the case of e-commerce–oriented recommendations, interpretability is not required as users understand the utility of the items and what they desire. Thus, the system simply needs to fully exploit the hidden needs and interests of users. Algorithms focus primarily on collecting users’ side information to identify their potential needs and respond to their individualized preferences [ 58 ]. Due to the consideration of medical privacy in the OMC scenario, the system is unable to extract patients’ hidden medical histories or other information from their historical treatment records [ 30 ]. Furthermore, patients generally lack medical knowledge and are unable to make independent judgments about the recommended results. Having interpretable algorithms improves not only the transparency of the recommendations but also the trust and acceptance of patients, which improves postevent satisfaction with physicians [ 4 ].
OMCs’ particularity is also reflected in its knowledge- and labor-intensive nature. OMCs are professional consultations and intellectually demanding services that involve bilateral interactions between physicians and patients [ 59 ], so physician workload must be carefully considered. Traditional e-commerce–oriented recommendation algorithms typically produce “popular products” or “superstars,” which do not consider the overwork of physicians. In reality, it is impossible to achieve an overloaded recommender scheme regardless of how well the patient’s condition matches the physician’s specialty. A few studies have addressed the “diversity” or “coverage” of recommendations; however, they only increase the total number of item types without considering the frequency of recommendations for a single item. Whenever a human-based service recommendation system is used, the workload problem must be considered, yet it has rarely been taken into account in previous studies.
Data about users are not always valuable. Whether user reviews contribute to the formulation of recommendations is also a difference between OMC scenarios and those of other applications. Several previous studies have attempted to obtain useful information from patient reviews, but these efforts have proven unsuccessful [ 60 , 61 ]. In general, patients are attracted to “popular” physicians with many positive reviews and few moderate and poor reviews, whereas young or unknown physicians are underrepresented, with few respondents and a lack of adequate review data. In total, 3 factors contribute to this phenomenon: patients are unprofessional, physicians are uncooperative, and evaluation of services is difficult. The first challenge is that patients are incapable of evaluating the effectiveness of professional services, and no significant correlation has been found between the online reviews of patients and their clinical outcomes [ 3 ]. Second, physicians will vigorously resist unprofessional, emotional, and malicious reviews that can harm their professional reputation [ 62 ] and may even “vote with their feet” to force the platform to block complaints. Third, the success of OMC services is dependent not only on physicians’ professionalism but also on patients’ perceptions and expectations. In addition, the patient's experience also depends on whether the medical institution where the doctor works can provide advanced medical equipment and a convenient medical environment. Even the ease of use, stability, and privacy security of OHC platforms may have an impact on patients’ evaluations [ 63 ]. Until it has been established what techniques and methods are being used to extract key elements from subjective, ambiguous, and complex patient reviews, e-service–oriented personalized recommendation systems should be cautious about using comments and ratings.
e-Commerce oriented | e-Service oriented | |
Components | Users vs commodities | Users vs users |
Recommended items | Commodities | Services |
Decision makers | Only users | 2-sided users |
Personal preferences | Only users | Both patients and physicians |
Workload | — | Physicians |
Reviews and ratings | Important features | Useful but needs caution |
Interpretability | Optional | Required |
a Not applicable.
Personalized recommendations are based on user preferences, and acquiring accurate user preferences is key to ensuring their quality [ 29 , 32 ]. In contrast to other recommendations, OMC recommendations need to consider the preferences of both consumers and providers as an OHC is a 2-sided market constituted by both patients and physicians, each with independent and stable preferences. Physician preferences have regrettably been ignored in previous recommendation systems, which has resulted in infeasible recommendations. Malgonde et al [ 5 ] proposed a 2-sided recommendation framework for digital platforms to mitigate user emergence as a commercially complex adaptive system with differential and evolving goals, preferences, and constraints for both sides of a 2-sided market. The patients’ personal preferences influence their selection behavior and, thus, their satisfaction with the recommendations [ 23 , 33 , 38 ]. In turn, the physicians’ preferences influence their willingness to receive consultations, and in turn, the physicians’ onboarding and retention determine the continuity and development of the OHC [ 64 ]. Due to the differences in scale and quality of data between the 2 types of users, patients and physicians should have independent approaches to the extraction of features and the mining of behavioral patterns.
