is qualitative research empirical

La Salle University

Connelly library, library main menu.

• Course Reserves
• Interlibrary Loan (ILL)
• Study Room Use & Reservations
• Technology & Printing
• Citation Guides
• Reserve Library Space
• Request Instruction
• Copyright Information
• Guides for Faculty

Special Collections

• University Archives
• Historical & Cultural Collections
• Rare Bibles & Prayer Books
• Historical Research Guides
• Information & Guidelines
• Staff Directory
• Meet with a Librarian
• Directions & Building Maps

Research Hub

• Research Tools
• Research Guides

Qualitative and Quantitative Research

What is "empirical research".

• empirical research
• Locating Articles in Cinahl and PsycInfo
• Locating Articles in PubMed
• Getting the Articles

Empirical research  is based on observed and measured phenomena and derives knowledge from actual experience rather than from theory or belief. 

How do you know if a study is empirical? Read the subheadings within the article, book, or report and look for a description of the research "methodology."  Ask yourself: Could I recreate this study and test these results?

Key characteristics to look for:

• Specific research questions  to be answered
• Definition of the  population, behavior, or   phenomena  being studied
• Description of the  process  used to study this population or phenomena, including selection criteria, controls, and testing instruments (such as surveys)

Another hint: some scholarly journals use a specific layout, called the "IMRaD" format, to communicate empirical research findings. Such articles typically have 4 components:

• Introduction : sometimes called "literature review" -- what is currently known about the topic -- usually includes a theoretical framework and/or discussion of previous studies
• Methodology:  sometimes called "research design" --  how to recreate the study -- usually describes the population, research process, and analytical tools
• Results : sometimes called "findings"  --  what was learned through the study -- usually appears as statistical data or as substantial quotations from research participants
• Discussion : sometimes called "conclusion" or "implications" -- why the study is important -- usually describes how the research results influence professional practices or future studies
• << Previous: Home
• Next: Locating Articles in Cinahl and PsycInfo >>

Chat Assistance

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, generate accurate citations for free.

• Knowledge Base

Methodology

• What Is Qualitative Research? | Methods & Examples

What Is Qualitative Research? | Methods & Examples

Published on June 19, 2020 by Pritha Bhandari . Revised on June 22, 2023.

Qualitative research involves collecting and analyzing non-numerical data (e.g., text, video, or audio) to understand concepts, opinions, or experiences. It can be used to gather in-depth insights into a problem or generate new ideas for research.

Qualitative research is the opposite of quantitative research , which involves collecting and analyzing numerical data for statistical analysis.

Qualitative research is commonly used in the humanities and social sciences, in subjects such as anthropology, sociology, education, health sciences, history, etc.

• How does social media shape body image in teenagers?
• How do children and adults interpret healthy eating in the UK?
• What factors influence employee retention in a large organization?
• How is anxiety experienced around the world?
• How can teachers integrate social issues into science curriculums?

Table of contents

Approaches to qualitative research, qualitative research methods, qualitative data analysis, advantages of qualitative research, disadvantages of qualitative research, other interesting articles, frequently asked questions about qualitative research.

Qualitative research is used to understand how people experience the world. While there are many approaches to qualitative research, they tend to be flexible and focus on retaining rich meaning when interpreting data.

Common approaches include grounded theory, ethnography , action research , phenomenological research, and narrative research. They share some similarities, but emphasize different aims and perspectives.

Note that qualitative research is at risk for certain research biases including the Hawthorne effect , observer bias , recall bias , and social desirability bias . While not always totally avoidable, awareness of potential biases as you collect and analyze your data can prevent them from impacting your work too much.

Here's why students love Scribbr's proofreading services

Discover proofreading & editing

Each of the research approaches involve using one or more data collection methods . These are some of the most common qualitative methods:

• Observations: recording what you have seen, heard, or encountered in detailed field notes.
• Interviews:  personally asking people questions in one-on-one conversations.
• Focus groups: asking questions and generating discussion among a group of people.
• Surveys : distributing questionnaires with open-ended questions.
• Secondary research: collecting existing data in the form of texts, images, audio or video recordings, etc.
• You take field notes with observations and reflect on your own experiences of the company culture.
• You distribute open-ended surveys to employees across all the company’s offices by email to find out if the culture varies across locations.
• You conduct in-depth interviews with employees in your office to learn about their experiences and perspectives in greater detail.

Qualitative researchers often consider themselves “instruments” in research because all observations, interpretations and analyses are filtered through their own personal lens.

For this reason, when writing up your methodology for qualitative research, it’s important to reflect on your approach and to thoroughly explain the choices you made in collecting and analyzing the data.

Qualitative data can take the form of texts, photos, videos and audio. For example, you might be working with interview transcripts, survey responses, fieldnotes, or recordings from natural settings.

Most types of qualitative data analysis share the same five steps:

• Prepare and organize your data. This may mean transcribing interviews or typing up fieldnotes.
• Review and explore your data. Examine the data for patterns or repeated ideas that emerge.
• Develop a data coding system. Based on your initial ideas, establish a set of codes that you can apply to categorize your data.
• Assign codes to the data. For example, in qualitative survey analysis, this may mean going through each participant’s responses and tagging them with codes in a spreadsheet. As you go through your data, you can create new codes to add to your system if necessary.
• Identify recurring themes. Link codes together into cohesive, overarching themes.

There are several specific approaches to analyzing qualitative data. Although these methods share similar processes, they emphasize different concepts.

Qualitative research often tries to preserve the voice and perspective of participants and can be adjusted as new research questions arise. Qualitative research is good for:

• Flexibility

The data collection and analysis process can be adapted as new ideas or patterns emerge. They are not rigidly decided beforehand.

• Natural settings

Data collection occurs in real-world contexts or in naturalistic ways.

• Meaningful insights

Detailed descriptions of people’s experiences, feelings and perceptions can be used in designing, testing or improving systems or products.

• Generation of new ideas

Open-ended responses mean that researchers can uncover novel problems or opportunities that they wouldn’t have thought of otherwise.

Researchers must consider practical and theoretical limitations in analyzing and interpreting their data. Qualitative research suffers from:

• Unreliability

The real-world setting often makes qualitative research unreliable because of uncontrolled factors that affect the data.

• Subjectivity

Due to the researcher’s primary role in analyzing and interpreting data, qualitative research cannot be replicated . The researcher decides what is important and what is irrelevant in data analysis, so interpretations of the same data can vary greatly.

• Limited generalizability

Small samples are often used to gather detailed data about specific contexts. Despite rigorous analysis procedures, it is difficult to draw generalizable conclusions because the data may be biased and unrepresentative of the wider population .

• Labor-intensive

Although software can be used to manage and record large amounts of text, data analysis often has to be checked or performed manually.

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.

• Chi square goodness of fit test
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Inclusion and exclusion criteria

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

There are five common approaches to qualitative research :

• Grounded theory involves collecting data in order to develop new theories.
• Ethnography involves immersing yourself in a group or organization to understand its culture.
• Narrative research involves interpreting stories to understand how people make sense of their experiences and perceptions.
• Phenomenological research involves investigating phenomena through people’s lived experiences.
• Action research links theory and practice in several cycles to drive innovative changes.

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.

There are various approaches to qualitative data analysis , but they all share five steps in common:

• Prepare and organize your data.
• Review and explore your data.
• Develop a data coding system.
• Assign codes to the data.
• Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.

Bhandari, P. (2023, June 22). What Is Qualitative Research? | Methods & Examples. Scribbr. Retrieved September 6, 2024, from https://www.scribbr.com/methodology/qualitative-research/

Is this article helpful?

Pritha Bhandari

Other students also liked, qualitative vs. quantitative research | differences, examples & methods, how to do thematic analysis | step-by-step guide & examples, what is your plagiarism score.

Canvas | University | Ask a Librarian

• Library Homepage
• Arrendale Library

Empirical & Non-Empirical Research

• Empirical Research

Introduction: What is Empirical Research?

Quantitative methods, qualitative methods.

• Quantitative vs. Qualitative
• Reference Works for Social Sciences Research
• What is Non-Empirical Research?
• Contact Us!

 Call us at 706-776-0111

  Chat with a Librarian

  Send Us Email

  Library Hours

Empirical research  is based on phenomena that can be observed and measured. Empirical research derives knowledge from actual experience rather than from theory or belief. 

Key characteristics of empirical research include:

• Specific research questions to be answered;
• Definitions of the population, behavior, or phenomena being studied;
• Description of the methodology or research design used to study this population or phenomena, including selection criteria, controls, and testing instruments (such as surveys);
• Two basic research processes or methods in empirical research: quantitative methods and qualitative methods (see the rest of the guide for more about these methods).

(based on the original from the Connelly LIbrary of LaSalle University)

Empirical Research: Qualitative vs. Quantitative

Learn about common types of journal articles that use APA Style, including empirical studies; meta-analyses; literature reviews; and replication, theoretical, and methodological articles.

Academic Writer

© 2024 American Psychological Association.

• More about Academic Writer ...

Quantitative Research

A quantitative research project is characterized by having a population about which the researcher wants to draw conclusions, but it is not possible to collect data on the entire population.

• For an observational study, it is necessary to select a proper, statistical random sample and to use methods of statistical inference to draw conclusions about the population. 
• For an experimental study, it is necessary to have a random assignment of subjects to experimental and control groups in order to use methods of statistical inference.

Statistical methods are used in all three stages of a quantitative research project.

For observational studies, the data are collected using statistical sampling theory. Then, the sample data are analyzed using descriptive statistical analysis. Finally, generalizations are made from the sample data to the entire population using statistical inference.

For experimental studies, the subjects are allocated to experimental and control group using randomizing methods. Then, the experimental data are analyzed using descriptive statistical analysis. Finally, just as for observational data, generalizations are made to a larger population.

Iversen, G. (2004). Quantitative research . In M. Lewis-Beck, A. Bryman, & T. Liao (Eds.), Encyclopedia of social science research methods . (pp. 897-898). Thousand Oaks, CA: SAGE Publications, Inc.

Qualitative Research

What makes a work deserving of the label qualitative research is the demonstrable effort to produce richly and relevantly detailed descriptions and particularized interpretations of people and the social, linguistic, material, and other practices and events that shape and are shaped by them.

Qualitative research typically includes, but is not limited to, discerning the perspectives of these people, or what is often referred to as the actor’s point of view. Although both philosophically and methodologically a highly diverse entity, qualitative research is marked by certain defining imperatives that include its case (as opposed to its variable) orientation, sensitivity to cultural and historical context, and reflexivity. 

In its many guises, qualitative research is a form of empirical inquiry that typically entails some form of purposive sampling for information-rich cases; in-depth interviews and open-ended interviews, lengthy participant/field observations, and/or document or artifact study; and techniques for analysis and interpretation of data that move beyond the data generated and their surface appearances. 

Sandelowski, M. (2004).  Qualitative research . In M. Lewis-Beck, A. Bryman, & T. Liao (Eds.),  Encyclopedia of social science research methods . (pp. 893-894). Thousand Oaks, CA: SAGE Publications, Inc.

