critical analysis of survey research

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21 Design and Analysis of Surveys

• Published: March 2018
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This chapter discusses the most important aspects of designing, administrating, and analyzing surveys in clinical research. It highlights the different topics of the main stages of survey research. It reviews the design and planning phase, especially focusing on the definition of the survey research question. It discusses the process of instrument design, from planning and development to pre-test and validation, types of sampling, and methods for survey administration. It also reviews the importance of the data analysis plan and report. Furthermore, it refers to potential problems and pitfalls, as well as legal and ethical issues encountered when conducting surveys, and strategies to address them. Finally, it includes a case study and explores different questions to be addressed when using surveys in clinical research.

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Methodology

• Survey Research | Definition, Examples & Methods

Survey Research | Definition, Examples & Methods

Published on August 20, 2019 by Shona McCombes . Revised on June 22, 2023.

Survey research means collecting information about a group of people by asking them questions and analyzing the results. To conduct an effective survey, follow these six steps:

• Determine who will participate in the survey
• Decide the type of survey (mail, online, or in-person)
• Design the survey questions and layout
• Distribute the survey
• Analyze the responses
• Write up the results

Surveys are a flexible method of data collection that can be used in many different types of research .

Table of contents

What are surveys used for, step 1: define the population and sample, step 2: decide on the type of survey, step 3: design the survey questions, step 4: distribute the survey and collect responses, step 5: analyze the survey results, step 6: write up the survey results, other interesting articles, frequently asked questions about surveys.

Surveys are used as a method of gathering data in many different fields. They are a good choice when you want to find out about the characteristics, preferences, opinions, or beliefs of a group of people.

Common uses of survey research include:

• Social research : investigating the experiences and characteristics of different social groups
• Market research : finding out what customers think about products, services, and companies
• Health research : collecting data from patients about symptoms and treatments
• Politics : measuring public opinion about parties and policies
• Psychology : researching personality traits, preferences and behaviours

Surveys can be used in both cross-sectional studies , where you collect data just once, and in longitudinal studies , where you survey the same sample several times over an extended period.

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Before you start conducting survey research, you should already have a clear research question that defines what you want to find out. Based on this question, you need to determine exactly who you will target to participate in the survey.

Populations

The target population is the specific group of people that you want to find out about. This group can be very broad or relatively narrow. For example:

• The population of Brazil
• US college students
• Second-generation immigrants in the Netherlands
• Customers of a specific company aged 18-24
• British transgender women over the age of 50

Your survey should aim to produce results that can be generalized to the whole population. That means you need to carefully define exactly who you want to draw conclusions about.

Several common research biases can arise if your survey is not generalizable, particularly sampling bias and selection bias . The presence of these biases have serious repercussions for the validity of your results.

It’s rarely possible to survey the entire population of your research – it would be very difficult to get a response from every person in Brazil or every college student in the US. Instead, you will usually survey a sample from the population.

The sample size depends on how big the population is. You can use an online sample calculator to work out how many responses you need.

There are many sampling methods that allow you to generalize to broad populations. In general, though, the sample should aim to be representative of the population as a whole. The larger and more representative your sample, the more valid your conclusions. Again, beware of various types of sampling bias as you design your sample, particularly self-selection bias , nonresponse bias , undercoverage bias , and survivorship bias .

There are two main types of survey:

• A questionnaire , where a list of questions is distributed by mail, online or in person, and respondents fill it out themselves.
• An interview , where the researcher asks a set of questions by phone or in person and records the responses.

Which type you choose depends on the sample size and location, as well as the focus of the research.

Questionnaires

Sending out a paper survey by mail is a common method of gathering demographic information (for example, in a government census of the population).

• You can easily access a large sample.
• You have some control over who is included in the sample (e.g. residents of a specific region).
• The response rate is often low, and at risk for biases like self-selection bias .

Online surveys are a popular choice for students doing dissertation research , due to the low cost and flexibility of this method. There are many online tools available for constructing surveys, such as SurveyMonkey and Google Forms .

• You can quickly access a large sample without constraints on time or location.
• The data is easy to process and analyze.
• The anonymity and accessibility of online surveys mean you have less control over who responds, which can lead to biases like self-selection bias .

If your research focuses on a specific location, you can distribute a written questionnaire to be completed by respondents on the spot. For example, you could approach the customers of a shopping mall or ask all students to complete a questionnaire at the end of a class.

• You can screen respondents to make sure only people in the target population are included in the sample.
• You can collect time- and location-specific data (e.g. the opinions of a store’s weekday customers).
• The sample size will be smaller, so this method is less suitable for collecting data on broad populations and is at risk for sampling bias .

Oral interviews are a useful method for smaller sample sizes. They allow you to gather more in-depth information on people’s opinions and preferences. You can conduct interviews by phone or in person.

• You have personal contact with respondents, so you know exactly who will be included in the sample in advance.
• You can clarify questions and ask for follow-up information when necessary.
• The lack of anonymity may cause respondents to answer less honestly, and there is more risk of researcher bias.

Like questionnaires, interviews can be used to collect quantitative data: the researcher records each response as a category or rating and statistically analyzes the results. But they are more commonly used to collect qualitative data : the interviewees’ full responses are transcribed and analyzed individually to gain a richer understanding of their opinions and feelings.

Next, you need to decide which questions you will ask and how you will ask them. It’s important to consider:

• The type of questions
• The content of the questions
• The phrasing of the questions
• The ordering and layout of the survey

Open-ended vs closed-ended questions

There are two main forms of survey questions: open-ended and closed-ended. Many surveys use a combination of both.

Closed-ended questions give the respondent a predetermined set of answers to choose from. A closed-ended question can include:

• A binary answer (e.g. yes/no or agree/disagree )
• A scale (e.g. a Likert scale with five points ranging from strongly agree to strongly disagree )
• A list of options with a single answer possible (e.g. age categories)
• A list of options with multiple answers possible (e.g. leisure interests)

Closed-ended questions are best for quantitative research . They provide you with numerical data that can be statistically analyzed to find patterns, trends, and correlations .

Open-ended questions are best for qualitative research. This type of question has no predetermined answers to choose from. Instead, the respondent answers in their own words.

Open questions are most common in interviews, but you can also use them in questionnaires. They are often useful as follow-up questions to ask for more detailed explanations of responses to the closed questions.

The content of the survey questions

To ensure the validity and reliability of your results, you need to carefully consider each question in the survey. All questions should be narrowly focused with enough context for the respondent to answer accurately. Avoid questions that are not directly relevant to the survey’s purpose.

When constructing closed-ended questions, ensure that the options cover all possibilities. If you include a list of options that isn’t exhaustive, you can add an “other” field.

Phrasing the survey questions

In terms of language, the survey questions should be as clear and precise as possible. Tailor the questions to your target population, keeping in mind their level of knowledge of the topic. Avoid jargon or industry-specific terminology.

Survey questions are at risk for biases like social desirability bias , the Hawthorne effect , or demand characteristics . It’s critical to use language that respondents will easily understand, and avoid words with vague or ambiguous meanings. Make sure your questions are phrased neutrally, with no indication that you’d prefer a particular answer or emotion.

Ordering the survey questions

The questions should be arranged in a logical order. Start with easy, non-sensitive, closed-ended questions that will encourage the respondent to continue.

If the survey covers several different topics or themes, group together related questions. You can divide a questionnaire into sections to help respondents understand what is being asked in each part.

If a question refers back to or depends on the answer to a previous question, they should be placed directly next to one another.

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Before you start, create a clear plan for where, when, how, and with whom you will conduct the survey. Determine in advance how many responses you require and how you will gain access to the sample.

When you are satisfied that you have created a strong research design suitable for answering your research questions, you can conduct the survey through your method of choice – by mail, online, or in person.

There are many methods of analyzing the results of your survey. First you have to process the data, usually with the help of a computer program to sort all the responses. You should also clean the data by removing incomplete or incorrectly completed responses.

If you asked open-ended questions, you will have to code the responses by assigning labels to each response and organizing them into categories or themes. You can also use more qualitative methods, such as thematic analysis , which is especially suitable for analyzing interviews.

Statistical analysis is usually conducted using programs like SPSS or Stata. The same set of survey data can be subject to many analyses.

Finally, when you have collected and analyzed all the necessary data, you will write it up as part of your thesis, dissertation , or research paper .

In the methodology section, you describe exactly how you conducted the survey. You should explain the types of questions you used, the sampling method, when and where the survey took place, and the response rate. You can include the full questionnaire as an appendix and refer to it in the text if relevant.

Then introduce the analysis by describing how you prepared the data and the statistical methods you used to analyze it. In the results section, you summarize the key results from your analysis.

In the discussion and conclusion , you give your explanations and interpretations of these results, answer your research question, and reflect on the implications and limitations of the research.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Student’s  t -distribution
• Normal distribution
• Null and Alternative Hypotheses
• Chi square tests
• Confidence interval
• Quartiles & Quantiles
• Cluster sampling
• Stratified sampling
• Data cleansing
• Reproducibility vs Replicability
• Peer review
• Prospective cohort study

Research bias

• Implicit bias
• Cognitive bias
• Placebo effect
• Hawthorne effect
• Hindsight bias
• Affect heuristic
• Social desirability bias

A questionnaire is a data collection tool or instrument, while a survey is an overarching research method that involves collecting and analyzing data from people using questionnaires.

A Likert scale is a rating scale that quantitatively assesses opinions, attitudes, or behaviors. It is made up of 4 or more questions that measure a single attitude or trait when response scores are combined.

To use a Likert scale in a survey , you present participants with Likert-type questions or statements, and a continuum of items, usually with 5 or 7 possible responses, to capture their degree of agreement.

Individual Likert-type questions are generally considered ordinal data , because the items have clear rank order, but don’t have an even distribution.

Overall Likert scale scores are sometimes treated as interval data. These scores are considered to have directionality and even spacing between them.

The type of data determines what statistical tests you should use to analyze your data.

The priorities of a research design can vary depending on the field, but you usually have to specify:

• Your research questions and/or hypotheses
• Your overall approach (e.g., qualitative or quantitative )
• The type of design you’re using (e.g., a survey , experiment , or case study )
• Your sampling methods or criteria for selecting subjects
• Your data collection methods (e.g., questionnaires , observations)
• Your data collection procedures (e.g., operationalization , timing and data management)
• Your data analysis methods (e.g., statistical tests  or thematic analysis )

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• Critically appraising...

Critically appraising qualitative research

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• Peer review
• Ayelet Kuper , assistant professor 1 ,
• Lorelei Lingard , associate professor 2 ,
• Wendy Levinson , Sir John and Lady Eaton professor and chair 3
• 1 Department of Medicine, Sunnybrook Health Sciences Centre, and Wilson Centre for Research in Education, University of Toronto, 2075 Bayview Avenue, Room HG 08, Toronto, ON, Canada M4N 3M5
• 2 Department of Paediatrics and Wilson Centre for Research in Education, University of Toronto and SickKids Learning Institute; BMO Financial Group Professor in Health Professions Education Research, University Health Network, 200 Elizabeth Street, Eaton South 1-565, Toronto
• 3 Department of Medicine, Sunnybrook Health Sciences Centre
• Correspondence to: A Kuper ayelet94{at}post.harvard.edu

Six key questions will help readers to assess qualitative research

Summary points

Appraising qualitative research is different from appraising quantitative research

Qualitative research papers should show appropriate sampling, data collection, and data analysis

Transferability of qualitative research depends on context and may be enhanced by using theory

Ethics in qualitative research goes beyond review boards’ requirements to involve complex issues of confidentiality, reflexivity, and power

Over the past decade, readers of medical journals have gained skills in critically appraising studies to determine whether the results can be trusted and applied to their own practice settings. Criteria have been designed to assess studies that use quantitative methods, and these are now in common use.