Patients’ preferences and needs have been relatively adequately explored in existing studies on physician recommendations. As shown in Table 5 , when choosing a physician, patients typically consider the physician’s disciplinary background, professional competence, and institutional reputation, as well as other factors such as distance, cost, and follow-up care. To provide patients with personalized recommendations, Pan et al [ 29 ] proposed a user preference–learning algorithm to learn patient preferences. Jiang and Xu [ 24 ] proposed an integrated recommendation method that uses hierarchical analysis to screen candidate physicians based on 3 dimensions: semantic matching of physician-patient professional texts, objective evaluation of physician authority, and subjective evaluation of physician online word of mouth. Ye et al [ 65 ] used SPSS to screen patient decision factors and recommend physicians based on their composite scores. Wang et al [ 37 ] even directly used the number of visits as an important determining factor for how patients viewed the standard of care provided by physicians. Xu et al [ 30 ] investigated the privacy issues of patients and provided a multi-indicator group decision–ranking system of physicians.
Study | Reputation | Service (experience) | Affordability (costs) | Others (cares) | |||||||||||||||||
Affiliation | Reputation | Word of mouth | |||||||||||||||||||
Education | Organization | Position or title | Achievements | Online ratings | User evaluation | Expertise | Practices | Histories | Distance | Expenses | Following costs | Privacy | Discrimination | ||||||||
Jiang and Xu [ ] | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | |||||||||||||
Liu et al [ ] | ✓ | ✓ | ✓ | ||||||||||||||||||
Deng et al [ ] | ✓ | ✓ | ✓ | ||||||||||||||||||
Li et al [ ] | ✓ | ✓ | ✓ | ✓ | |||||||||||||||||
Li et al [ ] | ✓ | ✓ | ✓ | ✓ | |||||||||||||||||
Li and Hubner [ ] | ✓ | ||||||||||||||||||||
Xu et al [ ] | ✓ | ✓ | ✓ | ||||||||||||||||||
Xu et al [ ] | ✓ | ✓ | ✓ | ||||||||||||||||||
Gong et al [ ] | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | |||||||||||||||
Ju and Zhang [ ] | ✓ | ✓ | ✓ | ||||||||||||||||||
Wang et al [ ] | ✓ | ✓ | ✓ | ✓ | ✓ | ||||||||||||||||
Yuan and Deng [ ] | ✓ | ✓ | ✓ | ✓ | ✓ |
“Worshipping famous physicians” has become a very common phenomenon among patients. No matter the severity of the patient’s disease, most patients prefer senior physicians from bigger institutions and more reputable practices [ 37 ]. The reputation of a physician is one of their most valuable attributes and plays an important role in patients’ decision-making process [ 67 ]. Generally, physician reputation can be divided into 2 categories: offline and online reputation [ 66 ]. The former is determined by the hospital’s rank, academic title, professional level, the number of years in the field, and the popularity of the physician, and the latter depends on patient evaluations and ratings as well as the number of votes received, acknowledgment letters, online gifts, and other factors. Patients’ cult of famous physicians is largely based on physicians’ offline reputations. Liu et al [ 66 ] found that the ranking of the hospital and the title of the physician had a direct impact on patients’ choices. The higher the title and ranking, the more popular the individual was. Deng et al [ 67 ] also concluded that the title of the physician had a significant impact on the choice of the patient. Patients favored the chief or deputy chief physician over the regular resident physician. In addition, offline reputation can moderate the impact of online reviews on patient choice. Li et al [ 69 ] demonstrated that hospital rank and physician professional credentials negatively moderate the effect of physician online ratings and activity on patient choice. Huang et al [ 72 ] revealed that a physician’s high title negatively moderated the effect on physician service ratings while positively moderating the number of service reviews. Word of mouth in OHCs determines physicians’ online reputation. The experiences of previous patients, reviews, and recommendations are important decision-making aids for newcomers. Deng et al [ 67 ] revealed that the number of views and votes received on physicians’ home pages positively influenced patients’ choice of physician. Gong et al [ 71 ] examined the impact of online reviews and online ratings of physicians on patient decisions from the perspective of trust theory. Li et al [ 69 ] found that positive physician reviews were positively related to a patient’s choice of physician, whereas negative physician reviews played the opposite role, and that negative reviews had a greater impact on a patient’s choice of a physician than positive reviews. Li and Hubner [ 70 ] demonstrated that patients preferred physicians with higher technical skills over those with higher interpersonal skills based on the different dimensions of physician ratings.
With regard to social exchange theory, physicians’ participation in OHCs is a social exchange behavior, and services such as publishing scientific articles, providing OMC services, and offering appointment registration can bring physicians financial and social rewards [ 6 , 72 ]. The quality of a physician’s services is reflected in patients’ online ratings and postevaluations, which in turn influence the decision to choose a physician made by potential patients in the future. Physician service quality in OHCs can be measured by the level of platform activity, engagement, responsiveness, and frequency of updating popular articles. Deng et al [ 67 ] asserted that physicians’ behaviors, such as regular updating of medical information, publication of scientific articles, and answering patients’ questions, can enhance their community reputation, which in turn can attract more patients. Gong et al [ 71 ] noted that updating physicians’ information frequently and providing quality online services were critical to building trust between physicians and patients. Using the number of physician publications of popular articles in OHCs, Li et al [ 69 ] found that physician activeness was positively associated with patient selection.