• Next: Quantitative vs. Qualitative >>
• Last Updated: Jul 24, 2024 12:04 PM
• URL: https://library.piedmont.edu/empirical-research
• Ebooks & Online Video
• New Materials
• Renew Checkouts
• Faculty Resources
• Library Friends
• Library Services
• Our Mission
• Library History
• Ask a Librarian!
• Making Citations
• Working Online

Arrendale Library Piedmont University 706-776-0111

Research Basics - All Subjects

• Getting Started
• Evaluating Resources
• Generating Search Terms
• Citations, Copyright, Plagiarism
• What is AI Plagiarism?
• Physical Library Resources
• Digital Library Resources
• Videos / Tutorials on Search Skills
• Primary Sources (Social Sciences & Humanities)
• Subject Guides: Find your Major This link opens in a new window
• Open Educational Resources (OER) This link opens in a new window

Empirical Studies

Quantitative Research

Qualitative Research

Mixed Methods Research

Montgomery Library at Campbellsville University does not necessarily endorse all content found on third party websites.

• An empirical study is research derived from actual observation or experimentation.
• The written articles resulting from empirical studies undergo a rigorous review by experts in the field of study prior to being published in journals.
• After passing this review the articles are published in a scholarly, peer-reviewed, or academic journal.
• Empirical study articles will generally contain the following features: Abstract - This is a summary of the article. Introduction - This is often identified as the hypothesis of the study and describes the researcher's intent.            Method - A description of how the research was conducted. Results - A description of the findings obtained as a result of the research. Most often answers the hypothesis. Conclusion - A description of how/if the findings were successful and the impact made as a result. References - A detailed listing of all resources cited in the article that support the written work.

Keywords for Empirical Studies:

empirical, experiment, methodology, observation, outcomes, sample size, statistical analysis, study

Types of Empirical Studies:

There are several types of empirical research, and three common types are  quantitative , qualitative ,  and  mixed methods research ,  which are all explained below. Many empirical studies in the social sciences use mixed methods to examine complex phenomena.

Four Main Types of Quantitative Research Design:

• Descriptive
• Correlational
• Quasi-experimental 
• Experimental

Five Main Types of Qualitative Research

• Grounded theory 
• Phenomenology
• Ethnography 
• Historical Research

Mixed methods research uses strategies from both qualitative and quantitative research processes to provide a greater understanding of the subject matter.

• << Previous: Open Educational Resources (OER)
• Next: Help >>
• Last Updated: Sep 5, 2024 11:13 AM
• URL: https://campbellsville.libguides.com/researchbasics

• University of Memphis Libraries
• Research Guides

Empirical Research: Defining, Identifying, & Finding

Defining empirical research, what is empirical research, quantitative or qualitative.

• Introduction
• Database Tools
• Search Terms
• Image Descriptions

Calfee & Chambliss (2005)  (UofM login required) describe empirical research as a "systematic approach for answering certain types of questions."  Those questions are answered "[t]hrough the collection of evidence under carefully defined and replicable conditions" (p. 43). 

The evidence collected during empirical research is often referred to as "data." 

Characteristics of Empirical Research

Emerald Publishing's guide to conducting empirical research identifies a number of common elements to empirical research: 

• A  research question , which will determine research objectives.
• A particular and planned  design  for the research, which will depend on the question and which will find ways of answering it with appropriate use of resources.
• The gathering of  primary data , which is then analysed.
• A particular  methodology  for collecting and analysing the data, such as an experiment or survey.
• The limitation of the data to a particular group, area or time scale, known as a sample [emphasis added]: for example, a specific number of employees of a particular company type, or all users of a library over a given time scale. The sample should be somehow representative of a wider population.
• The ability to  recreate  the study and test the results. This is known as  reliability .
• The ability to  generalize  from the findings to a larger sample and to other situations.

If you see these elements in a research article, you can feel confident that you have found empirical research. Emerald's guide goes into more detail on each element. 

Empirical research methodologies can be described as quantitative, qualitative, or a mix of both (usually called mixed-methods).

Ruane (2016)  (UofM login required) gets at the basic differences in approach between quantitative and qualitative research:

• Quantitative research  -- an approach to documenting reality that relies heavily on numbers both for the measurement of variables and for data analysis (p. 33).
• Qualitative research  -- an approach to documenting reality that relies on words and images as the primary data source (p. 33).

Both quantitative and qualitative methods are empirical . If you can recognize that a research study is quantitative or qualitative study, then you have also recognized that it is empirical study. 

Below are information on the characteristics of quantitative and qualitative research. This video from Scribbr also offers a good overall introduction to the two approaches to research methodology: 

Characteristics of Quantitative Research 

Researchers test hypotheses, or theories, based in assumptions about causality, i.e. we expect variable X to cause variable Y. Variables have to be controlled as much as possible to ensure validity. The results explain the relationship between the variables. Measures are based in pre-defined instruments.

Examples: experimental or quasi-experimental design, pretest & post-test, survey or questionnaire with closed-ended questions. Studies that identify factors that influence an outcomes, the utility of an intervention, or understanding predictors of outcomes. 

Characteristics of Qualitative Research

Researchers explore “meaning individuals or groups ascribe to social or human problems (Creswell & Creswell, 2018, p3).” Questions and procedures emerge rather than being prescribed. Complexity, nuance, and individual meaning are valued. Research is both inductive and deductive. Data sources are multiple and varied, i.e. interviews, observations, documents, photographs, etc. The researcher is a key instrument and must be reflective of their background, culture, and experiences as influential of the research.

Examples: open question interviews and surveys, focus groups, case studies, grounded theory, ethnography, discourse analysis, narrative, phenomenology, participatory action research.

Calfee, R. C. & Chambliss, M. (2005). The design of empirical research. In J. Flood, D. Lapp, J. R. Squire, & J. Jensen (Eds.),  Methods of research on teaching the English language arts: The methodology chapters from the handbook of research on teaching the English language arts (pp. 43-78). Routledge.  http://ezproxy.memphis.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=nlebk&AN=125955&site=eds-live&scope=site .

Creswell, J. W., & Creswell, J. D. (2018).  Research design: Qualitative, quantitative, and mixed methods approaches  (5th ed.). Thousand Oaks: Sage.

How to... conduct empirical research . (n.d.). Emerald Publishing.  https://www.emeraldgrouppublishing.com/how-to/research-methods/conduct-empirical-research .

Scribbr. (2019). Quantitative vs. qualitative: The differences explained  [video]. YouTube.  https://www.youtube.com/watch?v=a-XtVF7Bofg .

Ruane, J. M. (2016).  Introducing social research methods : Essentials for getting the edge . Wiley-Blackwell.  http://ezproxy.memphis.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=nlebk&AN=1107215&site=eds-live&scope=site .  

• << Previous: Home
• Next: Identifying Empirical Research >>
• Last Updated: Apr 2, 2024 11:25 AM
• URL: https://libguides.memphis.edu/empirical-research

Qualitative Research : Definition

Qualitative research is the naturalistic study of social meanings and processes, using interviews, observations, and the analysis of texts and images.  In contrast to quantitative researchers, whose statistical methods enable broad generalizations about populations (for example, comparisons of the percentages of U.S. demographic groups who vote in particular ways), qualitative researchers use in-depth studies of the social world to analyze how and why groups think and act in particular ways (for instance, case studies of the experiences that shape political views).   

Events and Workshops

• Introduction to NVivo Have you just collected your data and wondered what to do next? Come join us for an introductory session on utilizing NVivo to support your analytical process. This session will only cover features of the software and how to import your records. Please feel free to attend any of the following sessions below: April 25th, 2024 12:30 pm - 1:45 pm Green Library - SVA Conference Room 125 May 9th, 2024 12:30 pm - 1:45 pm Green Library - SVA Conference Room 125
• Next: Choose an approach >>
• Choose an approach
• Find studies
• Learn methods
• Getting Started
• Get software
• Get data for secondary analysis
• Network with researchers

• Last Updated: Aug 9, 2024 2:09 PM
• URL: https://guides.library.stanford.edu/qualitative_research

• Skip to main content
• Skip to primary sidebar
• Skip to footer
• QuestionPro

• Solutions Industries Gaming Automotive Sports and events Education Government Travel & Hospitality Financial Services Healthcare Cannabis Technology Use Case AskWhy Communities Audience Contactless surveys Mobile LivePolls Member Experience GDPR Positive People Science 360 Feedback Surveys
• Resources Blog eBooks Survey Templates Case Studies Training Help center

Home Market Research

Empirical Research: Definition, Methods, Types and Examples

Content Index

Empirical research: Definition

Empirical research: origin, quantitative research methods, qualitative research methods, steps for conducting empirical research, empirical research methodology cycle, advantages of empirical research, disadvantages of empirical research, why is there a need for empirical research.

Empirical research is defined as any research where conclusions of the study is strictly drawn from concretely empirical evidence, and therefore “verifiable” evidence.

This empirical evidence can be gathered using quantitative market research and  qualitative market research  methods.

For example: A research is being conducted to find out if listening to happy music in the workplace while working may promote creativity? An experiment is conducted by using a music website survey on a set of audience who are exposed to happy music and another set who are not listening to music at all, and the subjects are then observed. The results derived from such a research will give empirical evidence if it does promote creativity or not.

LEARN ABOUT: Behavioral Research

You must have heard the quote” I will not believe it unless I see it”. This came from the ancient empiricists, a fundamental understanding that powered the emergence of medieval science during the renaissance period and laid the foundation of modern science, as we know it today. The word itself has its roots in greek. It is derived from the greek word empeirikos which means “experienced”.

In today’s world, the word empirical refers to collection of data using evidence that is collected through observation or experience or by using calibrated scientific instruments. All of the above origins have one thing in common which is dependence of observation and experiments to collect data and test them to come up with conclusions.

LEARN ABOUT: Causal Research

Types and methodologies of empirical research

Empirical research can be conducted and analysed using qualitative or quantitative methods.

• Quantitative research : Quantitative research methods are used to gather information through numerical data. It is used to quantify opinions, behaviors or other defined variables . These are predetermined and are in a more structured format. Some of the commonly used methods are survey, longitudinal studies, polls, etc
• Qualitative research:   Qualitative research methods are used to gather non numerical data.  It is used to find meanings, opinions, or the underlying reasons from its subjects. These methods are unstructured or semi structured. The sample size for such a research is usually small and it is a conversational type of method to provide more insight or in-depth information about the problem Some of the most popular forms of methods are focus groups, experiments, interviews, etc.

Data collected from these will need to be analysed. Empirical evidence can also be analysed either quantitatively and qualitatively. Using this, the researcher can answer empirical questions which have to be clearly defined and answerable with the findings he has got. The type of research design used will vary depending on the field in which it is going to be used. Many of them might choose to do a collective research involving quantitative and qualitative method to better answer questions which cannot be studied in a laboratory setting.

LEARN ABOUT: Qualitative Research Questions and Questionnaires

Quantitative research methods aid in analyzing the empirical evidence gathered. By using these a researcher can find out if his hypothesis is supported or not.

• Survey research: Survey research generally involves a large audience to collect a large amount of data. This is a quantitative method having a predetermined set of closed questions which are pretty easy to answer. Because of the simplicity of such a method, high responses are achieved. It is one of the most commonly used methods for all kinds of research in today’s world.

Previously, surveys were taken face to face only with maybe a recorder. However, with advancement in technology and for ease, new mediums such as emails , or social media have emerged.