In this article we offer guidance for readers on how to assess a study that uses qualitative research methods by providing six key questions to ask when reading qualitative research (box 1). However, the thorough assessment of qualitative research is an interpretive act and requires informed reflective thought rather than the simple application of a scoring system.

Box 1 Key questions to ask when reading qualitative research studies

Was the sample used in the study appropriate to its research question.

Were the data collected appropriately?

Were the data analysed appropriately?

Can I transfer the results of this study to my own setting?

Does the study adequately address potential ethical issues, including reflexivity?

Overall: is what the researchers did clear?

One of the critical decisions in a qualitative study is whom or what to include in the sample—whom to interview, whom to observe, what texts to analyse. An understanding that qualitative research is based in experience and in the construction of meaning, combined with the specific research question, should guide the sampling process. For example, a study of the experience of survivors of domestic violence that examined their reasons for not seeking help from healthcare providers might focus on interviewing a …

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• > The Cambridge Handbook of Research Methods and Statistics for the Social and Behavioral Sciences
• > Literature Review

Book contents

• The Cambridge Handbook of Research Methods and Statistics for the Social and Behavioral Sciences
• Cambridge Handbooks in Psychology
• Copyright page
• Contributors
• Part I From Idea to Reality: The Basics of Research
• 1 Promises and Pitfalls of Theory
• 2 Research Ethics for the Social and Behavioral Sciences
• 3 Getting Good Ideas and Making the Most of Them
• 4 Literature Review
• 5 Choosing a Research Design
• 6 Building the Study
• 7 Analyzing Data
• 8 Writing the Paper
• Part II The Building Blocks of a Study
• Part III Data Collection
• Part IV Statistical Approaches
• Part V Tips for a Successful Research Career

4 - Literature Review

from Part I - From Idea to Reality: The Basics of Research

Published online by Cambridge University Press:  25 May 2023

A literature review is a survey of scholarly sources that establishes familiarity with and an understanding of current research in a particular field. It includes a critical analysis of the relationship among different works, seeking a synthesis and an explanation of gaps, while relating findings to the project at hand. It also serves as a foundational aspect of a well-grounded thesis or dissertation, reveals gaps in a specific field, and establishes credibility and need for those applying for a grant. The enormous amount of textual information necessitates the development of tools to help researchers effectively and efficiently process huge amounts of data and quickly search, classify, and assess their relevance. This chapter presents an assessable guide to writing a comprehensive review of literature. It begins with a discussion of the purpose of the literature review and then presents steps to conduct an organized, relevant review.

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• Literature Review
• By Rachel Adams Goertel
• Edited by Austin Lee Nichols , Central European University, Vienna , John Edlund , Rochester Institute of Technology, New York
• Book: The Cambridge Handbook of Research Methods and Statistics for the Social and Behavioral Sciences
• Online publication: 25 May 2023
• Chapter DOI: https://doi.org/10.1017/9781009010054.005

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Critical Appraisal of Clinical Research

Azzam al-jundi.

1 Professor, Department of Orthodontics, King Saud bin Abdul Aziz University for Health Sciences-College of Dentistry, Riyadh, Kingdom of Saudi Arabia.

Salah Sakka

2 Associate Professor, Department of Oral and Maxillofacial Surgery, Al Farabi Dental College, Riyadh, KSA.

Evidence-based practice is the integration of individual clinical expertise with the best available external clinical evidence from systematic research and patient’s values and expectations into the decision making process for patient care. It is a fundamental skill to be able to identify and appraise the best available evidence in order to integrate it with your own clinical experience and patients values. The aim of this article is to provide a robust and simple process for assessing the credibility of articles and their value to your clinical practice.

Introduction

Decisions related to patient value and care is carefully made following an essential process of integration of the best existing evidence, clinical experience and patient preference. Critical appraisal is the course of action for watchfully and systematically examining research to assess its reliability, value and relevance in order to direct professionals in their vital clinical decision making [ 1 ].

Critical appraisal is essential to:

• Combat information overload;
• Identify papers that are clinically relevant;
• Continuing Professional Development (CPD).

Carrying out Critical Appraisal:

Assessing the research methods used in the study is a prime step in its critical appraisal. This is done using checklists which are specific to the study design.

Standard Common Questions:

• What is the research question?
• What is the study type (design)?
• Selection issues.
• What are the outcome factors and how are they measured?
• What are the study factors and how are they measured?
• What important potential confounders are considered?
• What is the statistical method used in the study?
• Statistical results.
• What conclusions did the authors reach about the research question?
• Are ethical issues considered?

The Critical Appraisal starts by double checking the following main sections:

I. Overview of the paper:

• The publishing journal and the year
• The article title: Does it state key trial objectives?
• The author (s) and their institution (s)

The presence of a peer review process in journal acceptance protocols also adds robustness to the assessment criteria for research papers and hence would indicate a reduced likelihood of publication of poor quality research. Other areas to consider may include authors’ declarations of interest and potential market bias. Attention should be paid to any declared funding or the issue of a research grant, in order to check for a conflict of interest [ 2 ].

II. ABSTRACT: Reading the abstract is a quick way of getting to know the article and its purpose, major procedures and methods, main findings, and conclusions.

• Aim of the study: It should be well and clearly written.
• Materials and Methods: The study design and type of groups, type of randomization process, sample size, gender, age, and procedure rendered to each group and measuring tool(s) should be evidently mentioned.
• Results: The measured variables with their statistical analysis and significance.
• Conclusion: It must clearly answer the question of interest.

III. Introduction/Background section:

An excellent introduction will thoroughly include references to earlier work related to the area under discussion and express the importance and limitations of what is previously acknowledged [ 2 ].

-Why this study is considered necessary? What is the purpose of this study? Was the purpose identified before the study or a chance result revealed as part of ‘data searching?’

-What has been already achieved and how does this study be at variance?

-Does the scientific approach outline the advantages along with possible drawbacks associated with the intervention or observations?

IV. Methods and Materials section : Full details on how the study was actually carried out should be mentioned. Precise information is given on the study design, the population, the sample size and the interventions presented. All measurements approaches should be clearly stated [ 3 ].

V. Results section : This section should clearly reveal what actually occur to the subjects. The results might contain raw data and explain the statistical analysis. These can be shown in related tables, diagrams and graphs.

VI. Discussion section : This section should include an absolute comparison of what is already identified in the topic of interest and the clinical relevance of what has been newly established. A discussion on a possible related limitations and necessitation for further studies should also be indicated.

Does it summarize the main findings of the study and relate them to any deficiencies in the study design or problems in the conduct of the study? (This is called intention to treat analysis).

• Does it address any source of potential bias?
• Are interpretations consistent with the results?
• How are null findings interpreted?
• Does it mention how do the findings of this study relate to previous work in the area?
• Can they be generalized (external validity)?
• Does it mention their clinical implications/applicability?
• What are the results/outcomes/findings applicable to and will they affect a clinical practice?
• Does the conclusion answer the study question?
• -Is the conclusion convincing?
• -Does the paper indicate ethics approval?
• -Can you identify potential ethical issues?
• -Do the results apply to the population in which you are interested?
• -Will you use the results of the study?

Once you have answered the preliminary and key questions and identified the research method used, you can incorporate specific questions related to each method into your appraisal process or checklist.

1-What is the research question?

For a study to gain value, it should address a significant problem within the healthcare and provide new or meaningful results. Useful structure for assessing the problem addressed in the article is the Problem Intervention Comparison Outcome (PICO) method [ 3 ].

P = Patient or problem: Patient/Problem/Population:

It involves identifying if the research has a focused question. What is the chief complaint?

E.g.,: Disease status, previous ailments, current medications etc.,

I = Intervention: Appropriately and clearly stated management strategy e.g.,: new diagnostic test, treatment, adjunctive therapy etc.,

C= Comparison: A suitable control or alternative

E.g.,: specific and limited to one alternative choice.

O= Outcomes: The desired results or patient related consequences have to be identified. e.g.,: eliminating symptoms, improving function, esthetics etc.,

The clinical question determines which study designs are appropriate. There are five broad categories of clinical questions, as shown in [ Table/Fig-1 ].

[Table/Fig-1]:

Categories of clinical questions and the related study designs.

2- What is the study type (design)?

The study design of the research is fundamental to the usefulness of the study.

In a clinical paper the methodology employed to generate the results is fully explained. In general, all questions about the related clinical query, the study design, the subjects and the correlated measures to reduce bias and confounding should be adequately and thoroughly explored and answered.

Participants/Sample Population:

Researchers identify the target population they are interested in. A sample population is therefore taken and results from this sample are then generalized to the target population.

The sample should be representative of the target population from which it came. Knowing the baseline characteristics of the sample population is important because this allows researchers to see how closely the subjects match their own patients [ 4 ].

Sample size calculation (Power calculation): A trial should be large enough to have a high chance of detecting a worthwhile effect if it exists. Statisticians can work out before the trial begins how large the sample size should be in order to have a good chance of detecting a true difference between the intervention and control groups [ 5 ].

• Is the sample defined? Human, Animals (type); what population does it represent?
• Does it mention eligibility criteria with reasons?
• Does it mention where and how the sample were recruited, selected and assessed?
• Does it mention where was the study carried out?
• Is the sample size justified? Rightly calculated? Is it adequate to detect statistical and clinical significant results?
• Does it mention a suitable study design/type?
• Is the study type appropriate to the research question?
• Is the study adequately controlled? Does it mention type of randomization process? Does it mention the presence of control group or explain lack of it?
• Are the samples similar at baseline? Is sample attrition mentioned?
• All studies report the number of participants/specimens at the start of a study, together with details of how many of them completed the study and reasons for incomplete follow up if there is any.
• Does it mention who was blinded? Are the assessors and participants blind to the interventions received?
• Is it mentioned how was the data analysed?
• Are any measurements taken likely to be valid?

Researchers use measuring techniques and instruments that have been shown to be valid and reliable.

Validity refers to the extent to which a test measures what it is supposed to measure.

(the extent to which the value obtained represents the object of interest.)

• -Soundness, effectiveness of the measuring instrument;
• -What does the test measure?
• -Does it measure, what it is supposed to be measured?
• -How well, how accurately does it measure?

Reliability: In research, the term reliability means “repeatability” or “consistency”

Reliability refers to how consistent a test is on repeated measurements. It is important especially if assessments are made on different occasions and or by different examiners. Studies should state the method for assessing the reliability of any measurements taken and what the intra –examiner reliability was [ 6 ].