It is also important for patients to consider the time and financial expense of visiting their physician when selecting a physician, preferring an appointment time and location that is convenient for them as well as cost-effective treatment options [ 29 , 32 ]. One of the factors that patients consider when choosing a physician is the location of the physician. Typically, patients consult on the web before consulting offline, and the location of the OMC-receiving physician is related to the convenience of future offline consultations. Ju and Zhang [ 38 ] considered the location of the patient to improve the convenience of combining online consultation with offline treatment. Deveugele et al [ 73 ] analyzed questionnaire data from 6 European countries and studied video recordings of consultations and found that the location of a physician’s hospital affected the length of the online consultation. Compared to geographical location, consultation costs have relatively little impact on patients’ choice of OMC services. Khairat et al [ 74 ] reported that costs were one of the primary factors determining patients’ choice between mobile health and telemedicine. Fletcher et al [ 75 ] also argued that the cost of providing mental health treatment via video at home was significantly lower than the cost of providing in-person care assuming that patients can make use of existing personal technology.
The personal characteristics of a physician, such as their appearance and gender, can also influence patients’ choices. Ouyang and Wang [ 76 ] found that a serious and stable physician appearance image contributes to patients’ trust in physicians, which in turn influences their medical choices. In addition, patients have some stereotypes about physicians’ gender. The gender difference in physicians also extends to the distinction between different departments and medical specialties. Bertakis [ 77 ] found that male and female physicians practice in different ways, with female physicians providing more psychological counseling and preventive services and male physicians focusing more on technical practices such as physical examination. A physician’s gender also influences patient choice. Gong et al [ 71 ] found that physician gender influenced physician ratings and patient choice and that patient choice was enhanced when the physician was male.
Continual physician involvement is crucial to the survival, growth, and prosperity of OHCs [ 64 ]. Although patients are consumers of OHCs and physicians are merely providers, the latter are of greater significance and influence. Patients who participate in OHCs seek out famous physicians, and existing OHCs are essentially physician-driven organizations [ 57 ]. In comparison with their counterparts, physicians possess a higher level of cognition and more logical behavior. There is a relatively large amount of data on physicians in current OHCs. By mining behavioral data, it is possible to gain a better understanding of their motivations and expectations. Unfortunately, most previous studies have been primarily concerned with physicians’ fitness from a professional perspective rather than with their willingness and preferences from a drive and reward perspective. Current paradigms of research, which ignore the individualized preferences of the recommended population, are not adequate to meet the growing need for human-based, knowledge-based service recommendations. According to physician motivation theory, we propose a research paradigm to examine how perceptions of personal benefits and costs, satisfaction with individual needs, and cultural differences influence physicians’ OMC decisions. Few studies have examined physicians’ preferences, and more have discussed physicians’ motivation to participate, which influences physicians’ performance in OHCs. Physicians who join OHCs and provide OMC services face both costs and rewards [ 6 ]. A rational decision is based on weighing the costs and benefits. Physicians incur cognitive costs, which include fatigue, pain, and irritability generated by providing knowledge- and labor-intensive services, and implementation costs, which include time, material, and financial costs. Physicians receive a variety of rewards, including both social and economic rewards. The former describes that a physician is respected and valued by their patients for the services they provide in OHCs as well as for fulfilling their own needs and realizing their self-worth, and the latter represents that a physician receives both direct financial gains from OMCs as well as virtual gifts and bonuses from their patients. Financial and social rewards are significant factors influencing physicians’ engagement in OHCs and OMCs. Physicians’ expectations also play a role in the extent of their influence. Figure 7 illustrates how data mining of physicians who participate in OHCs and determining their motivation to participate in OMCs can be carried out. Data collected include but are not limited to academic titles, educational background, career experience, scientific research accomplishments, and case characteristics associated with their historical consultations. The objective of mining these data is to develop a multidimensional preference index system for material motivation, career motivation, and social capital motivation. This will enable us to improve the adoption rate of recommendations and promote a personalized physician recommendation system.