For example: Depletion of energy resources is a growing concern and hence there is a need for awareness about renewable energy. According to recent studies, fossil fuels still account for around 80% of energy consumption in the United States. Even though there is a rise in the use of green energy every year, there are certain parameters because of which the general population is still not opting for green energy. In order to understand why, a survey can be conducted to gather opinions of the general population about green energy and the factors that influence their choice of switching to renewable energy. Such a survey can help institutions or governing bodies to promote appropriate awareness and incentive schemes to push the use of greener energy.

Learn more: Renewable Energy Survey Template Descriptive Research vs Correlational Research

• Experimental research: In experimental research , an experiment is set up and a hypothesis is tested by creating a situation in which one of the variable is manipulated. This is also used to check cause and effect. It is tested to see what happens to the independent variable if the other one is removed or altered. The process for such a method is usually proposing a hypothesis, experimenting on it, analyzing the findings and reporting the findings to understand if it supports the theory or not.

For example: A particular product company is trying to find what is the reason for them to not be able to capture the market. So the organisation makes changes in each one of the processes like manufacturing, marketing, sales and operations. Through the experiment they understand that sales training directly impacts the market coverage for their product. If the person is trained well, then the product will have better coverage.

• Correlational research: Correlational research is used to find relation between two set of variables . Regression analysis is generally used to predict outcomes of such a method. It can be positive, negative or neutral correlation.

LEARN ABOUT: Level of Analysis

For example: Higher educated individuals will get higher paying jobs. This means higher education enables the individual to high paying job and less education will lead to lower paying jobs.

• Longitudinal study: Longitudinal study is used to understand the traits or behavior of a subject under observation after repeatedly testing the subject over a period of time. Data collected from such a method can be qualitative or quantitative in nature.

For example: A research to find out benefits of exercise. The target is asked to exercise everyday for a particular period of time and the results show higher endurance, stamina, and muscle growth. This supports the fact that exercise benefits an individual body.

• Cross sectional: Cross sectional study is an observational type of method, in which a set of audience is observed at a given point in time. In this type, the set of people are chosen in a fashion which depicts similarity in all the variables except the one which is being researched. This type does not enable the researcher to establish a cause and effect relationship as it is not observed for a continuous time period. It is majorly used by healthcare sector or the retail industry.

For example: A medical study to find the prevalence of under-nutrition disorders in kids of a given population. This will involve looking at a wide range of parameters like age, ethnicity, location, incomes  and social backgrounds. If a significant number of kids coming from poor families show under-nutrition disorders, the researcher can further investigate into it. Usually a cross sectional study is followed by a longitudinal study to find out the exact reason.

• Causal-Comparative research : This method is based on comparison. It is mainly used to find out cause-effect relationship between two variables or even multiple variables.

For example: A researcher measured the productivity of employees in a company which gave breaks to the employees during work and compared that to the employees of the company which did not give breaks at all.

LEARN ABOUT: Action Research

Some research questions need to be analysed qualitatively, as quantitative methods are not applicable there. In many cases, in-depth information is needed or a researcher may need to observe a target audience behavior, hence the results needed are in a descriptive analysis form. Qualitative research results will be descriptive rather than predictive. It enables the researcher to build or support theories for future potential quantitative research. In such a situation qualitative research methods are used to derive a conclusion to support the theory or hypothesis being studied.

LEARN ABOUT: Qualitative Interview

• Case study: Case study method is used to find more information through carefully analyzing existing cases. It is very often used for business research or to gather empirical evidence for investigation purpose. It is a method to investigate a problem within its real life context through existing cases. The researcher has to carefully analyse making sure the parameter and variables in the existing case are the same as to the case that is being investigated. Using the findings from the case study, conclusions can be drawn regarding the topic that is being studied.

For example: A report mentioning the solution provided by a company to its client. The challenges they faced during initiation and deployment, the findings of the case and solutions they offered for the problems. Such case studies are used by most companies as it forms an empirical evidence for the company to promote in order to get more business.

• Observational method:   Observational method is a process to observe and gather data from its target. Since it is a qualitative method it is time consuming and very personal. It can be said that observational research method is a part of ethnographic research which is also used to gather empirical evidence. This is usually a qualitative form of research, however in some cases it can be quantitative as well depending on what is being studied.

For example: setting up a research to observe a particular animal in the rain-forests of amazon. Such a research usually take a lot of time as observation has to be done for a set amount of time to study patterns or behavior of the subject. Another example used widely nowadays is to observe people shopping in a mall to figure out buying behavior of consumers.

• One-on-one interview: Such a method is purely qualitative and one of the most widely used. The reason being it enables a researcher get precise meaningful data if the right questions are asked. It is a conversational method where in-depth data can be gathered depending on where the conversation leads.

For example: A one-on-one interview with the finance minister to gather data on financial policies of the country and its implications on the public.

• Focus groups: Focus groups are used when a researcher wants to find answers to why, what and how questions. A small group is generally chosen for such a method and it is not necessary to interact with the group in person. A moderator is generally needed in case the group is being addressed in person. This is widely used by product companies to collect data about their brands and the product.

For example: A mobile phone manufacturer wanting to have a feedback on the dimensions of one of their models which is yet to be launched. Such studies help the company meet the demand of the customer and position their model appropriately in the market.

• Text analysis: Text analysis method is a little new compared to the other types. Such a method is used to analyse social life by going through images or words used by the individual. In today’s world, with social media playing a major part of everyone’s life, such a method enables the research to follow the pattern that relates to his study.

For example: A lot of companies ask for feedback from the customer in detail mentioning how satisfied are they with their customer support team. Such data enables the researcher to take appropriate decisions to make their support team better.

Sometimes a combination of the methods is also needed for some questions that cannot be answered using only one type of method especially when a researcher needs to gain a complete understanding of complex subject matter.

We recently published a blog that talks about examples of qualitative data in education ; why don’t you check it out for more ideas?

Learn More: Data Collection Methods: Types & Examples

Since empirical research is based on observation and capturing experiences, it is important to plan the steps to conduct the experiment and how to analyse it. This will enable the researcher to resolve problems or obstacles which can occur during the experiment.

Step #1: Define the purpose of the research

This is the step where the researcher has to answer questions like what exactly do I want to find out? What is the problem statement? Are there any issues in terms of the availability of knowledge, data, time or resources. Will this research be more beneficial than what it will cost.

Before going ahead, a researcher has to clearly define his purpose for the research and set up a plan to carry out further tasks.

Step #2 : Supporting theories and relevant literature

The researcher needs to find out if there are theories which can be linked to his research problem . He has to figure out if any theory can help him support his findings. All kind of relevant literature will help the researcher to find if there are others who have researched this before, or what are the problems faced during this research. The researcher will also have to set up assumptions and also find out if there is any history regarding his research problem

Step #3: Creation of Hypothesis and measurement

Before beginning the actual research he needs to provide himself a working hypothesis or guess what will be the probable result. Researcher has to set up variables, decide the environment for the research and find out how can he relate between the variables.

Researcher will also need to define the units of measurements, tolerable degree for errors, and find out if the measurement chosen will be acceptable by others.

Step #4: Methodology, research design and data collection

In this step, the researcher has to define a strategy for conducting his research. He has to set up experiments to collect data which will enable him to propose the hypothesis. The researcher will decide whether he will need experimental or non experimental method for conducting the research. The type of research design will vary depending on the field in which the research is being conducted. Last but not the least, the researcher will have to find out parameters that will affect the validity of the research design. Data collection will need to be done by choosing appropriate samples depending on the research question. To carry out the research, he can use one of the many sampling techniques. Once data collection is complete, researcher will have empirical data which needs to be analysed.

LEARN ABOUT: Best Data Collection Tools

Step #5: Data Analysis and result

Data analysis can be done in two ways, qualitatively and quantitatively. Researcher will need to find out what qualitative method or quantitative method will be needed or will he need a combination of both. Depending on the unit of analysis of his data, he will know if his hypothesis is supported or rejected. Analyzing this data is the most important part to support his hypothesis.

Step #6: Conclusion

A report will need to be made with the findings of the research. The researcher can give the theories and literature that support his research. He can make suggestions or recommendations for further research on his topic.

A.D. de Groot, a famous dutch psychologist and a chess expert conducted some of the most notable experiments using chess in the 1940’s. During his study, he came up with a cycle which is consistent and now widely used to conduct empirical research. It consists of 5 phases with each phase being as important as the next one. The empirical cycle captures the process of coming up with hypothesis about how certain subjects work or behave and then testing these hypothesis against empirical data in a systematic and rigorous approach. It can be said that it characterizes the deductive approach to science. Following is the empirical cycle.

• Observation: At this phase an idea is sparked for proposing a hypothesis. During this phase empirical data is gathered using observation. For example: a particular species of flower bloom in a different color only during a specific season.
• Induction: Inductive reasoning is then carried out to form a general conclusion from the data gathered through observation. For example: As stated above it is observed that the species of flower blooms in a different color during a specific season. A researcher may ask a question “does the temperature in the season cause the color change in the flower?” He can assume that is the case, however it is a mere conjecture and hence an experiment needs to be set up to support this hypothesis. So he tags a few set of flowers kept at a different temperature and observes if they still change the color?
• Deduction: This phase helps the researcher to deduce a conclusion out of his experiment. This has to be based on logic and rationality to come up with specific unbiased results.For example: In the experiment, if the tagged flowers in a different temperature environment do not change the color then it can be concluded that temperature plays a role in changing the color of the bloom.
• Testing: This phase involves the researcher to return to empirical methods to put his hypothesis to the test. The researcher now needs to make sense of his data and hence needs to use statistical analysis plans to determine the temperature and bloom color relationship. If the researcher finds out that most flowers bloom a different color when exposed to the certain temperature and the others do not when the temperature is different, he has found support to his hypothesis. Please note this not proof but just a support to his hypothesis.
• Evaluation: This phase is generally forgotten by most but is an important one to keep gaining knowledge. During this phase the researcher puts forth the data he has collected, the support argument and his conclusion. The researcher also states the limitations for the experiment and his hypothesis and suggests tips for others to pick it up and continue a more in-depth research for others in the future. LEARN MORE: Population vs Sample

LEARN MORE: Population vs Sample

There is a reason why empirical research is one of the most widely used method. There are a few advantages associated with it. Following are a few of them.

• It is used to authenticate traditional research through various experiments and observations.
• This research methodology makes the research being conducted more competent and authentic.
• It enables a researcher understand the dynamic changes that can happen and change his strategy accordingly.
• The level of control in such a research is high so the researcher can control multiple variables.
• It plays a vital role in increasing internal validity .

Even though empirical research makes the research more competent and authentic, it does have a few disadvantages. Following are a few of them.

• Such a research needs patience as it can be very time consuming. The researcher has to collect data from multiple sources and the parameters involved are quite a few, which will lead to a time consuming research.
• Most of the time, a researcher will need to conduct research at different locations or in different environments, this can lead to an expensive affair.
• There are a few rules in which experiments can be performed and hence permissions are needed. Many a times, it is very difficult to get certain permissions to carry out different methods of this research.
• Collection of data can be a problem sometimes, as it has to be collected from a variety of sources through different methods.

LEARN ABOUT:  Social Communication Questionnaire

Empirical research is important in today’s world because most people believe in something only that they can see, hear or experience. It is used to validate multiple hypothesis and increase human knowledge and continue doing it to keep advancing in various fields.