3-Selection issues:

The following questions should be raised:

• - How were subjects chosen or recruited? If not random, are they representative of the population?
• - Types of Blinding (Masking) Single, Double, Triple?
• - Is there a control group? How was it chosen?
• - How are patients followed up? Who are the dropouts? Why and how many are there?
• - Are the independent (predictor) and dependent (outcome) variables in the study clearly identified, defined, and measured?
• - Is there a statement about sample size issues or statistical power (especially important in negative studies)?
• - If a multicenter study, what quality assurance measures were employed to obtain consistency across sites?
• - Are there selection biases?
• • In a case-control study, if exercise habits to be compared:
• - Are the controls appropriate?
• - Were records of cases and controls reviewed blindly?
• - How were possible selection biases controlled (Prevalence bias, Admission Rate bias, Volunteer bias, Recall bias, Lead Time bias, Detection bias, etc.,)?
• • Cross Sectional Studies:
• - Was the sample selected in an appropriate manner (random, convenience, etc.,)?
• - Were efforts made to ensure a good response rate or to minimize the occurrence of missing data?
• - Were reliability (reproducibility) and validity reported?
• • In an intervention study, how were subjects recruited and assigned to groups?
• • In a cohort study, how many reached final follow-up?
• - Are the subject’s representatives of the population to which the findings are applied?
• - Is there evidence of volunteer bias? Was there adequate follow-up time?
• - What was the drop-out rate?
• - Any shortcoming in the methodology can lead to results that do not reflect the truth. If clinical practice is changed on the basis of these results, patients could be harmed.

Researchers employ a variety of techniques to make the methodology more robust, such as matching, restriction, randomization, and blinding [ 7 ].

Bias is the term used to describe an error at any stage of the study that was not due to chance. Bias leads to results in which there are a systematic deviation from the truth. As bias cannot be measured, researchers need to rely on good research design to minimize bias [ 8 ]. To minimize any bias within a study the sample population should be representative of the population. It is also imperative to consider the sample size in the study and identify if the study is adequately powered to produce statistically significant results, i.e., p-values quoted are <0.05 [ 9 ].

4-What are the outcome factors and how are they measured?

• -Are all relevant outcomes assessed?
• -Is measurement error an important source of bias?

5-What are the study factors and how are they measured?

• -Are all the relevant study factors included in the study?
• -Have the factors been measured using appropriate tools?

Data Analysis and Results:

- Were the tests appropriate for the data?

- Are confidence intervals or p-values given?

• How strong is the association between intervention and outcome?
• How precise is the estimate of the risk?
• Does it clearly mention the main finding(s) and does the data support them?
• Does it mention the clinical significance of the result?
• Is adverse event or lack of it mentioned?
• Are all relevant outcomes assessed?
• Was the sample size adequate to detect a clinically/socially significant result?
• Are the results presented in a way to help in health policy decisions?
• Is there measurement error?
• Is measurement error an important source of bias?

Confounding Factors:

A confounder has a triangular relationship with both the exposure and the outcome. However, it is not on the causal pathway. It makes it appear as if there is a direct relationship between the exposure and the outcome or it might even mask an association that would otherwise have been present [ 9 ].

6- What important potential confounders are considered?

• -Are potential confounders examined and controlled for?
• -Is confounding an important source of bias?

7- What is the statistical method in the study?

• -Are the statistical methods described appropriate to compare participants for primary and secondary outcomes?
• -Are statistical methods specified insufficient detail (If I had access to the raw data, could I reproduce the analysis)?
• -Were the tests appropriate for the data?
• -Are confidence intervals or p-values given?
• -Are results presented as absolute risk reduction as well as relative risk reduction?

Interpretation of p-value:

The p-value refers to the probability that any particular outcome would have arisen by chance. A p-value of less than 1 in 20 (p<0.05) is statistically significant.

• When p-value is less than significance level, which is usually 0.05, we often reject the null hypothesis and the result is considered to be statistically significant. Conversely, when p-value is greater than 0.05, we conclude that the result is not statistically significant and the null hypothesis is accepted.

Confidence interval:

Multiple repetition of the same trial would not yield the exact same results every time. However, on average the results would be within a certain range. A 95% confidence interval means that there is a 95% chance that the true size of effect will lie within this range.

8- Statistical results:

• -Do statistical tests answer the research question?

Are statistical tests performed and comparisons made (data searching)?

Correct statistical analysis of results is crucial to the reliability of the conclusions drawn from the research paper. Depending on the study design and sample selection method employed, observational or inferential statistical analysis may be carried out on the results of the study.

It is important to identify if this is appropriate for the study [ 9 ].

• -Was the sample size adequate to detect a clinically/socially significant result?
• -Are the results presented in a way to help in health policy decisions?

Clinical significance:

Statistical significance as shown by p-value is not the same as clinical significance. Statistical significance judges whether treatment effects are explicable as chance findings, whereas clinical significance assesses whether treatment effects are worthwhile in real life. Small improvements that are statistically significant might not result in any meaningful improvement clinically. The following questions should always be on mind:

• -If the results are statistically significant, do they also have clinical significance?
• -If the results are not statistically significant, was the sample size sufficiently large to detect a meaningful difference or effect?

9- What conclusions did the authors reach about the study question?

Conclusions should ensure that recommendations stated are suitable for the results attained within the capacity of the study. The authors should also concentrate on the limitations in the study and their effects on the outcomes and the proposed suggestions for future studies [ 10 ].

• -Are the questions posed in the study adequately addressed?
• -Are the conclusions justified by the data?
• -Do the authors extrapolate beyond the data?
• -Are shortcomings of the study addressed and constructive suggestions given for future research?
• -Bibliography/References:

Do the citations follow one of the Council of Biological Editors’ (CBE) standard formats?

10- Are ethical issues considered?

If a study involves human subjects, human tissues, or animals, was approval from appropriate institutional or governmental entities obtained? [ 10 , 11 ].

Critical appraisal of RCTs: Factors to look for:

• Allocation (randomization, stratification, confounders).
• Follow up of participants (intention to treat).
• Data collection (bias).
• Sample size (power calculation).
• Presentation of results (clear, precise).
• Applicability to local population.

[ Table/Fig-2 ] summarizes the guidelines for Consolidated Standards of Reporting Trials CONSORT [ 12 ].

[Table/Fig-2]:

Summary of the CONSORT guidelines.

Critical appraisal of systematic reviews: provide an overview of all primary studies on a topic and try to obtain an overall picture of the results.

In a systematic review, all the primary studies identified are critically appraised and only the best ones are selected. A meta-analysis (i.e., a statistical analysis) of the results from selected studies may be included. Factors to look for:

• Literature search (did it include published and unpublished materials as well as non-English language studies? Was personal contact with experts sought?).
• Quality-control of studies included (type of study; scoring system used to rate studies; analysis performed by at least two experts).
• Homogeneity of studies.

[ Table/Fig-3 ] summarizes the guidelines for Preferred Reporting Items for Systematic reviews and Meta-Analyses PRISMA [ 13 ].

[Table/Fig-3]:

Summary of PRISMA guidelines.

Critical appraisal is a fundamental skill in modern practice for assessing the value of clinical researches and providing an indication of their relevance to the profession. It is a skills-set developed throughout a professional career that facilitates this and, through integration with clinical experience and patient preference, permits the practice of evidence based medicine and dentistry. By following a systematic approach, such evidence can be considered and applied to clinical practice.

Financial or other Competing Interests

• Literature Searching
• Steps in Conducting a Literature Search
• 1 Reflection on Research Question
• Primary and Secondary Sources
• Hierarchy of Evidence
• Keyword Identification
• Boolean Search Operators
• Subject Headings, Thesaurus and MeSH
• 4 Conducting Search

Critical Appraisal

• Critical Appraisal Tools
• 6 Documenting Search
• Literature Reviews

What is Critical Appraisal?

Critical appraisal is the process of carefully and systematically examining research to judge its trustworthiness, value and relevance in a particular context. It is an essential skill for evidence-based practice as it allows people to find and use research evidence reliably and efficiently.

Key steps in critical appraisal:

1. Thoroughly understanding the research, including its aims, methodology, results and conclusions, while being aware of any limitations or potential bias.

2. Using a framework or checklist to provide structure and ensure all key points are considered. This allows you to record your reasoning behind decisions based on the research.

3. Identifying the research methods, such as study design, sample size, and data collection and analysis techniques, to assess validity and reliability.

4. Checking the results and conclusions to ensure they are justified by the data and not unduly influenced by bias.

5. Determining the relevance and applicability of the research findings to your specific context or question.

Critical appraisal skills are important as they enable you to systematically and objectively assess published papers, regardless of where they are published or who wrote them. It is crucial to avoid being misled by poor quality research and ensure that any findings used as evidence can reliably improve practice.

What are Critical Appraisal Tools?

Critical appraisal tools are instruments or checklists used to assess the methodological quality, validity, and relevance of published research studies. They provide a structured framework to evaluate various aspects of a study, such as the study design, sampling methods, data collection, statistical analysis, ethical considerations, and applicability of the results.

Key Points About Critical Appraisal Tools

They aim to assess the trustworthiness, relevance, and results of published papers by examining different components of the research process.

The content and criteria assessed by these tools can vary significantly, as there is a lack of consensus on the essential items for critical appraisal.

Many tools are study design-specific, evaluating different aspects for randomized controlled trials, observational studies, qualitative research, systematic reviews, and other study types.

Common elements appraised include sampling methods, internal validity, control of confounding factors, ethical conduct, statistical analysis, and generalizability of results.

Some tools provide an overall quality rating (e.g. high, medium, low) based on the individual item assessments.

The empirical basis for the construction and validation of many critical appraisal tools is often lacking, with limited evidence of their reliability and validity.

In summary, critical appraisal tools are structured instruments that aim to evaluate the methodological rigor and quality of research studies. They assess various aspects of the research process, but their content and criteria can vary widely due to the lack of consensus on essential items and empirical validation.

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Home » Critical Analysis – Types, Examples and Writing Guide

Critical Analysis – Types, Examples and Writing Guide

Table of Contents

Critical Analysis

Definition:

Critical analysis is a process of examining a piece of work or an idea in a systematic, objective, and analytical way. It involves breaking down complex ideas, concepts, or arguments into smaller, more manageable parts to understand them better.

Types of Critical Analysis

Types of Critical Analysis are as follows:

Literary Analysis

This type of analysis focuses on analyzing and interpreting works of literature , such as novels, poetry, plays, etc. The analysis involves examining the literary devices used in the work, such as symbolism, imagery, and metaphor, and how they contribute to the overall meaning of the work.

Film Analysis

This type of analysis involves examining and interpreting films, including their themes, cinematography, editing, and sound. Film analysis can also include evaluating the director’s style and how it contributes to the overall message of the film.

Art Analysis

This type of analysis involves examining and interpreting works of art , such as paintings, sculptures, and installations. The analysis involves examining the elements of the artwork, such as color, composition, and technique, and how they contribute to the overall meaning of the work.

Cultural Analysis

This type of analysis involves examining and interpreting cultural artifacts , such as advertisements, popular music, and social media posts. The analysis involves examining the cultural context of the artifact and how it reflects and shapes cultural values, beliefs, and norms.