Physicians’ motivations for joining OHCs are remarkable in their diversity. Physicians are concerned not only with financial rewards but also with career planning, professional reputation, and social capital. These considerations include the need for self-worth realization, prestige, social support, and personal branding [ 64 , 78 ]. The needs theory by Maslow [ 79 ] suggests that prestige contributes to self-realization. Social exchange theory also reveals that self-realization, prestige, and social support positively influence physicians’ willingness to provide online services, whereas executive costs negatively impact their willingness to do so. Using expectancy theory, Chen et al [ 64 ] found that both external motivation (eg, external rewards and expected relationships) and intrinsic motivation (ie, a sense of self-worth) positively influenced physicians’ willingness to provide consultation services, whereas consultation time, as a major cost, negatively moderated the relationship between physicians’ willingness to serve and their behavior. Zhou et al [ 80 ] combined mental health–related OHCs with motivation theory and demonstrated that both intrinsic (technical competence) and extrinsic (network reputation and financial rewards) motivations positively influenced psychologists’ voluntary behaviors. Yang et al [ 81 ] suggested that physicians’ contributions to OHCs were positively influenced by both personal and social motivations and physicians’ professional titles moderated this effect, with physicians with high titles emphasizing reputation and physicians with low titles emphasizing monetary rewards. Zhang et al [ 3 ] found that, when physicians reach an advanced level of expertise and knowledge, their material motivation declines and their professional motivation increases. Some physicians place great emphasis on personal branding, and their online services are designed to support their brand positioning and identity. Zhang et al [ 82 ] indicated that the OHC environment impacts brand performance, including trust and reputation, which become more significant factors in determining whether physicians participate in a consultation.
Most physicians provide OMC services for financial reasons. OHCs need to understand how to improve financial rewards for physicians to retain good physicians. Ren and Ma [ 17 ] investigated the factors influencing physicians’ economic income in OHCs in the context of the pandemic. They found that service quality had a significant positive effect on physicians’ economic returns. In addition, they found that physician teams increase income with disease privacy and physicians who established a team were more likely to earn more money. On OHCs, physicians share articles about health and medicine as well as providing paid OMC services. According to the literature [ 3 ], physicians share free messages due to both material and professional motivation, with the role of material motivation diminishing as physicians gain more expertise. Zhang et al [ 78 ] reported that mutual aid and altruism can positively influence the willingness of health experts to share knowledge. In addition, reputation and self-efficacy can play a greater role than regular users in health experts’ willingness to share knowledge. Yang et al [ 81 ] demonstrated that physicians are motivated to share paid messages for a variety of reasons. External motivation, enjoyment motivation, and professional motivation are all important factors.
According to the literature [ 16 ], social rewards have less influence on physician motivation than financial rewards. A combination of psychological and material rewards increases physician motivation to participate in OHCs. Material rewards are usually more useful than psychological rewards, but extreme rewards are less effective than moderate rewards. To increase physician retention, OHCs often include gamification elements such as badges, points, and leaderboards. Liu et al [ 83 ] observed that including gamification elements in medical communities can encourage continued participation and increase physician incomes, but on the other hand, gamification elements can also lead to greater income disparities among physicians.
Unlike previous studies, this paper focused on the personalized service recommendation system for 2-sided users. It is not just about providing patients with a list of physicians but also about exploring the overall combination solutions with optimal mutual benefits for both patients and physicians, shown in Figure 8 . Several important issues need to be addressed by researchers in this field, including the adoption of appropriate decision methods that effectively match the interests and preferences of both physicians and patients, improve the adoption rate of recommended solutions, and enhance the satisfaction of 2-sided users [ 84 ]. Xi and Juan [ 84 ] addressed the real problem of matching the supply and demand of health care services under an intelligent platform and proposed a decision-making method that considers both provider’s and consumer’s expectations as well as the psychological characteristics of hesitation and uncertainty. Gao et al [ 85 ] analyzed the problem of matching decisions for medical services in OHCs and constructed a matching decision model that is both satisfactory and stable. Zhong and Bai [ 86 ] analyzed the patient-physician preference matrix and constructed a 2-way matching model for specialty outpatient appointments oriented toward satisfying patients and physicians. Yang et al [ 87 ] used the 2-sided matching theory to design a patient-specialist paired appointment system in which the appointment process and the one-to-many appointment-matching algorithm were described. Chen et al [ 88 ] developed an innovative multi-attribute decision-making method for 2-sided matching considering the psychological behaviors of matching bodies as well as values of aspiration levels and evaluations.