For example: Pharmaceutical companies use empirical research to try out a specific drug on controlled groups or random groups to study the effect and cause. This way, they prove certain theories they had proposed for the specific drug. Such research is very important as sometimes it can lead to finding a cure for a disease that has existed for many years. It is useful in science and many other fields like history, social sciences, business, etc.

LEARN ABOUT: 12 Best Tools for Researchers

With the advancement in today’s world, empirical research has become critical and a norm in many fields to support their hypothesis and gain more knowledge. The methods mentioned above are very useful for carrying out such research. However, a number of new methods will keep coming up as the nature of new investigative questions keeps getting unique or changing.

Create a single source of real data with a built-for-insights platform. Store past data, add nuggets of insights, and import research data from various sources into a CRM for insights. Build on ever-growing research with a real-time dashboard in a unified research management platform to turn insights into knowledge.

LEARN MORE         FREE TRIAL

MORE LIKE THIS

Experimental vs Observational Studies: Differences & Examples

Sep 5, 2024

Interactive Forms: Key Features, Benefits, Uses + Design Tips

Sep 4, 2024

Closed-Loop Management: The Key to Customer Centricity

Sep 3, 2024

Net Trust Score: Tool for Measuring Trust in Organization

Sep 2, 2024

Other categories

• Academic Research
• Artificial Intelligence
• Assessments
• Brand Awareness
• Case Studies
• Communities
• Consumer Insights
• Customer effort score
• Customer Engagement
• Customer Experience
• Customer Loyalty
• Customer Research
• Customer Satisfaction
• Employee Benefits
• Employee Engagement
• Employee Retention
• Friday Five
• General Data Protection Regulation
• Insights Hub
• Life@QuestionPro
• Market Research
• Mobile diaries
• Mobile Surveys
• New Features
• Online Communities
• Question Types
• Questionnaire
• QuestionPro Products
• Release Notes
• Research Tools and Apps
• Revenue at Risk
• Survey Templates
• Training Tips
• Tuesday CX Thoughts (TCXT)
• Uncategorized
• What’s Coming Up
• Workforce Intelligence

• Ask a Librarian

Research: Overview & Approaches

• Getting Started with Undergraduate Research
• Planning & Getting Started
• Building Your Knowledge Base
• Locating Sources
• Reading Scholarly Articles
• Creating a Literature Review
• Productivity & Organizing Research
• Scholarly and Professional Relationships

Introduction to Empirical Research

Databases for finding empirical research, guided search, google scholar, examples of empirical research, sources and further reading.

• Interpretive Research
• Action-Based Research
• Creative & Experimental Approaches

Your Librarian

• Introductory Video This video covers what empirical research is, what kinds of questions and methods empirical researchers use, and some tips for finding empirical research articles in your discipline.

• Guided Search: Finding Empirical Research Articles This is a hands-on tutorial that will allow you to use your own search terms to find resources.

• Study on radiation transfer in human skin for cosmetics
• Long-Term Mobile Phone Use and the Risk of Vestibular Schwannoma: A Danish Nationwide Cohort Study
• Emissions Impacts and Benefits of Plug-In Hybrid Electric Vehicles and Vehicle-to-Grid Services
• Review of design considerations and technological challenges for successful development and deployment of plug-in hybrid electric vehicles
• Endocrine disrupters and human health: could oestrogenic chemicals in body care cosmetics adversely affect breast cancer incidence in women?

• << Previous: Scholarly and Professional Relationships
• Next: Interpretive Research >>
• Last Updated: Aug 13, 2024 12:18 PM
• URL: https://guides.lib.purdue.edu/research_approaches

Qualitative vs Empirical Research: Difference and Comparison

Sharing is caring!

Empirical research draws its conclusions strictly from ’empirical’ or ‘verifiable’ evidence.

This research analysis mode can be further bifurcated into two subsets known as qualitative and quantitative research, where the former deals with a general description of the studied phenomenon. In contrast, the latter is based on numerical analysis.

Key Takeaways Data type: Qualitative research focuses on non-numerical data like opinions, emotions, and experiences, while empirical research emphasizes measurable, numerical data. Methods: Qualitative research employs interviews, observations, and content analysis, while empirical research uses experiments, surveys, and quantitative data analysis. Goal: Qualitative research aims to understand human behavior and underlying motivations, whereas empirical research seeks to establish cause-and-effect relationships and test hypotheses.

Qualitative vs Empirical Research

The difference between qualitative and empirical research is misinterpreted as the practical method being the independent research method dealing with numerical data and facts. In contrast, the qualitative approach is concerned with the descriptive analysis and opinions of the subjects.

But, empirical research is not an independent category and entails both qualitative and quantitative methods as subcategories.

Similar Reads

• Qualitative vs Quantitative Research: Difference and Comparison
• What is Qualitative Research? | Definition, Methods, Examples, Pros vs Cons
• Research Method vs Research Methodology: Difference and Comparison
• Marketing Research vs Market Research: Difference and Comparison
• Formal Research vs Informal Research: Difference and Comparison

The empirical method gathers information from the numerical data by working on the predetermined notions. The qualitative approach also incorporates non-numerical data, such as interviews, focus groups, etc., to get a clearer idea of the subject.

Comparison Table

What is Qualitative Research?

The qualitative method is the research practice that considers the human subject to develop an understanding of human and social behaviour and to find a way to feel and react.

The advantages of Qualitative Research are as follows:

• It allows for exploring the hidden research approaches, which numerical data alone cannot do.
• Along with the research analysis, it allows for the contradictions to suggest the social reality of individuals.
• It follows no conventional standards; therefore, data duplication is impossible.

Along with the advantages, this research method has certain drawbacks as well, such as:

• Sampling size is not enough to provide the true reflection of ideas that the researcher wants to carry forward.
• A personal bias towards certain notions consciously or unconsciously influences it.

What is Empirical Research?

Empirical research is the analytical form of research method which collects information based on numerical data by employing statistical, mathematical, and numerical techniques from descriptive (qualitative) sources.

Surveys, focus groups, polls, experimental research, etc., are some of the elements of the analysis that it considers.

The advantages of Empirical research are as follows:

• One can easily collect more samples in less time by doing it quantitatively.
• Researchers can focus on specified cultural realities they want to explore.
• Most of the time, it doesn’t require direct interactions with the subjects.
• It can be performed in a lab-like setting or on some naturalistic grounds.

Along with the advantages, this research method also has certain drawbacks, such as:   

• This kind of research is very time-consuming and demands patience.
• It can also prove expensive because the researcher will conduct the study at different locations.

Main Differences Between Qualitative and Empirical Research

• The elements of analysis in Qualitative research are interviews, group discussions, case studies, etc., whereas, in Empirical research, the key elements are surveys, focus groups, interviews, etc.
• The qualitative mode of analysis is multi-method in focus. Therefore, it allows ambiguities and contradictions, but the Empirical method works by measuring things and drawing explanatory inferences. Therefore, it doesn’t always provide space for contradictions.

• https://rmitchel.uoregon.edu/sites/rmitchel1.uoregon.edu/files/resume/articles_refereed/1998-JED.pdf
• https://www.sciencedirect.com/science/article/pii/0169207095005917

I’ve put so much effort writing this blog post to provide value to you. It’ll be very helpful for me, if you consider sharing it on social media or with your friends/family. SHARING IS ♥️

Want to save this article for later? Click the heart in the bottom right corner to save to your own articles box!

By Emma Smith

Emma Smith holds an MA degree in English from Irvine Valley College. She has been a Journalist since 2002, writing articles on the English language, Sports, and Law. Read more about me on her bio page .

Related Post

Arithmetic progression vs arithmetic sequence: difference and comparison, arithmetic mean vs geometric sequence: difference and comparison, discipline vs punishment: difference and comparison, 10 useful 3d printing ideas: enhance your home with diy projects, how to login network solutions webmail: quick guide for users, where are mics & cameras on smart tvs a concise guide, need to use two displays dual monitor setup guide.

Information

• Author Services

Initiatives

You are accessing a machine-readable page. In order to be human-readable, please install an RSS reader.

All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess .

Feature papers represent the most advanced research with significant potential for high impact in the field. A Feature Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research directions and describes possible research applications.

Feature papers are submitted upon individual invitation or recommendation by the scientific editors and must receive positive feedback from the reviewers.

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

Original Submission Date Received: .

• Active Journals
• Find a Journal
• Proceedings Series
• For Authors
• For Reviewers
• For Editors
• For Librarians
• For Publishers
• For Societies
• For Conference Organizers
• Open Access Policy
• Institutional Open Access Program
• Special Issues Guidelines
• Editorial Process
• Research and Publication Ethics
• Article Processing Charges
• Testimonials
• Preprints.org
• SciProfiles
• Encyclopedia

Article Menu

• Subscribe SciFeed
• Recommended Articles
• Google Scholar
• on Google Scholar
• Table of Contents

Find support for a specific problem in the support section of our website.

Please let us know what you think of our products and services.

Visit our dedicated information section to learn more about MDPI.

JSmol Viewer

Toward a sustainable surimi industry: comprehensive review and future research directions of demersal fish stock assessment techniques.

1. Introduction

• What are the knowledge coverage and research gaps concerning key sustainability-related concepts in the utilization of demersal fish in the surimi industry, as well as in the LB-SPR method for assessing biological length-based reproduction as a support method in the surimi industry, and how can these gaps be addressed in future research?
• What aspects of implementation can enhance the quality and scope of the LB-SPR method in assessing reproduction based on biological length, and how can these steps contribute to improving sustainability in the surimi industry?

2. Materials and Methods

2.1. study design, 2.2. data collection and materials, 2.3. literature selection and mapping methods, 2.4. analytical method, 2.5. study framework, 3. results and discussion, 3.1. study selection, credibility–validity assessment, and knowledge cluster mapping.

3.2. Sustainability-Related Information in Demersal Fish Stock Assessments for the Surimi Industry

3.3. methodology of length-based reproductive assessments (lb-sprs), 3.4. case studies on the application and benefits of the lb-spr, 3.5. multi-aspect implications of lb-spr: fisheries business, communities, and policies, 3.6. contribution to the field, gaps, and recommendations for future research, 4. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, acknowledgments, conflicts of interest.