Historical Analysis

This type of analysis involves examining and interpreting historical documents , such as diaries, letters, and government records. The analysis involves examining the historical context of the document and how it reflects the social, political, and cultural attitudes of the time.

Philosophical Analysis

This type of analysis involves examining and interpreting philosophical texts and ideas, such as the works of philosophers and their arguments. The analysis involves evaluating the logical consistency of the arguments and assessing the validity and soundness of the conclusions.

Scientific Analysis

This type of analysis involves examining and interpreting scientific research studies and their findings. The analysis involves evaluating the methods used in the study, the data collected, and the conclusions drawn, and assessing their reliability and validity.

Critical Discourse Analysis

This type of analysis involves examining and interpreting language use in social and political contexts. The analysis involves evaluating the power dynamics and social relationships conveyed through language use and how they shape discourse and social reality.

Comparative Analysis

This type of analysis involves examining and interpreting multiple texts or works of art and comparing them to each other. The analysis involves evaluating the similarities and differences between the texts and how they contribute to understanding the themes and meanings conveyed.

Critical Analysis Format

Critical Analysis Format is as follows:

I. Introduction

• Provide a brief overview of the text, object, or event being analyzed
• Explain the purpose of the analysis and its significance
• Provide background information on the context and relevant historical or cultural factors

II. Description

• Provide a detailed description of the text, object, or event being analyzed
• Identify key themes, ideas, and arguments presented
• Describe the author or creator’s style, tone, and use of language or visual elements

III. Analysis

• Analyze the text, object, or event using critical thinking skills
• Identify the main strengths and weaknesses of the argument or presentation
• Evaluate the reliability and validity of the evidence presented
• Assess any assumptions or biases that may be present in the text, object, or event
• Consider the implications of the argument or presentation for different audiences and contexts

IV. Evaluation

• Provide an overall evaluation of the text, object, or event based on the analysis
• Assess the effectiveness of the argument or presentation in achieving its intended purpose
• Identify any limitations or gaps in the argument or presentation
• Consider any alternative viewpoints or interpretations that could be presented
• Summarize the main points of the analysis and evaluation
• Reiterate the significance of the text, object, or event and its relevance to broader issues or debates
• Provide any recommendations for further research or future developments in the field.

VI. Example

• Provide an example or two to support your analysis and evaluation
• Use quotes or specific details from the text, object, or event to support your claims
• Analyze the example(s) using critical thinking skills and explain how they relate to your overall argument

VII. Conclusion

• Reiterate your thesis statement and summarize your main points
• Provide a final evaluation of the text, object, or event based on your analysis
• Offer recommendations for future research or further developments in the field
• End with a thought-provoking statement or question that encourages the reader to think more deeply about the topic

How to Write Critical Analysis

Writing a critical analysis involves evaluating and interpreting a text, such as a book, article, or film, and expressing your opinion about its quality and significance. Here are some steps you can follow to write a critical analysis:

• Read and re-read the text: Before you begin writing, make sure you have a good understanding of the text. Read it several times and take notes on the key points, themes, and arguments.
• Identify the author’s purpose and audience: Consider why the author wrote the text and who the intended audience is. This can help you evaluate whether the author achieved their goals and whether the text is effective in reaching its audience.
• Analyze the structure and style: Look at the organization of the text and the author’s writing style. Consider how these elements contribute to the overall meaning of the text.
• Evaluate the content : Analyze the author’s arguments, evidence, and conclusions. Consider whether they are logical, convincing, and supported by the evidence presented in the text.
• Consider the context: Think about the historical, cultural, and social context in which the text was written. This can help you understand the author’s perspective and the significance of the text.
• Develop your thesis statement : Based on your analysis, develop a clear and concise thesis statement that summarizes your overall evaluation of the text.
• Support your thesis: Use evidence from the text to support your thesis statement. This can include direct quotes, paraphrases, and examples from the text.
• Write the introduction, body, and conclusion : Organize your analysis into an introduction that provides context and presents your thesis, a body that presents your evidence and analysis, and a conclusion that summarizes your main points and restates your thesis.
• Revise and edit: After you have written your analysis, revise and edit it to ensure that your writing is clear, concise, and well-organized. Check for spelling and grammar errors, and make sure that your analysis is logically sound and supported by evidence.

When to Write Critical Analysis

You may want to write a critical analysis in the following situations:

• Academic Assignments: If you are a student, you may be assigned to write a critical analysis as a part of your coursework. This could include analyzing a piece of literature, a historical event, or a scientific paper.
• Journalism and Media: As a journalist or media person, you may need to write a critical analysis of current events, political speeches, or media coverage.
• Personal Interest: If you are interested in a particular topic, you may want to write a critical analysis to gain a deeper understanding of it. For example, you may want to analyze the themes and motifs in a novel or film that you enjoyed.
• Professional Development : Professionals such as writers, scholars, and researchers often write critical analyses to gain insights into their field of study or work.

Critical Analysis Example

An Example of Critical Analysis Could be as follow:

Research Topic:

The Impact of Online Learning on Student Performance

Introduction:

The introduction of the research topic is clear and provides an overview of the issue. However, it could benefit from providing more background information on the prevalence of online learning and its potential impact on student performance.

Literature Review:

The literature review is comprehensive and well-structured. It covers a broad range of studies that have examined the relationship between online learning and student performance. However, it could benefit from including more recent studies and providing a more critical analysis of the existing literature.

Research Methods:

The research methods are clearly described and appropriate for the research question. The study uses a quasi-experimental design to compare the performance of students who took an online course with those who took the same course in a traditional classroom setting. However, the study may benefit from using a randomized controlled trial design to reduce potential confounding factors.

The results are presented in a clear and concise manner. The study finds that students who took the online course performed similarly to those who took the traditional course. However, the study only measures performance on one course and may not be generalizable to other courses or contexts.

Discussion :

The discussion section provides a thorough analysis of the study’s findings. The authors acknowledge the limitations of the study and provide suggestions for future research. However, they could benefit from discussing potential mechanisms underlying the relationship between online learning and student performance.

Conclusion :

The conclusion summarizes the main findings of the study and provides some implications for future research and practice. However, it could benefit from providing more specific recommendations for implementing online learning programs in educational settings.

Purpose of Critical Analysis

There are several purposes of critical analysis, including:

• To identify and evaluate arguments : Critical analysis helps to identify the main arguments in a piece of writing or speech and evaluate their strengths and weaknesses. This enables the reader to form their own opinion and make informed decisions.
• To assess evidence : Critical analysis involves examining the evidence presented in a text or speech and evaluating its quality and relevance to the argument. This helps to determine the credibility of the claims being made.
• To recognize biases and assumptions : Critical analysis helps to identify any biases or assumptions that may be present in the argument, and evaluate how these affect the credibility of the argument.
• To develop critical thinking skills: Critical analysis helps to develop the ability to think critically, evaluate information objectively, and make reasoned judgments based on evidence.
• To improve communication skills: Critical analysis involves carefully reading and listening to information, evaluating it, and expressing one’s own opinion in a clear and concise manner. This helps to improve communication skills and the ability to express ideas effectively.

Importance of Critical Analysis

Here are some specific reasons why critical analysis is important:

• Helps to identify biases: Critical analysis helps individuals to recognize their own biases and assumptions, as well as the biases of others. By being aware of biases, individuals can better evaluate the credibility and reliability of information.
• Enhances problem-solving skills : Critical analysis encourages individuals to question assumptions and consider multiple perspectives, which can lead to creative problem-solving and innovation.
• Promotes better decision-making: By carefully evaluating evidence and arguments, critical analysis can help individuals make more informed and effective decisions.
• Facilitates understanding: Critical analysis helps individuals to understand complex issues and ideas by breaking them down into smaller parts and evaluating them separately.
• Fosters intellectual growth : Engaging in critical analysis challenges individuals to think deeply and critically, which can lead to intellectual growth and development.

Advantages of Critical Analysis

Some advantages of critical analysis include:

• Improved decision-making: Critical analysis helps individuals make informed decisions by evaluating all available information and considering various perspectives.
• Enhanced problem-solving skills : Critical analysis requires individuals to identify and analyze the root cause of a problem, which can help develop effective solutions.
• Increased creativity : Critical analysis encourages individuals to think outside the box and consider alternative solutions to problems, which can lead to more creative and innovative ideas.
• Improved communication : Critical analysis helps individuals communicate their ideas and opinions more effectively by providing logical and coherent arguments.
• Reduced bias: Critical analysis requires individuals to evaluate information objectively, which can help reduce personal biases and subjective opinions.
• Better understanding of complex issues : Critical analysis helps individuals to understand complex issues by breaking them down into smaller parts, examining each part and understanding how they fit together.
• Greater self-awareness: Critical analysis helps individuals to recognize their own biases, assumptions, and limitations, which can lead to personal growth and development.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Absorptive capacity for science-based innovation propensity: an empirical analysis using Japanese National Innovation Survey

• Open access
• Published: 04 September 2024

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• Kazuyuki Motohashi   ORCID: orcid.org/0000-0003-0985-5331 1 , 2 , 3 ,
• Kenta Ikeuchi 2 , 3 &
• Akira Yamaguchi 3  

It is found that private firms are withdrawing from basic research, as evidenced by the decline in the number of scientific papers written by authors who are inside of firms. On the other hand, scientific knowledge is becoming increasingly important in the industrial innovation process, so that accessing external scientific knowledge for science based innovation becomes critical to radical innovation at firm. This study conducts an empirical analysis of absorptive capacity required to realize radical innovations based on scientific knowledge. Specifically, by connecting bibliographic information of academic papers and patents with firm data from the Japanese National Innovation Survey, we analyze the type of absorptive capacity by the way to obtain external knowledge. The results show that internal R&D is important both for with and without collaboration with university, while human capital with experiences of scientific research in university is important, particularly for successful innovation through university collaboration.

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1 Introduction

Looking at the R&D activities of U.S. companies over the long term, there is a tendency for firms to withdraw from basic research and focus on downstream development activities. The reasons for this include the selection and focus of large companies (focusing on application development in specific fields rather than basic research), the rise of emerging countries such as China and intensifying competition (short-term orientation toward innovation), and the division of roles between universities and other public research institutions responsible for basic research (Arora et al., 2021 ). The decline in the number of scientific by corporate authors is found also in Japan (Nishikawa et al., 2021 ).

At the same time, however, scientific knowledge becomes increasingly important in the industrial innovation process, particularly for radical innovation (Marx & Fuegi, 2020 ). The increasing importance of science is observed in an interview survey, conducted for large corporations in Japan (Motohashi et al., 2012 ) as well. This trend is particularly evident in high-tech sectors such as IT and life sciences. The development of digital technology and the expansion of the Internet have revolutionized the innovation process. In the field of AI, corporate researchers are publishing papers and filing patent applications simultaneously (Hartmann et al., 2020). Human genome analysis has also significantly changed the R&D process for pharmaceuticals. It has become necessary for major pharmaceutical manufacturers to incorporate the latest scientific findings into their R&D by collaborating with university-launched ventures and other organizations (Motohashi, 2009 ).