The future research direction of the physician-patient 2-sided matching recommendation system should take into account the decision-making environment of realistic situations. As an example, due to the complexity of medicine and the ambiguity of human thinking, most patients are unable to express clear preference sequences due to their cognitive limitations. By mining consultation text and behavioral characteristics of OHC users, the OMC recommendation system should be able to capture customized preference sequences. Even for physicians, who have higher cognitive levels, more logical behavior, and clearer motivation, there are still situations in which expectation evolution and multiple preferences cannot be ordered. Therefore, the recommendation system must accommodate their intuitive fuzzy preferences. Using intuitive fuzzy preferences, biased order relations can be expressed and preference strengths can be differentiated. Figure 9 illustrates how an intuitionistic fuzzy set matrix is transformed into a satisfaction matrix. The system should then construct a multi-objective optimized, stable 2-sided matching model based on intuitionistic fuzzy number information with the objective of maximizing physician-patient matches, stability, and satisfaction with the matching results.
Physicians, as humans, have not only individual drivers and preferences but also variability in load tolerance. The fact that the recommended physicians represent a limited human resource has generally been overlooked in previous studies. Physicians should not overwork, and they should not be overused for an extended period [ 37 ]. Physician overload affects physician fatigue and consultative quality as well as patient waiting time, which deteriorates the comprehensive evaluation of the recommendation system [ 29 ]. Currently, very few studies have explicitly considered the workload of recommended physicians in recommender systems. To address the problem of unbalanced use among physicians, Pan et al [ 29 ] added a balanced use approach (use balancing) to a preference-learning algorithm that included a negative penalty term for physicians whose current use exceeded the mean value. To balance patient preferences and hospital staff workload, Wang et al [ 37 ] developed a utility-diversity trade-off model based on physician capacity, patient preference, and outpatient workload, which had the effect of reducing the workload for highly regarded hospitals and physicians. Yuan and Deng [ 4 ] suggested that limiting the number of times that a physician is recommended could balance the workload while exposing more people to new physicians who could also share the workload. In addition to reducing the workload of chief physicians, Yang et al [ 32 ] increased the number of recommendations to new physicians, which translates to saving time and money for patients. The system could also be used to identify the activity of each physician’s intake based on historical consultations obtained from OHCs, which, we believe, represents a difference in the upper limit of the workload of individual physicians, which is influenced by the physician’s age, specialty department, and the number of offline consultations they have received.
The load balancing of OMC service recommendations is similar to personalized reranking, which generally refers to ranking items in the recommendation result list based on the user’s preference. On the basis of the recommendation results list, load balancing attempts to determine the workload of each recommended physician, adjust the list order, or replace the candidate physicians according to their predefined individual thresholds so that the recommendations are achieved as efficiently as possible. The reranking algorithms typically use 2 categories of indicators. First, they integrate the reranking indicators directly into the recommendation algorithm to train a multi-objective model. Second, heuristics are used to optimize the reranking indicators using a 2-stage approach of filtering and reranking followed by optimization of the load balancing. Among the integrated algorithms, Adomavicius [ 56 ] presented heuristic neighborhood techniques and matrix decomposition techniques to generate a more diverse set of recommendations with a lower workload for each physician. Pedronette and Torres [ 89 ] proposed a method for reordering image content retrieval systems that combined recommendations with clustering and encoding context through ranking lists. Among the 2-stage algorithms, Yu et al [ 90 ] investigated the relationship between recommendation accuracy and diversity and proposed an adaptive trust-aware recommendation model to improve cold-start and long-tail items. In the literature [ 33 ], a dynamic exponential inventory-balancing algorithm for recommendations is presented based on the condition that physician resources are limited in a dynamic environment and based on real-time remaining resources. Wang et al [ 37 ] developed 2 heuristic algorithms for balancing patient preferences and hospital staff workload as well as updating physician rankings without changing physician capabilities so that patients can access more skilled physicians in more hospitals. In summary, the algorithms differ depending on the application scenario. On the basis of mining historical data, we can determine physicians’ work tolerance levels; to optimize recommendation results, we can personalize constraints on physicians’ upper limit of workload and dynamically optimize between patients’ needs and physician energy so that the results are maximized while maintaining the quality of recommendations and reducing the workload of physicians. Using these ideas can reduce the waiting time for patients and ease the strain on physician resources.