• Sihono, S.; Purnomo, A.; Wibowo, S.; Dewi, F. Current 2021 Status of Surimi Industry in Indonesia and Possible Solutions: A Review. IOP Conf. Ser. Earth Environ. Sci. 2021 , 919 , 012036. [ Google Scholar ] [ CrossRef ]
• Liu, W.; Lyu, J.; Wu, D.; Cao, Y.; Ma, Q.; Lu, Y.; Zhang, X. Cutting Techniques in the Fish Industry: A Critical Review. Foods 2022 , 11 , 3206. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Rowan, N.J. The Role of Digital Technologies in Supporting and Improving Fishery and Aquaculture across the Supply Chain—Quo Vadis? Aquac. Fish. 2023 , 8 , 365–374. [ Google Scholar ] [ CrossRef ]
• Dimarchopoulou, D.; Mous, P.J.; Firmana, E.; Wibisono, E.; Coro, G.; Humphries, A.T. Exploring the Status of the Indonesian Deep Demersal Fishery Using Length-Based Stock Assessments. Fish. Res. 2021 , 243 , 106089. [ Google Scholar ] [ CrossRef ]
• Yin, T.; Park, J.W. Comprehensive Review: By-Products from Surimi Production and Better Utilization. Food Sci. Biotechnol. 2023 , 32 , 1957–1980. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Huber, R.; D’Onofrio, C.; Devaraju, A.; Klump, J.; Loescher, H.W.; Kindermann, S.; Guru, S.; Grant, M.; Morris, B.; Wyborn, L.; et al. Integrating Data and Analysis Technologies within Leading Environmental Research Infrastructures: Challenges and Approaches. Ecol. Inform. 2021 , 61 , 101245. [ Google Scholar ] [ CrossRef ]
• National Academies of Sciences, Engineering, and Medicine. Reproducibility and Replicability in Science ; National Academies Press (US): Washington, DC, USA, 2019; pp. 1–257. [ Google Scholar ]
• Coscino, C.L.; Bellquist, L.; Harford, W.J.; Semmens, B.X. Influence of Life History Characteristics on Data-Limited Stock Status Assertions and Minimum Size Limit Evaluations Using Length-Based Spawning Potential Ratio (LB-SPR). Fish. Res. 2024 , 276 , 107036. [ Google Scholar ] [ CrossRef ]
• Prince, J.; Lalavanua, S.; Tamanitoakula, J.; Tamata, L.; Green, S.; Radway, S.; Loganimoce, E.; Vodivodi, T.; Marama, K.; Waqainabete, P.; et al. Spawning Potential Surveys in Fiji: A New Song of Change for Small-Scale Fisheries in the Pacific. Conserv. Sci. Pract. 2020 , 3 , e273. [ Google Scholar ] [ CrossRef ]
• Eskandari, G.R.; Savari, A.; Koochaknejad, E.; Ghefleh, M.J. Spawner Stock Biomass per Recruit, Spawning Potential Ratio and Biological Reference Point of the Tiger Tooth Croaker (Otolithes Ruber), in the Northwestern Part of the Persian Gulf. J. Ichthyol. Res. 2013 , 1 , 1–14. [ Google Scholar ]
• Prince, J.; Victor, S.; Kloulchad, V.; Hordyk, A. Length Based SPR Assessment of Eleven Indo-Pacific Coral Reef Fish Populations in Palau. Fish. Res. 2015 , 171 , 42–58. [ Google Scholar ] [ CrossRef ]
• Lindfield, S. Palau’s Reef Fisheries: Changes in Size and Spawning Potential from Past to Present ; Coral Reef Research Foundation: Koror, Palau, 2017; pp. 1–23. [ Google Scholar ]
• Yonvitner; Boer, M.; Kurnia, R. Length Based Data of Nemipterus Japonicus to Spawning Potential Ratio (SPR) Estimation on Small Scale Fisheries (SSF) Management in Sunda Strait. IOP Conf. Ser. Earth Environ. Sci. 2021 , 674 , 012002. [ Google Scholar ] [ CrossRef ]
• Yonvitner; Kurnia, R.; Boer, M. Length Based-Spawning Potential Ratio (LB-SPR), on Exploited Demersal Stock ( Priachantus tayenus ) in Small Scale Fisheries, Sunda Strait. IOP Conf. Ser. Earth Environ. Sci. 2021 , 744 , 012103. [ Google Scholar ] [ CrossRef ]
• Bekova, R.; Raikova-Petrova, G. Maturity, Sex Ratio and Spawning Time of Liza Aurata Risso, 1810 and Liza Saliens Risso, 1810 (Mugilidae) from the Bulgarian Black Sea Coast. J. BioSci. Biotechnol. 2017 , 6 , 17–21. [ Google Scholar ]
• Priatna, A.; Boer, M.; Kurnia, R.; Yonvitner. Length Based Spawning Potential Ratio of Indian Scad (Decapterus Russeli, Rupell, 1928) in the South China Sea. IOP Conf. Ser. Earth Environ. Sci. 2021 , 744 , 012056. [ Google Scholar ] [ CrossRef ]
• Ba, K.; Woods, P.J. Assessing the North-West African Stock of Black Hakes (Merluccius Polli and Merluccius Senegalensis) Using Catch-Msy and Length-Based Spawning Potential Ratio Models. In Proceedings of the GRÓ Fisheries Training Program Under the Auspices of UNESCO, Hafnarfjörður, Iceland, 1 January 2020; GRÓ-FTP: Reykjavík, Iceland, 2019; pp. 1–32. [ Google Scholar ]
• Prince, J.; Smith, A.; Raffe, M.; Seeto, S.; Higgs, J. Spawning Potential Surveys in Solomon Islands’ Western Province. SPC Fish. Newsl. 2020 , 162 , 58–68. [ Google Scholar ]
• Yonvitner; Kurnia, R.; Boer, M. Life History and Length Based-Spawning Potential Ratio (LB-SPR) of Exploited Demersal Fish Stock (Upeneus Sp) in Sunda Strait. IOP Conf. Ser. Earth Environ. Sci. 2021 , 718 , 012074. [ Google Scholar ] [ CrossRef ]
• Yokie, A.A. An Assessment of the Sardinella Maderensis Stock of Liberia Coastal Waters Using the Length Based Spawning Potential Ratio (LBSPR). In Proceedings of the GRÓ Fisheries Training Program Under the Auspices of UNESCO, Hafnarfjörður, Iceland, 1 January 2020; GRÓ-FTP: Reykjavík, Iceland, 2019; pp. 1–22. [ Google Scholar ]
• Miles, D. A Taxonomy of Research Gaps: Identifying and Defining the Seven Research Gaps. In Proceedings of the Doctoral Workshop: Finding Research Gaps-Research Methods and Strategies, Dallas, TX, USA, 16 August 2017; Volume 1, pp. 1–15. [ Google Scholar ]
• Goodyear, C. Spawning Stock Biomass per Recruit in Fisheries Management: Foundation and Current Use. In Canadian Special Publication of Fisheries and Aquatic Sciences ; No. 120; National Research Council of Canada: Ottawa, ON, Canada, 1993; pp. 67–82. [ Google Scholar ]
• Gabriel, W.L.; Sissenwine, M.P.; Overholtz, W.J. Analysis of Spawning Stock Biomass per Recruit: An Example for Georges Bank Haddock. N. Am. J. Fish. Manag. 1989 , 9 , 383–391. [ Google Scholar ] [ CrossRef ]
• Ault, J.S.; Smith, S.G.; Luo, J.; Monaco, M.E.; Appeldoorn, R.S. Length-Based Assessment of Sustainability Benchmarks for Coral Reef Fishes in Puerto Rico. Environ. Conserv. 2008 , 35 , 221–231. [ Google Scholar ] [ CrossRef ]
• Arocha, F.; Bárrios, A. Sex Ratios, Spawning Seasonality, Sexual Maturity, and Fecundity of White Marlin (Tetrapturus Albidus) from the Western Central Atlantic. Fish. Res. 2009 , 95 , 98–111. [ Google Scholar ] [ CrossRef ]
• Kibona, O.M.; Jonasson, J.P. Application of Length-Based Spawning Potential Ratio Method and Analysis of the Structure of the Electronic Catch Assessment Survey in Marine Waters of Mainland, Tanzania. In Proceedings of the GRÓ Fisheries Training Program under the Auspices of UNESCO, Hafnarfjörður, Iceland, 1 January 2020; GRÓ-FTP: Reykjavík, Iceland, 2019; pp. 1–40. [ Google Scholar ]
• Prayitno, M.; Setiawan, H.; Jatmiko, I.; Arif Rahman, M.; Wiadnya, D. Spawning Potential Ratio (SPR) of Sulphur Goatfish ( Upeneus sulphureus ): Biological Basis for Demersal Fishery Management in Java Sea. IOP Conf. Ser. Earth Environ. Sci. 2020 , 441 , 012141. [ Google Scholar ] [ CrossRef ]
• Brooks, E.N.; Powers, J.E.; Cortés, E. Analytical Reference Points for Age-Structured Models: Application to Data-Poor Fisheries. ICES J. Mar. Sci. 2010 , 67 , 165–175. [ Google Scholar ] [ CrossRef ]
• Nugroho, D.; Patria, M.P.; Supriatna, J.; Adrianto, L. The Estimates Spawning Potential Ratio of Three Dominant Demersal Fish Species Landed in Tegal, North Coast of Central Java, Indonesia. Biodiversitas J. Biol. Divers. 2017 , 18 , 844–849. [ Google Scholar ] [ CrossRef ]
• Leadbitter, D.; Guenneugues, P.; Park, J. Tropical Surimi Performance Improvement—Certifications and Ratings ; Tropical Surimi Performance Improvement—Certifications and Ratings; Certification and Ratings Collaboration: Vancouver, BC, Canada, 2020; p. 201. [ Google Scholar ]
• Cooper, K.; White, R.E. An Introduction to Critical Approaches. In Qualitative Research in the Post-Modern Era: Critical Approaches and Selected Methodologies ; Springer: London, UK; New York, NY, USA, 2022; pp. 29–58. [ Google Scholar ]
• Creswell, J.W. Research Design: Qualitative, Quantitative, and Mixed Methods Approaches , 3rd ed.; SAGE Publications Ltd.: Thousand Oaks, CA, USA, 2009; Volume 3, pp. 1–270. [ Google Scholar ]
• Lee, S.; Smith, C.A.M. Criteria for Quantitative and Qualitative Data Integration: Mixed-Methods Research Methodology. CIN Comput. Inform. Nurs. 2012 , 30 , 251–256. [ Google Scholar ] [ CrossRef ]
• Harzing, A.-W. The Publish or Perish Book: Your Guide to Effective and Responsible Citation Analysis ; Tarma Software Research: Melbourne, Australia, 2010; Volume 1, pp. 1–255. [ Google Scholar ]
• Scopus. How Do I Work with Document Search Results? Available online: https://service.elsevier.com/app/answers/detail/a_id/11423/supporthub/scopus/ (accessed on 7 December 2023).
• Cooke, A.; Smith, D.; Booth, A. Beyond PICO: The SPIDER Tool for Qualitative Evidence Synthesis. Qual. Health Res. 2012 , 22 , 1435–1443. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Frandsen, T.F.; Bruun Nielsen, M.F.; Lindhardt, C.L.; Eriksen, M.B. Using the Full PICO Model as a Search Tool for Systematic Reviews Resulted in Lower Recall for Some PICO Elements. J. Clin. Epidemiol. 2020 , 127 , 69–75. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Moher, D.; Shamseer, L.; Clarke, M.; Ghersi, D.; Liberati, A.; Petticrew, M.; Shekelle, P.; Stewart, L.A.; PRISMA-P Group. Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P) 2015 Statement. Syst. Rev. 2015 , 4 , 1–9. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Page, M.J.; Moher, D.; Bossuyt, P.M.; Boutron, I.; Hoffmann, T.C.; Mulrow, C.D.; Shamseer, L.; Tetzlaff, J.M.; Akl, E.A.; Brennan, S.E.; et al. PRISMA 2020 Explanation and Elaboration: Updated Guidance and Exemplars for Reporting Systematic Reviews. BMJ 2021 , 372 , n160. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Waltman, L.; van Eck, N.J.; Noyons, E.C.M. A Unified Approach to Mapping and Clustering of Bibliometric Networks. J. Informetr. 2010 , 4 , 629–635. [ Google Scholar ] [ CrossRef ]