The scientific knowledge can be a source of novel and radical innovation (Mention, 2011 ; Veugelers & Wang, 2019 ), so that the incorporating external knowledge into its innovation process becomes an important management issue at firm. However, a challenge for science based innovation entails a significant risk for companies (Arora et al., 2016 ). First, scientific findings only show that something is possible in principle, but do not always provide sufficient proof of concept to be materialized into a viable product or technology. In addition, it is uncertain whether the new product can be accepted by the market if it is a novel product with new features.

The complementary internal resources at firm to overcome significant technology and market risks, or “absorptive capacity” is important for successful innovation. The absorptive capacity is defined as “ability to recognize the value of new information, assimilate it, and apply it to commercial ends” (Cohen & Levinthal, 1990 ). The original concept has been substantially articulated in the variety of literature (Zahra & George, 2002 ; Lane et al., 2006 ; Tadorova and Durisin, 2007), and some studies applies the concept to understand how to manage external knowledge in science based innovation (Belderbos et al., 2016 ; Bishop et al., 2011 ; Cassiman et al., 2018 ; Kobarg et al., 2018 ; Melnychuk et al., 2021 ). While the internal R&D investment have been used as a typical proxy of absorptive capability (Cohen & Levinthal, 1990 ), in the literature analyzing university industry collaboration, the role of scientific talents to absorb external scientific knowledge is also investigated (Bishop et al., 2011 ; Kobarg et al., 2018 ; Melnychuk et al., 2021 ). Cassiman et at. ( 2018 ) looks into the role of mobile talents from university to firm as a bridging role between academic and industry.

Many of these studies use a patent indicator as an output of innovation. Therefore, their focus is the potential absorptive capacity, such as valuation and acquisition of external knowledge source (Zahra & George, 2002 ; Tadorova and Durisin, 2007). In contrast, the absorptive capacity in latter stage of innovation, such as assimilation/transformation and exploitation, has not been analyzed enough. One exception is Kobarg et al. ( 2018 ), which use the data from German Innovation Survey, and look into the relationship between university industry (UI) collaboration and the type of product/process innovation (incremental vs radical). In this analysis, the type of absorptive capacity (internal R&D and scientific talents) is identified in the relationship between collaboration with university and the innovation performance.

We extend this work by using the national innovation survey of Japan (NISTEP, 2018 ) to incorporate the information of product innovation at firm, linked with research paper and patent information to measure the sources of scientific knowledge. This paper separates the way to acquire external scientific knowledge between collaboration with university and codified knowledge in research paper. Then we test the fitness of two types of absorptive capacity, internal R&D investment and human resources with scientific knowledge, to each of these two types of external knowledge sources.

In the following section below, we discuss the existing literature of absorptive capacity in science based innovation and develop the hypotheses to be tested in our empirical analysis. Then, we explain our dataset used in the empirical analysis in Sect.  3 . Section  4 presents the empirical results based on our regression models. Then, we discuss about the results in Chapter 5. Finally, we conclude this paper by summarizes the entire study and draw some management and policy implications, together with the limitations of our study and potential future works.

2 Related literature and hypotheses

2.1 scientific knowledge as a source of radical innovation.

The realization of product innovation by firms can be categorized as either radical or incremental innovation. Radical or incremental innovation is primarily determined by the extent to which the innovation output, i.e., the new product or service, differs from existing offerings (Chandy & Tellis, 2000 ). Science-based innovations are initiated by new scientific knowledge. Typically, the revision of a scientific principle will result in a radical improvement of an existing technology, ultimately resulting in a radical innovation that differs significantly from existing products (Mention, 2011 ). In other words, technological exploration is generally considered to precede the establishment of a valid product technology that can be commercialized (establishment of proof of concept), followed by the process of market exploration (development of new customers or identification of needs different from those of existing products among existing customers) and exploration (determination of final product specifications) (Danneels, 2002 ).

Scientific findings are often generated by universities and other public research institutions engaged in scientific, technological, and academic activities (Aghion & Tirole, 1994 ). Public research institutions such as universities do not conduct research and development for profit, while academic competition (competitiveness within academic fields) is evaluated based on the novelty of research results (Hicks, 2012). Therefore, research results from universities and other institutions based on public funds are likely to be highly novel (Amara & Landry, 2005 ; Nieto and Santamaria, 2007 ). Scientific research activities are also conducted by companies, although they make up a small percentage of total corporate R&D investment. In addition, compared to R&D solely conducted by for-profit companies, projects utilizing industry-academia collaborations are more likely to target at novel innovations (Laursen and Salter, 2006 ).

2.2 Absorptive capacity for science based innovation

Innovations based on scientific prior art tend to be technologically radical and new to the market, but the process of such science based innovation to be materialized as a new product is long and risky (Arora et al., 2016 ). In particular, on the technical side, proof of concept is needed to incorporate scientific expertise into commercial technology. With this, a process of combining existing technological assets, along with information about the market, is necessary for the final product development. Each of these phases requires greater amount of absorptive capacity within the firm (Belderbos et al., 2016 ; Kobarg et al., 2018 ).

First, human capital with both an understanding of scientific expertise and knowledge of product development are needed to transform scientific knowledge into technologies that can be commercialized (Murovec and Pordan, 2009 ; Kobarg et al., 2018 ). Since experience in both scientific research and R&D within a company is required, specifically, these are in-house development employees with experience in conducting academic research at universities, or in-house scientists engaged in scientific activities within the company (Cassiman et al., 2018 ). These in-house personnel play an important role in interpreting scientific knowledge obtained through industry-academia collaborations from the perspective of industrial technology, exploring the possibility of using the findings as physical products.

In addition, to incorporate and commercialize external scientific findings as a technological asset, a firm needs to assimilate and transform external knowledge into the product development process inside the firm (Cohen & Levinthal, 1990 ). A science based innovation requires substantial development activities, such as experiments and market tests due to the newness of its final product. Therefore, to realize such innovation, a firm needs to have substantial technology stock, through active R&D investments (Murovec and Prodan, 2009 ; Kobarg et. al, 2018 ).

2.3 Type of external scientific knowledge and absorptive capacity

The type of absorptive capacity varies by external knowledge (Lane et al., 2006 ). Here we discuss about two types of external scientific knowledge. One is a general codified knowledge, mainly through public information sources such as research papers, academic conferences, and internet sources. The other one is the one obtained by collaborative activities with university. A university industry collaboration involves substantial interactions across two parties, so that the firm can obtain private and tacit knowledge through joint research activities with university researchers.

In both cases, some external scientific knowledge is fed into the innovation process at firm, but the difference is that major activities of whole innovation process is conducted within the firm in the former case, while the R&D activities are jointly conducted with university in the latter case. According to the standard phase model of absorptive capacity (Zahra & George, 2002 ; Todorova and Durisin, 2007), the assimilation and transformation of external knowledge are conducted jointly in the case of university collaboration, instead of internally in the case of acquiring codified scientific knowledge through research papers.

The internal technological capability, achieved by substantial R&D investment, is an essential resource, or absorptive capability in both cases, since the firm has to make a relatively long process of science based innovation complete. This is particularly relevant in the case of acquiring codified scientific knowledge though general information sources such as research papers and conferences, where the firm must have technological capability to assess external scientific knowledge for its proper acquisition, to assimilate and to transform it into the firm’s innovation process for new product. In case of collaboration with university, a firm can rely on university for some part of research activity, but the product development for the market is mainly a task for the firm, so that a substantial R&D investment should be incurred to the whole process, as well. Therefore,

Hypothesis 1

Larger R&D investment is required to achieve radical innovation based on external scientific knowledge both in the cases of with and without university collaboration.

In the case of university collaboration, while a firm can share the burden for knowledge generation with its counterpart university, the firm should cross the institutional difference across two parties (Gittelman & Kogut, 2003 ). There are substantial differences between a private firm for profit and non profit research organization such as university. In the collaborative R&D project, such institutional difference entails a risk of conflicts in terms of the directions and the objectives of joint activities. Under such environment, a person who knows both environments is important as a bridging role between firm and university (Cassiman et al., 2018 ; Kobarg et al., 2018 ). In contrast, in the case of using just a codified scientific knowledge in research papers and not involving joint R&D with university, such organizational difficulty does not exist. Since all of science based innovation process are managed inside a firm, so that the firm does not face the difficulty to fill the gap across different types of institutions. Therefore

Hypothesis 2

Higher numbers of internal staffs with experience of scientific research at university are required to achieve radical innovation based on university collaboration, but it is not the case for science based innovation by using codified knowledge in research papers.

3 Data and descriptive statistics

3.1 dataset.

This paper uses the dataset linking the results of the Japan’s National Innovation Survey in 2015 (NISTEP, 2018 ), the patent information of Japan Patent Office and the research paper information of Clarivate Web of Science. In order to determine the internal scientific activities by firm, we utilize the results of matching paper authors and patent inventors along with the results of measuring content similarity using textual information of paper abstracts and patent abstracts (Ikeuchi & Motohashi, 2019 ; Motohashi et al., 2024 ). We use the patents whose contents are similar to those of any scientific research paper, as a proxy of innovation build upon codified scientific knowledge in research papers. In addition, we identify firm’s activity of university collaboration by joint patent invention with university researchers.

This patent information is linked with the Japanese National Innovation Survey (J-NIS) in 2015 (NISTEP, 2018 ) at firm level. For this linkage, we use the information of the firm name and its location both in patent applicant and the survey respondent of J-NIS. The patent applicant name and address is obtained by using the IIP Patent Database (Goto & Motohashi, 2007 ), and conduct fuzzy matching of company name with the survey respondent is conducted. After extracting potential pairs of matched names (with the Levenstein distance of 0.75 threshold), we used the location information (geocoding information from the addresses) to select the matched pair, by selecting the pairs located geographically close each other. Footnote 1

The reference period for the National Innovation Survey in 2015 (4th National Innovation Survey) is the innovation activities from 2012 to 2014, and patents filed in the three years prior to the reference period (2009–2011) are used for our empirical analysis. The patent information is aggregated at the firm level, then the cross section data with 602 observations are created.

3.2 Dependent variable

As for the measurement of radicalness of product innovation, we use the type of innovation, i.e., “no innovation realized,” “new innovation to the firm,” “new innovation to the market,” and “new innovation to the world,” from the National Innovation Survey 2015. The dependent variable in our empirical models is a categorical variable shown in Table  1 (categorical variable with cardinal order information).

3.3 Independent variables

The independent variables of our interest are two kinds of indicators of external use of scientific knowledge, the one through university collaboration and the others through codified information in research papers. We use “academic inventors (ratio)” and “number of academic patent citations” as a proxy for the former one. Academic inventors (ratio)" is the ratio of the number of patents co-invented by researchers affiliated with universities or public research institutions to the number of patents applied by the firm. This indicates the degree of the firm’s commitment to industry-academia collaboration activities. On the other hand, the “number of academic patent citations” is the average number of academic patents (invented by the researchers belonging to universities or public research institutions) cited by the patents held by a company. Since the citation to university patents is likely to occur by collaboration with the university, this indicator also reflects the degree of firm’s involvement in collaborative R&D with university. We use these two variables to make sure the robustness of our results. These two kinds of indicator for each patent are aggregated into a firm level by taking the average over all patents applied by the firm.