National legislation to protect user privacy in the health care sector is among the most stringent [ 30 , 80 ]. OMC service recommendations can only use anonymized, scrambled, encrypted, and other technically processed historical data. Consequently, it is difficult to obtain an individual identifier for each patient in the data set, which limits the algorithmic mining of patient features. Furthermore, national regulations regarding the prevention of leakage and misuse of personal information are becoming increasingly strict, and all personalized recommendation systems must and can only conduct legitimate research following user privacy protection [ 63 ]. Technically, CF models are not suitable for OMC recommendation scenarios regardless of whether they are user-based CF or term-based CF. A user is unlikely to seek help on the web unless they are ill or experiencing certain symptoms. Therefore, the specialty of physicians that patients seek is not determined by their explicit or implicit interests but rather by their medical needs at that time. The concept of “inferring future needs from patients’ historical data” is not logical in the context of the OMC service scenario. Unfortunately, some existing studies continue to attempt to mine peripheral information and even private information from patients, which is both illegal and ineffective. Simply reusing CF from e-commerce recommendations and recommending physicians based on historical patient data regardless of medical privacy will ruin personalized e-service recommendations. Xu et al [ 30 ] proposed an effective and privacy-preserving medical service recommendation scheme that identifies patients’ demands with physicians’ information along with their reputation score, and it is considered the first study to develop a physician recommendation scheme that ensures computational efficiency. Similarly, to ensure patient privacy, Narducci et al [ 25 ] constructed a semantic recommendation system that does not link the health data entered by patients to their true identities. As user information is protected by regulations, patient consultations contain only isolated texts and graphics related to disease descriptions. Additional information is lacking, potential preferences are unclear, and invisible needs are not addressed comprehensively. As a means of achieving intelligent recommendations under privacy protection, the system must “dance with shackles on.” To guide personalized preference mining, engineering psychology theories would be better applied, followed by natural semantic processing tools, topic models to refine patient descriptions, and semantic mining to quantify qualitative indicators. Patients’ social networks and multimodal interaction sessions in OHCs would be better collected through this system, as well as identifying potential preferences, qualitative indicators, quantitative indicators, and perceptions of patients through natural language processing, multimodal data analysis, and heterogeneous dynamic network mining.
Theoretical contributions.
This review highlights a significant gap in research regarding service-oriented recommendations within OHCs. While OMCs are widely used on the internet, there is a notable scarcity of corresponding research on service recommendations within these environments. Traditionally, research on OMC recommendation systems has followed the conventional e-commerce model, focusing on recommending “items” to “users” rather than customizing e-service recommendations, such as recommending “users” to “users.” This lack of focus on personalized service recommendations limits the potential for enhancing user experience within OHCs. Moreover, existing recommendation algorithms primarily focus on mining, modeling, and matching expert knowledge, neglecting the consideration of 2-sided user preferences and the workload of service providers. This oversight can result in recommendations that do not effectively cater to the needs and preferences of both service providers and consumers within OHCs.
Another crucial aspect highlighted in this review is the limited consideration given to the cognitive capabilities of service consumers. Current recommendation algorithms often fail to adequately address the issue that service consumers may lack professional cognitive capabilities. Adopting interpretable recommendation algorithms could help bridge this gap and improve the effectiveness of service recommendations within OHCs. Furthermore, this review emphasizes the importance of using consumer comments judiciously in the recommendation process. While consumer comments can provide valuable insights, they should be analyzed with caution to ensure the reliability and relevance of the recommendations generated.
In summary, research on personalized recommendations for online knowledge services within OHCs is still in its early stages, facing challenges such as the “cold start” problem and the lack of a theoretical framework or algorithm. Addressing these challenges is crucial for advancing the field and enhancing the quality of service recommendations within OHCs.
The practical implications of the review findings are 2-fold and can greatly benefit stakeholders within OHCs. First, the insights provided by this review can aid OHC stakeholders, including platform administrators and policy makers, in evaluating and optimizing the design of recommender systems. By understanding that service-oriented recommendation systems should function as 2-sided matching systems rather than just expertise retrieval systems, stakeholders can make informed decisions about system design and implementation. This understanding can lead to the promotion of policies that prioritize the consideration of 2-sided preferences, thereby enhancing user satisfaction and engagement within OHCs.
Second, the review findings can assist developers in prioritizing their work and implementing measures to address key challenges faced by OHCs. For instance, developers can focus on enhancing workload balancing for physicians by optimizing recommendation algorithms to consider both the workload of service providers and the preferences of service consumers. In addition, developers can implement measures to protect patient privacy while still providing personalized recommendations, thereby fostering trust and confidence among users.
Overall, the practical value of the review findings lies in their ability to guide stakeholders and developers in optimizing the design and functionality of recommender systems within OHCs, ultimately leading to improved user experiences and outcomes.
The primary limitation is the relatively small number of included studies, leading to less robust synthesized results. Despite a growing body of research on physician recommendations, there remains a scarcity of strictly designed OMC-oriented recommender systems. Notably, while online medical applications are widely used in China, this review excluded papers published in Chinese due to language constraints.