• Critical Appraisal Skills Programme CASP (Qualitative Studies Checklist). Available online: https://casp-uk.net/casp-tools-checklists (accessed on 13 October 2023).
• Peterson, B.L. Thematic Analysis/Interpretive Thematic Analysis. In The International Encyclopedia of Communication Research Methods ; Matthes, J., Davis, C.S., Potter, R.F., Eds.; Wiley: Hoboken, NJ, USA, 2017; pp. 1–9. [ Google Scholar ]
• Braun, V.; Clarke, V. Conceptual and Design Thinking for Thematic Analysis. Qual. Psychol. 2022 , 9 , 3–26. [ Google Scholar ] [ CrossRef ]
• Fishery Product Quarantine Agency. (Indonesia Marine and Fishery Ministry, Central Jakarta, Jakarta, Indonesia). Personal Communication. 2024. [ Google Scholar ]
• Marine and Fishery Ministry. (Government of Indonesia, Central Jakarta, Jakarta, Indonesia). Unpublished Data. 2024. [ Google Scholar ]
• Trade Ministry. (Government of Indonesia, Central Jakarta, Jakarta, Indonesia). Unpublished Data. 2024. [ Google Scholar ]
• United Nations Commodity Trade ; Personal Communication; United Nations: New York, NY, USA, 2012.
• Cooper, K.; White, R.E. (Eds.) Qualitative Research in the Post-Modern Era: Contexts of Qualitative Research ; Springer: London, UK; New York, NY, USA, 2012; p. 159. [ Google Scholar ]
• Tomaszewski, L.E.; Zarestky, J.; Gonzalez, E. Planning Qualitative Research: Design and Decision Making for New Researchers. Int. J. Qual. Methods 2020 , 19 , 1609406920967174. [ Google Scholar ] [ CrossRef ]
• Makri, C.; Neely, A. Grounded Theory: A Guide for Exploratory Studies in Management Research. Int. J. Qual. Methods 2021 , 20 , 16094069211013654. [ Google Scholar ] [ CrossRef ]
• Jackson, H.W.; Tiller, R.E. Preliminary Observations on Spawning Potential in the Striped Bass (Roccus Saxatilis Walbaum) ; Chesapeake Biological Laboratory: Solomons Island, MD, USA, 1952; pp. 1–16. [ Google Scholar ]
• Zamroni, A.; Ernawati, T. Population Dynamic and Spawning Potential Ratio of Short Mackerel (Rastrelliger Brachysoma Bleeker, 1851) in the Northern Coast of Java. Indones. Fish. Res. J. 2019 , 25 , 1. [ Google Scholar ] [ CrossRef ]
• Jaya, M.M.; Wiryawan, B.; Simbolon, D. The Analysis of Tuna Resource Utilization Level with Spawning Potential Ratio Method in Sendangbiru Waters. Jurnal Ilmu Dan Teknologi Kelautan Tropis. 2017 , 9 , 597–604. [ Google Scholar ] [ CrossRef ]
• Collins, L.A.; Johnson, A.G.; Koenig, C.C.; Baker, M.S., Jr. Reproductive Patterns, Sex Ratio, and Fecundity in Gag, Mycteroperca Microlepis (Serranidae), a Protogynous Grouper from the Northeastern Gulf of Mexico. Fish. Bull. 1998 , 96 , 415–427. [ Google Scholar ]
• White, D.B.; Palmer, S.M. Age, Growth, and Reproduction of the Red Snapper, Lutjanus Campechanus, from the Atlantic Waters of the Southeastern US. Bull. Mar. Sci. 2004 , 75 , 335–360. [ Google Scholar ]
• Manickchand-Heileman, S.; Ehrhardt, N. Spawning Frequency, Fecundity and Spawning Potential of the Whitemouth Croaker Micropogonias Furnieri in Trinidad, West Indies. Bull. Mar. Sci. 1996 , 58 , 156–164. [ Google Scholar ]
• Beets, J.; Friedlander, A. Evaluation of a Conservation Strategy: A Spawning Aggregation Closure for Red Hind, Epinephelus Guttatus, in the U.S. Virgin Islands. Environ. Biol. Fishes 1999 , 55 , 91–98. [ Google Scholar ] [ CrossRef ]
• Fisch, N.; Camp, E.V. Spawning Potential Ratio: A Key Metric Considered in Managing Florida’s Fisheries: FA241, 5/2022. EDIS 2022 , 3 , 1–4. [ Google Scholar ] [ CrossRef ]
• Mace, P.M.; Sissenwine, M.P. How Much Spawning per Recruit Is Enough? In Risk Evaluation and Biological Reference Points for Fisheries Management ; Canadian Special Publication of Fisheries and Aquatic Sciences; Smith, S.J., Hunt, J.J., Rivard, D., Eds.; National Research Council and Department of Fisheries and Oceans: Ottawa, Canada, 1993; pp. 101–118. [ Google Scholar ]
• Taylor, R.G.; Whittington, J.A.; Grier, H.J.; Crabtree, R.E. Age, Growth, Maturation, and Protandric Sex Reversal in Common Snook, Centropomus Undecimalis, from the East and West Coasts of South Florida. Fish. Bull. 2000 , 98 , 612–624. [ Google Scholar ]
• Coleman, F.C.; Koenig, C.C.; Eklund, A.-M.; Grimes, C.B. Management and Conservation of Temperate Reef Fishes in the Grouper-Snapper Complex. In Life in the Slow Lane: Ecology and Conservation of Long-Lived Marine Animals ; American Fisheries Society: Bethesda, MD, USA, 1999; Volume 23, pp. 233–242. [ Google Scholar ]
• Farley, J.H.; Williams, A.J.; Hoyle, S.D.; Davies, C.R.; Nicol, S.J. Reproductive Dynamics and Potential Annual Fecundity of South Pacific Albacore Tuna ( Thunnus alalunga ). PLoS ONE 2013 , 8 , e60577. [ Google Scholar ] [ CrossRef ]
• Zhou, S.; Punt, A.E.; Lei, Y.; Deng, R.A.; Hoyle, S.D. Identifying Spawner Biomass Per-recruit Reference Points from Life-history Parameters. Fish Fish. 2020 , 21 , 760–773. [ Google Scholar ] [ CrossRef ]
• Ernawati, T.; Agustina, S.; Kembaren, D.; Yulianto, I.; Satria, F. Life History Parameters and Spawning Potential Ratio of Some Reef Fish Species in Fisheries Management Area 715 of Indonesia. AACL Bioflux 2021 , 14 , 3092–3103. [ Google Scholar ]
• Vahabnezhad, A.; Hashemi, S.A.R.; Taghavimotlagh, S.; Daryanabard, R. Length Based Spawning Potential Ratio (LB-SPR) of Javelin Grunter, Pomadasys Kaakan (Cuvier, 1830) in the Persian Gulf and Oman Sea. Iran. J. Fish. Sci. 2021 , 20 , 1560–1572. [ Google Scholar ]
• Ernawati, T.; Budiarti, T. Life History and Length Base Spawning Potential Ratio (LB-SPR) of Malabar Snapper Lutjanus Malabaricus (Bloch & Schneider, 1801) in Western of South Sulawesi, Indonesia. IOP Conf. Ser. Earth Environ. Sci. 2019 , 404 , 012023. [ Google Scholar ]
• Zedta, R.R.; Madduppa, H. Exploitation Level of Yellowfin Tuna ( Thunnus albacares ) Resources in Indian Ocean Using Spawning Potential Ratio Analysis Approach. J. Penelit. Perikan. Indones. 2021 , 27 , 33–41. [ Google Scholar ]
• Sigler, M.F.; Lunsford, C.R. Effects of Individual Quotas on Catching Efficiency and Spawning Potential in the Alaska Sablefish Fishery. Can. J. Fish. Aquat. Sci. 2001 , 58 , 1300–1312. [ Google Scholar ] [ CrossRef ]
• Prince, J. Informing Community-Based Fisheries Management with Spawning Potential Surveys. Pac. Community Newsl. 2017 , 154 , 43–52. [ Google Scholar ]
• Silberschneider, V.; Gray, C.A.; Stewart, J. Age, Growth, Maturity and the Overfishing of the Iconic Sciaenid, Argyrosomus Japonicus, in South-Eastern, Australia. Fish. Res. 2009 , 95 , 220–229. [ Google Scholar ] [ CrossRef ]
• Hordyk, A. The Development and Application of a Length-Based Method to Estimate the Spawning Potential Ratio in Data-Poor Fish Stocks. Ph.D. Dissertation, Murdoch University, Murdoch, Australia, 2014. [ Google Scholar ]
• Goodyear, C. Spawning Stock Biomass per Recruit: The Biological Basis for a Fisheries Management Tool. Collect. Vol. Sci. Pap. ICCAT 1990 , 32 , 487–497. [ Google Scholar ]
• Yonvitner, Y.; Boer, M.; Kurnia, R. Spawning Potential Ratio (SPR) Approach as a Management Measure of Skipjack Sustainability Record from Cilacap Fishing Port, Central Java, Indonesia. J. Ilm. Perikan. Dan Kelaut. 2021 , 13 , 199–207. [ Google Scholar ] [ CrossRef ]
• Brooks, E.N.; Shertzer, K.W.; Gedamke, T.; Vaughan, D.S. Stock Assessment of Protogynous Fish: Evaluating Measures of Spawning Biomass Used to Estimate Biological Reference Points. Fish. Bull. 2008 , 106 , 12–23. [ Google Scholar ]
• Hordyk, A.; Ono, K.; Valencia, S.; Loneragan, N.; Prince, J. A Novel Length-Based Empirical Estimation Method of Spawning Potential Ratio (SPR), and Tests of Its Performance, for Small-Scale, Data-Poor Fisheries. ICES J. Mar. Sci. 2015 , 72 , 217–231. [ Google Scholar ] [ CrossRef ]
• Satria, F.; Sadiyah, L. Possible Use of Length-Based Spawning Potential Ratio for Skipjack (Katsuwonus Pelamis) in Indonesia’s Archipelagic Waters. Indones. Fish. Res. J. 2017 , 23 , 45–53. [ Google Scholar ] [ CrossRef ]
• IISD Indonesia Should Assess Fisheries Support to Minimize Overfishing Risk. Global Studies Initiatives. July 2021. Available online: https://www.iisd.org/gsi/news-events/government-support-marine-fisheries-indonesia-should-be-assessed-minimize-risk (accessed on 1 June 2024).
• Hordyk, A.; Ono, K.; Sainsbury, K.; Loneragan, N.; Prince, J. Some Explorations of the Life History Ratios to Describe Length Composition, Spawning-per-Recruit, and the Spawning Potential Ratio. ICES J. Mar. Sci. 2015 , 72 , 204–216. [ Google Scholar ] [ CrossRef ]
• Nadon, M.O.; Ault, J.S.; Williams, I.D.; Smith, S.G.; DiNardo, G.T. Length-Based Assessment of Coral Reef Fish Populations in the Main and Northwestern Hawaiian Islands. PLoS ONE 2015 , 10 , 0133960. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Nurdin, M.S.; Putri, A.P.; Satari, D.Y.; Valentine, R.Y.; Azmi, F.; Haser, T.F. Spawning and Reproductive Potential of Blue Swimming Crab (Portunus Pelagicus) at Spermonde Archipelago, Indonesia. J. Biodjati 2022 , 7 , 199–211. [ Google Scholar ] [ CrossRef ]
• Munyandorero, J. Embracing Uncertainty, Continual Spawning, Estimation of the Stock–Recruit Steepness, and Size-Limit Designs with Length–Based per-Recruit Analyses for African Tropical Fisheries. Fish. Res. 2018 , 199 , 137–157. [ Google Scholar ] [ CrossRef ]
• Williams, E.H.; Shertzer, K.W. Implications of Life-History Invariants for Biological Reference Points Used in Fishery Management. Can. J. Fish. Aquat. Sci. 2003 , 60 , 710–720. [ Google Scholar ] [ CrossRef ]
• Maccall, A.D. Population Estimates for the Waning Years of the Pacific Sardine Fishery. CalCOFI Rep. 1979 , 20 , 72–82. [ Google Scholar ]
• Alejo-Plata, C.; Díaz-Jaimes, P.; Salgado-Ugarte, I.H. Sex Ratios, Size at Sexual Maturity, and Spawning Seasonality of Dolphinfish ( Coryphaena hippurus ) Captured in the Gulf of Tehuantepec, Mexico. Fish. Res. 2011 , 110 , 207–216. [ Google Scholar ] [ CrossRef ]