As for the latter one, we use the indicator of the patent, whose contents are similar to any of research papers. Here, we use the results of text analysis of all patents and research papers to identify similar documents to each of patent or research paper (Motohashi et al., 2024 ). Specifically, the number of research papers within top 10 (50 or 100) nearest documents (research papers or patents) for each patent is counted. The content of each focal patent is compared to all documents (the other patents and research papers), and the number of research papers within the top 10 nearest content documents can be between 0 (no research paper, all patents) and 10 (all research paper, no patents). The number of research papers in top 10 (or 50, 100) indicates the degree of closeness of the contents of focal patent to any scientific findings expressed in research papers. Therefore, such indicator can be used as a proxy of firm’s relying on codified scientific knowledge as a research paper. This patent based indicators are also aggregated as a firm level by taking the average over all patents applied by the firm.

We use two kinds of indicators for absorptive capacity. One is internal R&D investment, and the other is the staff with experience of research activities at university. For the former one, we use the natural logarithm of R&D investment per employee (log of R&D per capita). For the latter one, the "percentage of employees with postgraduate degrees" is used, since such employees have graduate level research experience for post graduate degree at university.

Control variables in the model are the number of employees (log), the R&D per employee (log of R&D plus 1 divided by number of employees), percentage of employees with graduate degrees, overseas sales dummy Footnote 2 and industry dummy variables (69 categories).

Table 2 shows the basic statistics, together with Table 3 as the correlation matrix of these variables.

4 Empirical analysis

This section presents the results of an ordered probit analysis using indicators of radical innovation as the dependent variable with variety of explanatory variables, described above. First, the results are shown where no absorptive capacity variable is included. Next, we present the results using both R&D per capita and the percentage of employees with postgraduate degrees as cross terms for absorptive capacity. Finally, we present an analysis using two kinds of absorptive capacity separately as a robustness check.

Table 4 shows the estimated coefficients of the ordered probit regression analysis of radical innovations when the absorptive capacity is not considered. It is found that there is no statistically significant effect on radical innovation for any of use of external scientific knowledge, either in the case of university collaboration (number of citations of academic inventors and academic patents) or the case of codified knowledge in research paper (the number of papers in the top 10, 50, and 100 similar documents). The results reconfirm the fact that an absorptive capacity is important for a firm to make successful radical innovation by using external scientific knowledge.

Table 5 shows the results when two measures of absorptive capacity, R&D spend per employee and the percentage of employees with postgraduate degrees, are used as cross terms with use of external scientific knowledge. The cross terms in the models (5)–(7) for academia inventors (ratio) and the percentage of postgraduates and R&D per capita are significantly positive. For the number of neighboring papers, the single term and the cross term with R&D per capita are significantly positive in the models (5)–(8). In the model (8), the cross term between the number of academic patent citations and the percentage of graduate school graduates is significantly positive. The number of employees as a control variable and the amount of R&D per capita themselves are also significantly positive.

First, the R&D expenditures per employee, the complementarity with both types of external scientific knowledge, codified science knowledge (the numbers of neighbor papers) and collaboration with university (co-invention patents with university and university patent citation) is found, so that the hypothesis 1 is supported.

Second, it is found that the availability of scientific research personnel is complement to collaboration with university for radical innovation, but such relationship is not found for codified scientific knowledge, so that the hypothesis 2 is supported. Footnote 3

The robustness check for the results in Table  5 is supplied by using two kinds of absorptive capacity separately. First, Table  6 shows the results when only R&D per capita is treated as a cross term of absorptive capacity. It is found that the cross terms with both codified scientific knowledge and collaboration with university is positive and statistically significant, which supports the hypothesis 1. The coefficients for the single terms of number of employees and R&D per capita are significant as is the case in Table  5 . However, the single term for academic inventors (ratio), which was not significant in Table  5 , becomes to be significantly positive in Table  6 .

Second, the results for the percentage of postgraduates used as absorptive capacity are presented in Table  7 , showing the cross term with collaboration with university is positive and statistically significant, but that with codified scientific knowledge is not statistically significant. This is consistent with the results in Table  5 , supporting the hypothesis 2.

5 Discussion

It is found that the type of absorptive capacity is different by the way that external scientific knowledge is acquired in our analysis. That is, a talent with experience of scientific research at university is required to make collaboration with university successful, but it is not the case in using codified scientific knowledge in research paper without collaborative activities with university. In contrast, the R&D investment is important both in the case of collaboration with university and using codified scientific knowledge.

Our foregoing argument is based on the assumption that research papers contains some elements of scientific breakthroughs, leading to radical innovation. However, it should be noted that some research papers have more application orientation, which give some practical advices to achieve incremental innovation. Such heterogeneity of research paper can explain that the single term of codified scientific knowledge (the number of neighbor papers) does not get statistically significant coefficient, to our dependent variable, the degree of radicalness of product innovation. However, we have robust results of statistically significant coefficients to the cross term of R&D investment, which would reflect the fact that substantial internal efforts are required to achieve radical innovation based on breakthrough scientific findings.

The similar argument can be applied to collaborative activities with university. The university collaborative project does not always target at radical innovation, but could be seeking for incremental innovation. Kobarg et al. ( 2018 ) analyze two types of absorptive capability (in-house R&D and advanced research personnel) for collaborative R&D with university, and shows the findings that in-house R&D investment (in-house technology stock) is complementary for radical innovation, but substitutive for incremental innovation, as it impedes technology absorption from universities. On the other hand, it shows that advanced research personnel are complementary in terms of innovation through industry-academia collaboration, regardless of the type of innovation (radical or incremental).

Our results are consistent with Kobarg et al. ( 2018 ) for in-house R&D, since it is found that substantial R&D is required more for radical innovation, as compare to less radical innovation. However, we also found that the similar results for personnel with research experience in university, while Kobarg et al. ( 2018 ) shows advanced research personnel is important both for radical and incremental innovation, implying its impact on radicalness is not clear. Such inconsistency may come from our study’s looking at a slightly different aspect of human capital, as a bridging role across different types of organizations (for profit firm and non profit research organization). Such personnel is more important for university collaboration targeting radical innovation based on basic science at university, as compared to the project for incremental innovation with a help of university knowledge. In this sense, it is important to distinguish the skill of bridging two different types of organizations from research ability with her own scientific research ability. Our empirical model picks up the former skill set more, so that it does not play an important role in the case of accessing to only codified scientific knowledge without university collaboration.

6 Conclusion

While there have been various studies on science-based inventions using citations to scientific papers in patents and the novelty of such patents (Veugelers & Wang, 2019 ; Cassiman et al., 2018 ), few has analyzed their relationship with the output of innovations such as new products. With this regards, our contribution is to present the empirical evidences on the types of absorptive capability for the whole process of innovation, by using product innovation information in the national innovation survey.

We also look into the fitness of types of absorptive capacity to the way of acquiring external scientific knowledge, though codified knowledge in research paper or collaboration with university. Specifically, we have found that internal R&D investment is important capability to realize radical innovation by using both types of scientific knowledge. In contrast, the personnel with post graduate degree is important for collaboration with university, but the bridging role between a firm and a university is not required for using codified scientific knowledge in research papers.

While a firm is withdrawing from basic science and seek for collaboration with university to fill the blanks on average, such firm should be aware that having a talent with bridging an institutional gap between firm and university is critical to achieve radical innovation though such collaboration. Such talent is supposed to have some scientific research experience a university, being employed as a freshly graduated from university or a mobile researcher from university to firm (Cassiman et al., 2018 ).

As well as this management implications, our study suggests to policy makers on the importance of absorptive capacity to promote open innovation. In Japan, an open innovation at firm, including collaboration with university, is promoted by R&D tax measures (Ikeuchi, 2022 ). In order to promote radical science based innovation at firm, the government should consider some policy assisting a firm to hire the talent with post graduate degree, preferably phd level with substantial research activities at university.

While our study sheds new light on the type of absorptive capacity for science based innovation, there are some limitations. First, our study is based on a cross section dataset, where it is difficult to show the causal relationship. We use the time difference between dependent variable and independent variables to identify the direction of causality, but such treatment may not be enough. Unfortunately, the national innovation survey in Japan has been conducted for the respondents randomly sampled in each shot, which does not allow to us to construct a panel dataset. Therefore, we hope some further research to be conducted by using community innovation survey in other countries.

The second limitation is that our empirical setting does not test the importance of “bridging talent” directly. We use a researcher with post graduate degree as a proxy for such ability. However, such researcher is a person who has advanced scientific knowledge as well, so that she might not be involved in university industry collaboration, but work for just in-house R&D project. Whether working with university or not, can be identified by looking at co-author (co-inventor) of research paper (patent) information. So such researcher level study could be our next step for further understanding the necessary skills to make science based innovation successful.

This was done using the "CSV Address Matching Service" of the Center for Spatial Information Science at the University of Tokyo ( https://geocode.csis.u-tokyo.ac.jp/home/csv-admatch/ ). The nearby location here is those pairs with less than 0.15 degree of the sum of the differences in terms of latitude and longitude.

Since one of the innovation types includes "new to the world," this can be controlled to reflect whether or not the company in question is expanding its business to overseas market.

The robustness checks with a different industrial classification system (13 categories) and with sub samples by picking up the industries with no less than 5 observations (under the current detail classification system) are conducted. In addition, the robustness check as regards to a potential bias associated with the firm size heterogeneity are conducted (redoing the same regressions for the sub samples with firms with 2 or more, 3 or more and 5 or more patents). Then we have almost robust estimate results in all regressions. The results for this robustness checks will be supplied upon request to the correspondent author.

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Motohashi, K., Ikeuchi, K. & Yamaguchi, A. Absorptive capacity for science-based innovation propensity: an empirical analysis using Japanese National Innovation Survey. J Technol Transf (2024). https://doi.org/10.1007/s10961-024-10138-x

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Leadership support and satisfaction of healthcare professionals in China’s leading hospitals: a cross-sectional study

• Jinhong Zhao 1 , 2 ,
• Tingfang Liu 2 &
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BMC Health Services Research volume  24 , Article number:  1016 ( 2024 ) Cite this article

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Healthcare professionals’ job satisfaction is a critical indicator of healthcare performance, pivotal in addressing challenges such as hospital quality outcomes, patient satisfaction, and staff retention rates. Existing evidence underscores the significant influence of healthcare leadership on job satisfaction. Our study aims to assess the impact of leadership support on the satisfaction of healthcare professionals, including physicians, nurses, and administrative staff, in China’s leading hospitals.

A cross-sectional survey study was conducted on healthcare professionals in three leading hospitals in China from July to December 2021. These hospitals represent three regions in China with varying levels of social and economic development, one in the eastern region, one in the central region, and the third in the western region. Within each hospital, we employed a convenience sampling method to conduct a questionnaire survey involving 487 healthcare professionals. We assessed perceived leadership support across five dimensions: resource support, environmental support, decision support, research support, and innovation encouragement. Simultaneously, we measured satisfaction using the MSQ among healthcare professionals.