Recent years have seen an explosion of interest in physician recommendations, largely driven by the spread of OHCs and the success of artificial intelligence in other fields. As a result of the emergence of OMCs, an online service, physician recommendations have moved into a new age. These new-generation recommendation systems are service oriented rather than commodity oriented and build on the concept of 2-sided markets. This synergizes both patients and physicians with their needs and preferences individually, inspiring e-service recommendation thinking, vision, paradigms, approaches, and practices. This study has a distinctive pioneering character, and it is expected to open up a new branch of recommendation system theory. The e-service–oriented recommendations demonstrate their transformational, transdisciplinary, and translational features in terms of thinking, paradigms, methodologies, technologies, engineering, and practices. The paradigm shifts and directions are discussed in this paper. Unlike traditional e-commerce recommendations, e-service recommendations emphasize big-picture, outside-the-box thinking as well as data-driven, model-based, 2-sided hypotheses that pursue foundational and original recommendation thinking, theories, and practices from the essence of knowledge- and labor-intensive services inherent in the knowledge economy.
This project was supported by the Fundamental Research Funds for the Central Universities and the Research Funds of Renmin University of China (grant 23XNL017). This work was supported by the National Natural Science Foundation of China (72271233, 72071203), Suzhou Key Laboratory of Artificial Intelligence and Social Governance Technologies (SZS2023007), and Smart Social Governance Technology and Innovative Application Platform (YZCXPT2023101).
The data sets generated during and analyzed during this study are available from the corresponding author on reasonable request.
HJ and WX contributed to study conception and design. HJ and ZM collected, analyzed, and interpreted the data. HJ drafted the manuscript, and WX was responsible for its critical revision.
None declared.
PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 checklist.
collaborative filtering |
Grading of Recommendations, Assessment, Development, and Evaluations |
knowledge graph |
latent Dirichlet allocation |
online health community |
online medical consultation |
Preferred Reporting Items for Systematic Reviews and Meta-Analyses |
question and answer |
Edited by A Mavragani; submitted 10.03.23; peer-reviewed by M Mars, S Pesälä; comments to author 01.11.23; revised version received 22.12.23; accepted 21.05.24; published 30.07.24.
©Hongxun Jiang, Ziyue Mi, Wei Xu. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 30.07.2024.
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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Characteristics of research objectives. Research objectives must start with the word "To" because this helps readers identify the objective in the absence of headings and appropriate sectioning in research papers. 5,6. A good objective is SMART (mostly applicable to specific objectives): Specific—clear about the what, why, when, and how
Your research objectives may evolve slightly as your research progresses, but they should always line up with the research carried out and the actual content of your paper. Research aims. A distinction is often made between research objectives and research aims. A research aim typically refers to a broad statement indicating the general purpose ...
Examples of Specific Research Objectives: 1. "To examine the effects of rising temperatures on the yield of rice crops during the upcoming growth season.". 2. "To assess changes in rainfall patterns in major agricultural regions over the first decade of the twenty-first century (2000-2010).". 3.
Research Objectives. Research objectives refer to the specific goals or aims of a research study. They provide a clear and concise description of what the researcher hopes to achieve by conducting the research.The objectives are typically based on the research questions and hypotheses formulated at the beginning of the study and are used to guide the research process.
The research aims, objectives and research questions (collectively called the "golden thread") are arguably the most important thing you need to get right when you're crafting a research proposal, dissertation or thesis.We receive questions almost every day about this "holy trinity" of research and there's certainly a lot of confusion out there, so we've crafted this post to help ...
Research Objectives Examples in Different Fields. The application of research objectives spans various academic disciplines, each with its unique focus and methodologies. To illustrate how the objectives of the study guide a research paper across different fields, here are some research objective examples:
The writing of effective research aims and objectives can cause confusion and concern to new and experienced researchers and learners. This step in your research journey is usually the first written method used to convey your research idea to your tutor. Therefore, aims and objectives should clearly convey your topic, academic foundation, and ...
To develop a set of research objectives, you would then break down the various steps involved in meeting said aim. For example: This study will investigate the link between dehydration and the incidence of urinary tract infections (UTIs) in intensive care patients in Australia. To achieve this, the study objectives w ill include:
Steps for Writing Objectives in Research Paper. 1. Identify the Research Topic: Clearly define the subject or topic of your research. This will provide a broad context for developing specific research objectives. 2. Conduct a Literature Review. Review existing literature and research related to your topic.