• Stock, B.C.; Heppell, S.A.; Waterhouse, L.; Dove, I.C.; Pattengill-Semmens, C.V.; McCoy, C.M.; Bush, P.G.; Ebanks-Petrie, G.; Semmens, B.X. Pulse Recruitment and Recovery of Cayman Islands Nassau Grouper ( Epinephelus striatus ) Spawning Aggregations Revealed by in Situ Length-Frequency Data. ICES J. Mar. Sci. 2021 , 78 , 277–292. [ Google Scholar ] [ CrossRef ]
• Hoyle, S. Adjusted Biological Parameters and Spawning Biomass Calculations for Albacore Tuna in the South Pacific, and Their Implications for Stock Assessments. In Proceedings of the Scientific Committee Fourth Regular Session, WCPFC-SC4 ME-WP-02. Port Moresby, Papua New Guinea, 11–22 August 2008; pp. 11–22. [ Google Scholar ]
• Hilborn, R.; Walters, C.J. Role of Stock Assessment in Fisheries Management. In Quantitative Fisheries Stock Assessment: Choice, Dynamics and Uncertainty ; Hilborn, R., Walters, C.J., Eds.; Springer: Boston, MA, USA, 1992; pp. 3–21. [ Google Scholar ]
• Venugopal, V.; Shahidi, F.; Lee, T. Value-added Products from Underutilized Fish Species. Crit. Rev. Food Sci. Nutr. 1995 , 35 , 431–453. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Oktavia, S.; Setiawan, U.; Nurpadiana, H. Morphological Character Analysis of Mackerel (Scomberomorus Commerson Lac., 1800) in Sunda Strait. Biosf. J. Tadris Biol. 2020 , 11 , 1–10. [ Google Scholar ] [ CrossRef ]
• Daliri, M.; Kamrani, E.; Salarpouri, A.; Ben-Hasan, A. The Geographical Expansion of Fisheries Conceals the Decline in the Mean Trophic Level of Iran’s Catch. Ocean Coast. Manag. 2021 , 199 , 105411. [ Google Scholar ] [ CrossRef ]
• Buchanan, J.R.; Ralph, G.M.; Krupp, F.; Harwell, H.; Abdallah, M.; Abdulqader, E.; Al-Husaini, M.; Bishop, J.M.; Burt, J.A.; Choat, J.H. Regional Extinction Risks for Marine Bony Fishes Occurring in the Persian/Arabian Gulf. Biol. Conserv. 2019 , 230 , 10–19. [ Google Scholar ] [ CrossRef ]
• Valinassab, T.; Adjeer, M.; Sedghi, N.; Kamali, E. Monitoring of demersal resources by Swept Area Method within the Persian Gulf and Oman Sea. J. Anim. Environ. 2010 , 2 , 45–56. [ Google Scholar ]
• Lisamy, S.E.; Simanjuntak, C.P.; Ervinia, A.; Romdoni, T.A.; Munandar, A.; Nurfaiqah, S.; Guarte, D.M. Reproductive Aspects of the Japanese Threadfin Bream, Nemipterus Japonicus (Bloch, 1791) in the Southern Java Waters (FMA-RI 573). In Proceedings of the E3S Web of Conferences, Bali, Indonesia, 8–9 November 2023; EDP Sciences: Les Ulis, France, 2023; Volume 442, p. 01025. [ Google Scholar ]
• Prince, J.; Creech, S.; Madduppa, H.; Hordyk, A. Length Based Assessment of Spawning Potential Ratio in Data-Poor Fisheries for Blue Swimming Crab ( Portunus Spp.) in Sri Lanka and Indonesia: Implications for Sustainable Management. Reg. Stud. Mar. Sci. 2020 , 36 , 101309. [ Google Scholar ] [ CrossRef ]
• Aulia, I.; Rahmawati, A.; Syauqi, M.; Wahyuningsih, M.; Raimahua, S.; Akmalia, W.; Khalifa, M. Age Structure, Growth, and Mortality of Blue Swimming Crab ( Portunus pelagicus Linnaeus, 1758) in Banten Bay Waters. J. Biodjati 2023 , 8 , 69–80. [ Google Scholar ] [ CrossRef ]
• Hommik, K.; Fitzgerald, C.J.; Kelly, F.; Shephard, S. Dome-Shaped Selectivity in LB-SPR: Length-Based Assessment of Data-Limited Inland Fish Stocks Sampled with Gillnets. Fish. Res. 2020 , 229 , 105574. [ Google Scholar ] [ CrossRef ]
• Sumaila, U.R.; Tai, T.C. End Overfishing and Increase the Resilience of the Ocean to Climate Change. Front. Mar. Sci. 2020 , 7 , 523. [ Google Scholar ] [ CrossRef ]
• Dadzie, S. Vitellogenesis, Oocyte Maturation Pattern, Spawning Rhythm and Spawning Frequency in Otolithes Ruber (Schneider, 1801) (Sciaenidae) in the Kuwaiti Waters of the Arabian Gulf. Sci. Mar. 2007 , 71 , 239–248. [ Google Scholar ] [ CrossRef ]
• Jueseah, A.S. A Critical Review of the Liberian Fisheries Sector: A Technical Report. Ph.D Dissertation, University of Iceland, Reykjavik, Iceland, 2021. [ Google Scholar ]
• Wuor, M.; Mabon, L. Development of Liberia’s Fisheries Sectors: Current Status and Future Needs. Mar. Policy 2022 , 146 , 105325. [ Google Scholar ] [ CrossRef ]
• Sossoukpe, E.; Hoto, G.; Djidohokpin, G.; Fiogbe, E.D. Fishing of the Flat Sardinella (Sardinella Maderensis, Pisces, Lowe 1838) in Benin (West Africa) Nearshore Waters: Production, Typology of Fishing Gear and Current Status of Its Stock. Agric. Sci. 2022 , 13 , 1136–1150. [ Google Scholar ] [ CrossRef ]
• Porch, C.E.; Eklund, A.-M.; Scott, G.P. A Catch-Free Stock Assessment Model with Application to Goliath Grouper (Epinephelus itajara) off Southern Florida ; Southeast Fisheries Science Center: Miami, FL, USA, 2006; pp. 89–101. [ Google Scholar ]
• Pieniak, Z.; Vanhonacker, F.; Verbeke, W. Consumer Knowledge and Use of Information about Fish and Aquaculture. Food Policy 2013 , 40 , 25–30. [ Google Scholar ] [ CrossRef ]
• Adkisson, R. Nudge: Improving Decisions About Health, Wealth and Happiness. Soc. Sci. J. 2008 , 45 , 700–701. [ Google Scholar ] [ CrossRef ]
• Sun, C.-L.; Ehrhardt, N.M.; Porch, C.E.; Yeh, S.-Z. Analyses of Yield and Spawning Stock Biomass per Recruit for the South Atlantic Albacore ( Thunnus alalunga ). Fish. Res. 2002 , 56 , 193–204. [ Google Scholar ] [ CrossRef ]
• Froehlich, H.E.; Gentry, R.R.; Halpern, B.S. Global Change in Marine Aquaculture Production Potential under Climate Change. Nat. Ecol. Evol. 2018 , 2 , 1745–1750. [ Google Scholar ] [ CrossRef ]
• Priyadarshini, B.; Xavier, M.; Nayak, B.B.; Apang, T.; Balange, A.K. Quality Characteristics of Tilapia Surimi: Effect of Single Washing Cycle and Different Washing Media. J. Aquat. Food Prod. Technol. 2018 , 27 , 643–655. [ Google Scholar ] [ CrossRef ]
• Henriksson, P.J.G.; Banks, L.K.; Suri, S.K.; Pratiwi, T.Y.; Fatan, N.A.; Troell, M. Indonesian Aquaculture Futures-Identifying Interventions for Reducing Environmental Impacts. Environ. Res. Lett. 2019 , 14 , 124062. [ Google Scholar ] [ CrossRef ]
• Gentry, R.R.; Froehlich, H.E.; Grimm, D.; Kareiva, P.; Parke, M.; Rust, M.; Gaines, S.D.; Halpern, B.S. Mapping the Global Potential for Marine Aquaculture. Nat. Ecol. Evol. 2017 , 1 , 1317–1324. [ Google Scholar ] [ CrossRef ]
• Tal, Y.; Schreier, H.J.; Sowers, K.R.; Stubblefield, J.D.; Place, A.R.; Zohar, Y. Environmentally Sustainable Land-Based Marine Aquaculture. Aquaculture 2009 , 286 , 28–35. [ Google Scholar ] [ CrossRef ]
• Newman, S.G. Shrimp Farming Yesterday to Tomorrow. In Encyclopedia of Meat Sciences , 3rd ed.; Dikeman, M., Ed.; Elsevier: Oxford, UK, 2024; pp. 12–28. [ Google Scholar ]
• Gaspar, M.B.; Carvalho, S.; Cúrdia, J.; dos Santos, M.N.; Vasconcelos, P. Restoring Coastal Ecosystems from Fisheries and Aquaculture Impacts. In Treatise on Estuarine and Coastal Science , 2nd ed.; Baird, D., Elliott, M., Eds.; Academic Press: Oxford, UK, 2024; pp. 737–764. [ Google Scholar ]
• Johnson, T.R.; Beard, K.; Brady, D.C.; Byron, C.J.; Cleaver, C.; Duffy, K.; Keeney, N.; Kimble, M.; Miller, M.; Moeykens, S.; et al. A Social-Ecological System Framework for Marine Aquaculture Research. Sustainability 2019 , 11 , 2522. [ Google Scholar ] [ CrossRef ]
• Gentry, R.R.; Alleway, H.K.; Bishop, M.J.; Gillies, C.L.; Waters, T.; Jones, R. Exploring the Potential for Marine Aquaculture to Contribute to Ecosystem Services. Rev. Aquac. 2020 , 12 , 499–512. [ Google Scholar ] [ CrossRef ]
• Bradley, D.; Merrifield, M.; Miller, K.M.; Lomonico, S.; Wilson, J.R.; Gleason, M.G. Opportunities to Improve Fisheries Management through Innovative Technology and Advanced Data Systems. Fish Fish. 2019 , 20 , 564–583. [ Google Scholar ] [ CrossRef ]
• Blasiak, R.; Anderson, J.L.; Bridgewater, P.; Furuya, K.; Halpern, B.S.; Kurokura, H.; Morishita, J.; Yagi, N.; Minohara, A. Paradigms of Sustainable Ocean Management. Mar. Policy 2014 , 48 , 206–211. [ Google Scholar ] [ CrossRef ]
• Sun, C.-L.; Wang, S.-P.; Porch, C.E.; Yeh, S.-Z. Sex-Specific Yield per Recruit and Spawning Stock Biomass per Recruit for the Swordfish, Xiphias Gladius, in the Waters around Taiwan. Fish. Res. 2005 , 71 , 61–69. [ Google Scholar ] [ CrossRef ]
• DeMartini, E.E.; Everson, A.R.; Nichols, R.S. Estimates of Body Sizes at Maturation and at Sex Change, and the Spawning Seasonality and Sex Ratio of the Endemic Hawaiian Grouper ( Hyporthodus quernus , F. Epinephelidae). Fish Bull. 2010 , 109 , 123–134. [ Google Scholar ]
• Arocha, F. Management Implications in Using Spawning Stock Biomass as a Proxy for Total Egg Production in a Highly Fecund Species: The Swordfish Case. In Proceedings of the 51st Gulf and Caribbean Fisheries Institute Proceedings, November 1998 ; Gulf and Caribbean Fisheries Institute: Marathon, FL, USA, 2000; pp. 629–645. [ Google Scholar ]
• Tully, O.; Roantree, V.; Robinson, M. Maturity, Fecundity and Reproductive Potential of the European Lobster ( Homarus gammarus ) in Ireland. J. Mar. Biol. Assoc. United Kingd. 2001 , 81 , 61–68. [ Google Scholar ] [ CrossRef ]
• Luers, M.A.; DeMartini, E.E.; Humphreys, R.L. Seasonality, Sex Ratio, Spawning Frequency and Sexual Maturity of the Opakapaka Pristipomoides Filamentosus (Perciformes: Lutjanidae) from the Main Hawaiian Islands: Fundamental Input to Size-at-Retention Regulations. Mar. Freshw. Res. 2017 , 69 , 325–335. [ Google Scholar ] [ CrossRef ]
• Park, J.W.; Graves, D.; Draves, R.; Yongsawatdigul, J. Manufacture of Surimi. In Surimi and Surimi Seafood , 3rd ed.; CPC Press: Boca Raton, FL, USA, 2013; pp. 55–96. [ Google Scholar ]
• Pangsorn, S.; Laong-manee, P.; Siriraksophon, S. Status of Surimi Industry in the Southeast Asia ; Southeast Asian Fisheries Development Center: Laem Fa Pha, Thailand, 2007; p. 29. [ Google Scholar ]
• Sonu, S.C. Surimi ; NOAA Technical Memorandum NMFS; National Marine Fisheries Service: Sacramento, CA, USA, 1986; p. 135. [ Google Scholar ]