The overall satisfaction rate among surveyed healthcare professionals was 74.33%. Our study revealed significant support from senior leadership in hospitals for encouraging research (96.92%), inspiring innovation (96.30%), and fostering a positive work environment (93.63%). However, lower levels of support were perceived in decision-making (81.72%) and resource allocation (80.08%). Using binary logistic regression with satisfaction as the dependent variable and healthcare professionals’ perceived leadership support, hospital origin, job role, department, gender, age, education level, and professional designation as independent variables, the results indicated that support in resource provision (OR: 4.312, 95% CI: 2.412  ∼  7.710) and environmental facilitation (OR: 4.052, 95% CI: 1.134  ∼  14.471) significantly enhances healthcare personnel satisfaction.

The findings underscore the critical role of leadership support in enhancing job satisfaction among healthcare professionals. For hospital administrators and policymakers, the study highlights the need to focus on three key dimensions: providing adequate resources, creating a supportive environment, and involving healthcare professionals in decision-making processes.

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Introduction

In the era of accelerated globalization, the investigation of global leadership has assumed heightened significance [ 1 ]. Leadership, as a dynamic and evolving process, holds the potential to cultivate both the personal and professional growth of followers [ 2 ]. Effective healthcare leadership can enhance medical service quality, patient safety, and staff job satisfaction through skill development, vision establishment, and clear direction-setting [ 3 , 4 , 5 ]. Moreover, leadership support can effectively enhance staff well-being and work efficiency [ 6 , 7 ]. For example, Mendes et al. found that the quality of healthcare is significantly influenced by four dimensions of leadership: communication, recognition, development, and innovation [ 8 ]. Additionally, Shanafelt et al. discovered that leaders can effectively reduce employee burnout and subsequently improve the quality of medical services by formulating and implementing targeted work interventions and motivating employees [ 9 ].

Job satisfaction among healthcare professionals is a crucial indicator of healthcare performance, playing a vital role in addressing challenges related to hospital quality outcomes, patient satisfaction, and nurse retention rates [ 10 , 11 , 12 , 13 ]. Researchers from different national backgrounds have conducted studies on the job satisfaction of healthcare workers across various disciplines. For example, Balasubramanian et al. examined the satisfaction of immigrant dentists in Australia [ 14 ], Mascari et al. studied physicians and hospital researchers in the United States [ 15 ], and Rosta et al. investigated the satisfaction of doctors in Norway [ 12 ]. Research has demonstrated that characteristics of the work environment, balanced workloads, relationships with colleagues, career opportunities, and leadership support all influence job satisfaction [ 16 ]. Several instruments are commonly used to measure job satisfaction, each relevant depending on the context and discipline. For instance, the Job Descriptive Index (JDI) focuses on different facets of job satisfaction such as work, pay, promotion, supervision, and co-workers [ 17 ]. The Job Satisfaction Survey (JSS) covers similar dimensions and is particularly useful in public sector organizations due to its comprehensive nature and ease of use [ 18 ]. The Minnesota Satisfaction Questionnaire (MSQ) is a comprehensive tool that assesses employee satisfaction across multiple dimensions including intrinsic and extrinsic satisfaction, and is commonly used for evaluating job satisfaction in the healthcare field [ 19 ].

Recent studies have linked leadership to healthcare professionals’ job satisfaction, highlighting the pivotal role of leadership in guiding, coordinating, and motivating employees [ 5 ]. For instance, the Mayo Clinic found that leadership from immediate supervisors could alleviate burnout and increase job satisfaction [ 20 ]. Choi’s research indicated that leadership empowerment significantly enhances nursing staff’s job satisfaction [ 21 ]. Additionally, Liu discovered that the support provided by hospital senior leadership is closely associated with employee satisfaction [ 22 ].

In China, while leadership research has gained some traction in areas such as business and education, it remains relatively scarce within healthcare institutions. Existing studies primarily focus on the nursing sector, and comprehensive assessments of leadership at the leading public hospitals (top 10% of Chinese hospitals) have not been extensively conducted [ 23 , 24 ]. Research on leadership and healthcare professionals’ satisfaction often relies on single indicators to measure job satisfaction, such as overall job satisfaction or specific aspects like compensation satisfaction and burnout levels [ 25 ]. This narrow focus may fail to fully capture the multidimensional nature of employee satisfaction, which includes aspects such as workload, ability utilization, sense of achievement, initiative, training and self-development, and interpersonal communication [ 26 ]. Additionally, most existing studies focus on the job satisfaction of nurses or physicians in isolation, lacking comparative research across different groups within healthcare institutions, such as doctors, nurses, and administrative personnel [ 27 , 28 , 29 ].

Therefore, this study utilized the MSQ to conduct a thorough assessment of employee satisfaction and assess the impact of leadership support on the satisfaction of healthcare personnel in China’s leading public hospitals. Through this research, we aim to enhance the core competitiveness of hospitals and provide valuable data to support leadership assessments in developing countries’ healthcare institutions. Moreover, this study seeks to contribute to the broader international understanding of effective leadership practices in China’s leading public hospitals, with implications for global health management strategies.

Study design and participants

From July to December 2021, a cross-sectional survey study was conducted on healthcare professionals in China’s 3 leading hospitals. The 3 leading hospitals represent three regions in China with different levels of social and economic development, one in the eastern, one in the central, and one in the western. In each hospital, a convenience sampling method was used to conduct a questionnaire survey among physicians, nurses, and administrative staff.

Criteria for inclusion of healthcare professionals: (1) employed at the hospital for at least 1 year or more; (2) formal employees of the hospital (full-time staff); (3) possessing cognitive clarity and the ability to independently understand and respond to electronic questionnaires, as assessed by their leaders. Exclusion criteria: (1) diagnosed with mental health disorders that impair their ability to participate, as identified by the hospital’s mental health professionals; (2) unable to communicate effectively due to severe language barriers, hearing impairments, or other communication disorders, as determined by their direct supervisors or relevant medical evaluations; (3) visiting scholars, interns, or graduate students currently enrolled in a degree program.

Instrument development

Leadership support.

In reference to the Malcolm Baldrige National Quality Award (MBNQA) framework and Supporting Relationship Theory [ 6 , 30 , 31 ], we determined the survey scale after three expert discussions involving 5–7 individuals. These experts included personnel from health administrative departments, leading public hospital leaders, middle management, and researchers specializing in hospital management. Their collective expertise ensured that the survey comprehensively assessed leadership support within hospitals from the perspective of healthcare personnel. The Leadership Support Scale consists of 5 items: Environmental Support: ‘My leaders provide a work environment that helps me perform my job,’ Resource Support: ‘My leaders provide the resources needed to improve my work,’ Decision Support: ‘My leaders support my decisions to satisfy patients,’ Research Support: ‘My leaders support my application for scientific research projects,’ and Innovation Encouragement: ‘My leaders encourage me to innovate actively and think about problems in new ways‘ (Supplementary material). All questionnaire items are rated on a 5-point Likert scale, ranging from 1 = Strongly Disagree to 5 = Strongly Agree. The Cronbach’s alpha coefficient for the 5-item scale is 0.753.

Job satisfaction

The measurement of job satisfaction was carried out using the Minnesota Satisfaction Questionnaire (MSQ) [ 32 , 33 ], which has been widely used and has been shown by scholars to have good reliability and validity in China [ 34 , 35 ]. The questionnaire consists of 20 items that measure healthcare personnel’s satisfaction with various aspects of their job, including individual job load, ability utilization, achievement, initiative, hospital training and self-development, authority, hospital policies and practices, compensation, teamwork, creativity, independence, moral standards, hospital rewards and punishments, personal responsibility, job security, social service contribution, social status, employee relations and communication, and hospital working conditions and environment. Responses to these items were balanced and rated on a scale from 1 to 5, with 1 = Very Dissatisfied, 2 = Dissatisfied, 3 = Neither Dissatisfied nor Satisfied, 4 = Satisfied, and 5 = Very Satisfied. Scores range from 20 to 100, with higher scores indicating higher satisfaction. In this study, a comprehensive assessment of healthcare personnel’s job satisfaction was made using a score of 80 and above [ 32 ], where a score of ≥ 80 was considered satisfied, and below 80 was considered dissatisfied. The Cronbach’s alpha coefficient for the questionnaire in this survey was 0.983.

Investigation process

The survey was administered through an online platform “Wenjuanxing”, and distributed by department heads to healthcare professionals within their respective departments. The selection of departments and potential participants followed a structured process: (1) Potential participants were identified based on the inclusion criteria, which were communicated to the department heads. (2) Department heads received a digital link to the survey, which they forwarded to eligible staff members via email or internal communication platforms. (3) The informed consent form was integrated into the survey link, detailing the research objectives, ensuring anonymity, and emphasizing voluntary participation. At the beginning of the online survey, participants were asked if they agreed to participate. Those who consented continued with the survey, while those who did not agree were directed to end the survey immediately.

According to Kendall’s experience and methodology, the sample size can be 5–10 times the number of independent variables (40 items) [ 36 , 37 ]. Our sample size is ten times the number of independent variables. Considering potentially disqualified questionnaires, the sample size was increased by 10%, resulting in a minimum total sample size of 460. Therefore, we distributed 500 survey questionnaires.

Data analysis

We summarized the sociodemographic characteristics of healthcare personnel survey samples using descriptive statistical methods. For all variables, we calculated the frequencies and percentages of categorical variables. Different sociodemographic characteristics in relation to healthcare personnel’s perception of leadership support and satisfaction were analyzed using the Pearson χ² test. We employed a binary logistic regression model to estimate the risk ratio of healthcare personnel satisfaction under different levels of leadership support. Estimates from three sequentially adjusted models were reported to transparently demonstrate the impact of various adjustments: (1) unadjusted; (2) adjusted for hospital of origin; (3) adjusted for hospital of origin, gender, age, education level, job type, and department. For the binary logistic regression model, we employed a backward stepwise regression approach, with inclusion at P  < 0.05 and exclusion at P  > 0.10 criteria. In all analyses, a two-tailed p -value of < 0.05 was considered significant, and all analyses were conducted using SPSS 26.0 (IBM Corp., Armonk, NY, USA).

Demographic characteristics and job satisfaction

This study recruited a total of 500 healthcare personnel from hospitals to participate in the survey, with 487 valid questionnaires collected, resulting in an effective response rate of 97.4%. The majority of participants were female (77.21%), with ages concentrated between 30 and 49 years old (73.71%). The predominant job titles were mid-level (45.17%) and junior-level (27.31%), and educational backgrounds were mostly at the undergraduate (45.17%) and graduate (48.25%) levels. The marital status of most participants was married (79.88%), and their primary departments were surgery (38.19%) and internal medicine (24.85%). The overall satisfaction rate among the sampled healthcare personnel was 74.33%. Differences in satisfaction were statistically significant among healthcare personnel of different genders, ages, educational levels, job types, hospitals, and departments ( P  < 0.05). Table  1 displays the participants’ demographic characteristics and job satisfaction.

By analyzed the satisfaction level of healthcare personnel in different dimensions, the results show that “Social service” (94.3%) and “Moral values” (92.0%) have the highest satisfaction. “Activity” (66.8%) and “Compensation” (71.9%) were the least satisfied. Table  2 shows participants’ job satisfaction in different dimensions.