Once you've decided on your research objectives, you need to explain them in your paper, at the end of your problem statement. Keep your research objectives clear and concise, and use appropriate verbs to accurately convey the work that you will carry out for each one. Example: Verbs for research objectives I will assess … I will compare …
Research objective 2: This paper implements surveys and personal interviews to determine first-hand feedback from the youth members and the team leaders. Research objective 3: Aiming to compare and contrast, this study determines the positive outcomes of the unity project work between the branches of the youth movement in Belgium, aiming for ...
Summary. One of the most important aspects of a thesis, dissertation or research paper is the correct formulation of the aims and objectives. This is because your aims and objectives will establish the scope, depth and direction that your research will ultimately take. An effective set of aims and objectives will give your research focus and ...
A research paper is a piece of academic writing that provides analysis, interpretation, and argument based on in-depth independent research. About us; Disclaimer; ... Formal and objective: Research papers are written in a formal and objective tone, with an emphasis on clarity, precision, and accuracy. They avoid subjective language or personal ...
The objectives provide a clear direction and purpose for the study, guiding the researcher in their data collection and analysis. Here are some tips on how to write effective research objective: 1. Be clear and specific. Research objective should be written in a clear and specific manner.
A research objective is defined as a clear and concise statement of the specific goals and aims of a research study. It outlines what the researcher intends to accomplish and what they hope to learn or discover through their research. Research objectives are crucial for guiding the research process and ensuring that the study stays focused and ...
5 Examples of Research Objectives. The following examples of research objectives based on several published studies on various topics demonstrate how the research objectives are written: This study aims to find out if there is a difference in quiz scores between students exposed to direct instruction and flipped classrooms (Webb and Doman, 2016).
Objectives wrapped up within that question might be: 1) the incidence of eyestrain among children who watch a lot of TV, 2) their muscular development, 3) their level of socialization with other children. Design your objectives around answering these questions. 4. Limit your objectives to 3 to 5 at most.
Here are three simple steps that you can follow to identify and write your research objectives: 1. Pinpoint the major focus of your research. The first step to writing your research objectives is to pinpoint the major focus of your research project. In this step, make sure to clearly describe what you aim to achieve through your research.
The aim of the research is the overall purpose of conducting the research. It could be to add to the knowledge in the area, to address an existing gap in the knowledge, to devise and test a solution to an existing problem, and so on. Objectives are the specific outcomes you expect to achieve through your research and that will enable you to ...
Q4. What are the common mistakes to avoid in a research proposal?. A4. Here are a few common mistakes that you must avoid while writing a research proposal. 7 . No clear objectives: Objectives should be clear, specific, and measurable for the easy understanding among readers. Incomplete or unconvincing background research: Background research usually includes a review of the current scenario ...
Table of contents. Step 1: Introduce your topic. Step 2: Describe the background. Step 3: Establish your research problem. Step 4: Specify your objective (s) Step 5: Map out your paper. Research paper introduction examples. Frequently asked questions about the research paper introduction.
Charlesworth Author Services; 23 March, 2022; Developing and framing Research Objectives. Well-developed and focused research objectives go a long way in ensuring the success of a research project. The objectives even steer and shape the way the resulting research paper is structured.This article explains how to develop and frame your research objectives.
Here are three simple steps that you can. follow to identify and write your research objectives: Pinpoint the major focus of your research. The first step to writing your research objectives is to ...
AbstractStudy Objectives. The purpose of this systematic review is to evaluate the modifiable risk factors associated with OSA and analyze extant publicati ... a thorough assessment of the abstracts and content of each of these manuscripts led to the rejection of all but four papers, the latter being included in this systematic review ...
Research Paper On Tone; Research Paper On Tone. 974 Words 4 Pages. Semester A Unit 6 Lesson 11 Introduction and Objective Mood and tone should be maintained throughout your text. While your readers won't necessarily get confused if you switch from one voice to another, it will affect their reaction to the information you're giving them ...
Get your 100% customized paper done in as little as 1 hour. Let's start. Show example. Here's how to start using our objective generator for research: Choose an action verb from the drop-down list. Enter your aim. Move the toggle if needed. Click "Generate now" and enjoy a properly formulated objective.
@article{Hu2024MultiobjectiveOA, title={Multi-objective optimization and accelerated experimental research on load distribution of planetary roller screw mechanism}, author={Rui Hu and Peitang Wei and Xuesong Du and Siqi Liu and Nansu Zhang and Li Luo and Caichao Zhu}, journal={Tribology International}, year={2024}, url={https://api ...
Background: Online health communities have given rise to a new e-service known as online medical consultations (OMCs), enabling remote interactions between physicians and patients. To address challenges, such as patient information overload and uneven distribution of physician visits, online health communities should develop OMC-oriented recommenders.