Perception of different types of leadership support among healthcare professionals

Overall, surveyed healthcare personnel perceived significant levels of support from hospital leadership for research encouragement (96.92%), innovation inspiration (96.30%), and the work environment (93.63%), while perceiving lower levels of support for decision-making (81.72%) and resource allocation (80.08%). Female healthcare personnel perceived significantly higher levels of resource support compared to males ( P  < 0.05). Healthcare personnel in the 30–39 age group perceived significantly higher levels of resource, environmental, and research support compared to other age groups ( P  < 0.05). Healthcare personnel with senior-level job titles perceived significantly lower levels of resource and decision-making support compared to associate-level and lower job titles, and those with doctoral degrees perceived significantly lower levels of resource support compared to other educational backgrounds ( P  < 0.05).

Clinical doctors perceived significantly lower levels of resource and environmental support compared to administrative personnel and clinical nurses, while administrative personnel perceived significantly lower levels of decision-making support compared to clinical doctors and clinical nurses ( P  < 0.05). Among healthcare personnel in internal medicine, perceptions of resource, environmental, research, and innovation support were significantly lower than those in surgery, administration, and other departments, whereas perceptions of decision-making support in administrative departments were significantly lower than in internal medicine, surgery, and other departments ( P  < 0.05). Figure  1 displays the perception of leadership support among healthcare personnel with different demographic characteristics.

Perception of leadership support among healthcare professionals with different demographic characteristics in China’s leading public hospitals (* indicates P  < 0.05, ** indicates P  < 0.01, and *** indicates P  < 0.001.)

The impact of leadership support on job satisfaction among healthcare professionals

The study results indicate that healthcare personnel who perceive that their leaders provide sufficient resource, environmental, and decision-making support have significantly higher job satisfaction than those who feel that leaders have not provided enough support ( P  < 0.05). Similarly, healthcare personnel who perceive that their leaders provide sufficient research and innovation inspiration have significantly higher job satisfaction than those who believe leaders have not provided enough inspiration ( P  < 0.05). Table  3 displays the univariate analysis of leadership support on healthcare professional satisfaction.

With healthcare personnel satisfaction as the dependent variable, leadership resource support, environmental support, decision-making support, research support, and innovation inspiration were included in the binary logistic regression model. After adjusting for hospital, gender, age, education level, job type, and department, leadership’s increased resource support (OR: 4.312, 95% CI: 2.412  ∼  7.710) and environmental support (OR: 4.052, 95% CI: 1.134  ∼  14.471) were found to enhance the satisfaction levels of healthcare personnel significantly. Additionally, healthcare professionals in Hospital 2 (OR: 3.654, 95% CI: 1.796 to 7.435) and Hospital 3 (OR: 2.354, 95% CI: 1.099 to 5.038) exhibited higher levels of satisfaction compared to those in Hospital 1. Table 4 displays the binary Logistic regression analysis of leadership support on satisfaction among healthcare professionals.

This study aimed to determine the impact of support from hospital senior leadership on the job satisfaction of healthcare personnel and to explore the effects of demographic and different types of support on the job satisfaction of healthcare personnel in China. The research indicates that hospital leadership’s resource support, environmental support, and decision-making support have a significantly positive impact on the job satisfaction of healthcare personnel. These forms of support can assist healthcare personnel in better adapting to the constantly changing work environment and demands, thereby enhancing their job satisfaction, and ultimately, positively influencing the overall performance of the hospital and the quality of patient care.

Our research indicates that, using the same MSQ to measure job satisfaction, the job satisfaction among healthcare personnel in China’s top-tier hospitals is at 74.33%, which is higher than the results of a nationwide survey in 2016 (48.22%) [ 38 ] and a survey among doctors in Shanghai in 2013 (35.2%) in China [ 39 ]. This improvement is likely due to the Chinese government’s recent focus on healthcare personnel’s compensation and benefits, along with corresponding improvement measures, which have increased their job satisfaction. It’s worth noting that while job satisfaction among healthcare personnel in China’s top-tier hospitals is higher than the national average in China, it is slightly lower than the job satisfaction of doctors in the United States, as measured by the MSQ (81.73%) [ 40 ]. However, when compared to the job satisfaction by the MSQ of doctors in Southern Nigeria (26.7%) [ 32 ], nurses in South Korea (65.89%) [ 41 ], and nurses in Iran (59.7%) [ 42 ], the level of job satisfaction among healthcare personnel in China’s top-tier hospitals is significantly higher. This suggests that China has achieved some level of success in improving healthcare personnel’s job satisfaction. Studies have shown that for healthcare professionals, job satisfaction is influenced by work conditions, compensation, and opportunities for promotion, with varying levels of satisfaction observed across different cultural backgrounds and specialties [ 29 , 43 ]. Furthermore, the observed differences in job satisfaction levels can be influenced by cultural factors unique to China, including hierarchical workplace structures and the emphasis on collective well-being over individual recognition.

Leadership support can influence employees’ work attitudes and emotions. Effective leaders can establish a positive work environment, and provide constructive feedback, thereby enhancing employee job satisfaction [ 44 , 45 ]. Our research results show that clinical physicians perceive significantly lower levels of resource and environmental support compared to administrative staff and clinical nurses, while administrative staff perceive significantly lower levels of decision-making support compared to clinical physicians and clinical nurses. This difference can be attributed to their different roles and job nature within the healthcare team [ 9 ]. Nurses typically have direct patient care responsibilities, performing medical procedures, providing care, and monitoring patient conditions, making them in greater need of resource and environmental support to efficiently deliver high-quality care [ 46 ]. Doctors usually have responsibilities for clinical diagnosis and treatment, requiring better healthcare environments and resources due to their serious commitment to patients’ lives. Administrative staff often oversee the hospital’s day-to-day operations and management, including budgeting, resource allocation, and personnel management. Their work may be more organizationally oriented, involving strategic planning and management decisions. Therefore, they may require more decision-making support to succeed at the managerial level [ 47 ].

The job satisfaction of healthcare personnel is influenced by various factors, including the work environment, workload, career development, and leadership support [ 48 , 49 ]. When healthcare personnel are satisfied with their work, their job enthusiasm increases, contributing to higher patient satisfaction. Healthcare organizations should assess the leadership and management qualities of each hospital to enhance their leadership capabilities. This will directly impact employee satisfaction, retention rates, and patient satisfaction [ 50 ]. Resource support provided by leaders, such as data, human resources, financial resources, equipment resources, supplies (such as medications), and training opportunities, significantly influences the job satisfaction of healthcare personnel [ 51 ]. From a theoretical perspective, researchers believe that leaders’ behavior, by providing resources to followers, is one of the primary ways to influence employee satisfaction [ 7 ]. These resources can assist healthcare personnel in better fulfilling their job responsibilities, improving work efficiency, and thereby enhancing their job satisfaction.

In hospital organizations, leaders play a crucial role in shaping the work environment for healthcare personnel and providing decision-making support [ 52 , 53 ]. Hospital leaders are committed to ensuring the safety of the work environment for their employees by formulating and promoting policies and regulations. They also play a key role in actively identifying and addressing issues in the work environment, including conflicts among employees and resource shortages. These initiatives are aimed at continuously improving working conditions, enabling healthcare personnel to better fulfill their duties [ 54 ]. The actions of these leaders not only contribute to improving the job satisfaction of healthcare personnel but also create the necessary foundation for providing high-quality healthcare services.

It is worth noting that our research results show that in the context of leading public hospitals in China, leadership support for research, encouragement of innovation, and decision-making do not appear to significantly enhance the job satisfaction of healthcare personnel, which differs from some international literature [ 23 , 55 , 56 ]. International studies often suggest that fostering innovation is particularly important in influencing healthcare personnel’s job satisfaction [ 57 , 58 ]. Inspiring a shared vision is particularly important in motivating nursing staff and enhancing their job satisfaction and organizational commitment [ 59 ]. This may reflect the Chinese healthcare personnel’s perception of leadership’s innovation encouragement, scientific research encouragement, and decision support, but it does not significantly improve their job satisfaction. However, material support (resources and environment) can significantly increase their satisfaction.

Strengths and limitations of this study

For the first time, we analyzed the role of perceived leadership support in enhancing healthcare providers in China’s leading public hospitals. We assessed the impact of perceived leadership on healthcare professional satisfaction across five dimensions: resources, environment, decision-making, research, and innovation. The sample includes physicians, nurses, and administrative staff, providing a comprehensive understanding of leadership support’s impact on diverse positions and professional groups.

However, it’s important to note that this study exclusively recruited healthcare professionals from three leading public hospitals in China, limiting the generalizability of the research findings. Additionally, the cross-sectional nature of the study means that causality cannot be established. There is also a potential for response bias as the data were collected through self-reported questionnaires. Furthermore, the use of convenience sampling may introduce selection bias, and the reliance on electronic questionnaires may exclude those less comfortable with digital technology.

Implications for research and practice

The results of this study provide important empirical evidence supporting the significance of leadership assessment in the context of Chinese hospitals. Specifically, the findings underscore the critical role of leadership support in enhancing job satisfaction among healthcare professionals, which has implications for hospital operational efficiency and the quality of patient care. For hospital administrators and policymakers, the study highlights the need to prioritize leadership development programs that focus on the three dimensions of leadership support: resources, environment, and decision-making. Implementing targeted interventions in these areas can lead to improved job satisfaction. Moreover, this study serves as a foundation for comparative research across different cultural and organizational contexts, contributing to a deeper understanding of how leadership practices can be optimized to meet the unique needs of healthcare professionals in various regions.

Our study found a close positive correlation between leadership support in Chinese leading public hospitals and employee job satisfaction. They achieve this by providing ample resources to ensure employees can effectively fulfill their job responsibilities. Furthermore, they create a comfortable work environment and encourage active employee participation. By nurturing outstanding leadership and support, hospitals can enhance employee job satisfaction, leading to improved overall performance and service quality. This is crucial for providing high-quality healthcare and meeting patient needs.

Data availability

Data are available upon reasonable request.

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This study was funded by the Fundamental Research Funds for the Central Universities (2020-RC630-001), the Fundamental Research Funds for the Central Universities (3332022166), and the Chinese Academy of Medical Sciences (CAMS) Innovation Fund for Medical Sciences (2021-I2M-1-046).

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JZ, TL, and YL designed the study. JZ collected the original data in China, reviewed the literature, performed the analyses, and wrote the first draft of the manuscript. TL and YL critically revised the manuscript. All authors contributed to the interpretation of data and the final approved version.

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This study was conducted according to the guidelines of the Declaration of Helsinki and was approved by the Chinese Academy of Medical Sciences & Peking Union Medical College Institutional Review Board (CAMS & PUMC-IRC-2020-026). The survey was distributed by department heads and included informed consent and survey materials. The informed consent form described the research objectives, assured anonymity, emphasized voluntary participation, and instructed participants to complete the questionnaire through the online system. The statement ‘No signature is required, completing the survey implies consent to participate in the study’ implies implied consent.

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Zhao, J., Liu, T. & Liu, Y. Leadership support and satisfaction of healthcare professionals in China’s leading hospitals: a cross-sectional study. BMC Health Serv Res 24 , 1016 (2024). https://doi.org/10.1186/s12913-024-11449-3

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Received : 07 January 2024

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DOI : https://doi.org/10.1186/s12913-024-11449-3

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