Click here to enlarge figure

Share and Cite

Nugroho, K.C.; Zulbainarni, N.; Asikin, Z.; Budijanto, S.; Marimin, M. Toward a Sustainable Surimi Industry: Comprehensive Review and Future Research Directions of Demersal Fish Stock Assessment Techniques. Sustainability 2024 , 16 , 7759. https://doi.org/10.3390/su16177759

Nugroho KC, Zulbainarni N, Asikin Z, Budijanto S, Marimin M. Toward a Sustainable Surimi Industry: Comprehensive Review and Future Research Directions of Demersal Fish Stock Assessment Techniques. Sustainability . 2024; 16(17):7759. https://doi.org/10.3390/su16177759

Nugroho, Kuncoro Catur, Nimmi Zulbainarni, Zenal Asikin, Slamet Budijanto, and Marimin Marimin. 2024. "Toward a Sustainable Surimi Industry: Comprehensive Review and Future Research Directions of Demersal Fish Stock Assessment Techniques" Sustainability 16, no. 17: 7759. https://doi.org/10.3390/su16177759

Article Metrics

Article access statistics, further information, mdpi initiatives, follow mdpi.

Subscribe to receive issue release notifications and newsletters from MDPI journals

Disentangling the dynamic digital capability, digital transformation, and organizational performance relationships in SMEs: a configurational analysis based on fsQCA

• Published: 02 September 2024

Cite this article

• Hande Karadağ   ORCID: orcid.org/0000-0002-5388-807X 1 ,
• Faruk Şahin   ORCID: orcid.org/0000-0001-5013-8903 2 , 5 ,
• Nazlı Karamollaoğlu   ORCID: orcid.org/0000-0003-3897-4794 3 &
• Minna Saunila   ORCID: orcid.org/0000-0001-8952-6102 4  

While digitalization has become inevitable for firms of every size, a limited number of studies to date aimed to investigate the impact of digital capabilities and digital transformation on the organizational performance of small businesses. Drawing on the dynamic capabilities view, the current study analyzes the conditions under which the dynamic digital capability of a small and medium-sized enterprise (SME) would lead to higher performance. In this study, a unique fuzzy-set qualitative comparative analysis methodology was used for analyzing the data collected from 136 SMEs for investigating the IT utilization, human capital, digital maturity, and digitalization strategy antecedents of dynamic digital capability. The results reveal that two particular configurations of dynamic digital capability are identified as the main digitalization influencers of organizational performance in SMEs. To the best of our knowledge, this study presents the first empirical findings to the literature about dynamic digital capability and organizational performance relationships in SMEs through the utilization of configurational analysis methodology. Theoretically, the study addresses an acknowledged need for a holistic approach to uncover the underlying mechanisms of dynamic digital capability formation and digital transformation in small firms, with their impact on firm performance. The findings also present vital practical implications for business owners, policy-makers, and bodies responsible for SMEs, by providing new insights about the combination of factors that drive high performance, particularly at times of turbulence, in these units.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save.

• Get 10 units per month
• Download Article/Chapter or eBook
• 1 Unit = 1 Article or 1 Chapter
• Cancel anytime

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

Similar content being viewed by others

Beyond digital transformation: a multi-mixed methods study on big data analytics capabilities and innovation in enhancing organizational performance

Keeping pace with the digital transformation — exploring the digital orientation of SMEs

Competitive advantage and dynamic capability in small and medium-sized enterprises: a systematic literature review and future research directions

Explore related subjects.

• Artificial Intelligence

Kergroach, S. (2021), "SMEs Going Digital: Policy challenges and recommendations", OECD Going Digital Toolkit Notes, No. 15, OECD Publishing, Paris, https://doi.org/10.1787/c91088a4-en

Clemente-Almendros JA, Nicoara-Popescu D, Pastor-Sanz I (2024) Digital transformation in SMEs: Understanding its determinants and size heterogeneity. Technol Soc 77:102483

Article   Google Scholar  

Matarazzo M, Penco L, Profumo G, Quaglia R (2021) Digital transformation and customer value creation in Made in Italy SMEs: a dynamic capabilities perspective. J Bus Res 123:642–656

Omrani N, Rejeb N, Maalaoui A, Dabić M, Kraus S (2022) Drivers of digital transformation in SMEs. IEEE Trans Eng Manag 71:5030–5043

Kumar S, Sahoo S, Lim WM, Kraus S, Bamel U (2022) Fuzzy-set qualitative comparative analysis (fsQCA) in business and management research: a contemporary overview. Technol Forecast Soc Change 178:121599

Modgil S, Dwivedi YK, Rana NP, Gupta S, Kamble S (2022) Has COVID-19 accelerated opportunities for digital entrepreneurship? An Indian perspective. Technol Forecast Soc Change 175:121415

Nandi S, Sarkis J, Hervani A, Helms M (2020) Do blockchain and circular economy practices improve post-COVID-19 supply chains? A resource-based and resource-dependence perspective. Ind Manag Data Syst 121(2):333–363

Nazir S, Price B, Surendra NC, Kopp K (2022) Adapting agile development practices for hyper-agile environments: lessons learned from a COVID-19 emergency response research project. Inf Technol Manag 23(3):193–211

Skare M, de Obesso MDLM, Ribeiro-Navarrete S (2023) Digital transformation and European small and medium enterprises (SMEs): A comparative study using digital economy and society index data. Int J Inf Manag 68:102594

Bharadwaj AS (2000) A resource-based perspective on information technology capability and firm performance: an empirical investigation. MIS Q 24(1):169

Eller R, Alford P, Kallmünzer A, Peters M (2020) Antecedents, consequences, and challenges of small and medium-sized enterprise digitalization. J Bus Res 112:119–127

Bai C, Quayson M, Sarkis J (2021) COVID-19 pandemic digitization lessons for sustainable development of micro-and small-enterprises. Sustain Prod Consum 27:1989–2001

Ma F, Khan F, Khan KU, XiangYun S (2021) Investigating the impact of information technology, absorptive capacity, and dynamic capabilities on firm performance: an empirical study. SAGE Open 11(4):21582440211061388

Teece DJ, Pisano G, Shuen A (1997) Dynamic capabilities and strategic management. Strateg Manag J 18(7):509–533

Pisano G, Teece D (1994) The dynamic capabilities of firms: an introduction. Ind Corp Change 3(3):537–556

Ashley C, Tuten T (2015) Creative strategies in social media marketing: an exploratory study of branded social content and consumer engagement. Psychol Mark 33(1):15–27

Giotopoulos I, Kontolaimou A, Korra E, Tsakanikas A (2017) What drives ICT adoption by SMEs? Evidence from a large-scale survey in Greece. J Bus Res 81:60–69

Zhang M, Zhou Y, Li L, Gong B (2022) Manufacturing firms’ E-commerce adoption and performance: evidence from a large survey in Jiaxing China. Inf Technol Manag 24(4):313–335

Li L, Su F, Zhang W, Mao JY (2018) Digital transformation by SME entrepreneurs: a capability perspective. Inf Sys J 28(6):1129–1157

Loebbecke C, Wareham J (2003) The impact of eBusiness and the information society on ‘Strategy’ and ‘Strategıc Planning’: an assessment of new concepts and challenges. Inf Technol Manag 4:165–182

Wang X, Gu Y, Ahmad M, Xue C (2022) the impact of digital capability on manufacturing company performance. Sustainability 14(10):6214

Cooper V, Molla A (2017) Information systems absorptive capacity for environmentally driven IS-enabled transformation. Inf Sys J 27(4):379–425

Neirotti P, Raguseo E (2017) On the contingent value of IT-based capabilities for the competitive advantage of SMEs: mechanisms and empirical evidence. Inf Manag 54(2):139–153

Steininger DM, Mikalef P, Pateli A, Ortiz-de-Guinea A (2022) Dynamic capabilities in information systems research: A critical review, synthesis of current knowledge, and recommendations for future research. J Assoc Inf Sys 23(2):447–490

Google Scholar  

Wilden R, Gudergan SP, Nielsen BB, Lings I (2013) Dynamic capabilities and performance: strategy. Struct Environ Long Range Plan 46(1):72–96

Warner KS, Wäger M (2019) Building dynamic capabilities for digital transformation: an ongoing process of strategic renewal. Long-range plann 52(3):326–349

Park YoungKi, El Sawy OA, Fiss PC (2017) The role of business intelligence and communication technologies in organizational agility: a configurational approach. J Assoc Inf Sys 18(9):648–686

Ragin CC (2000) Fuzzy-set social science. University of Chicago Press, Chicago

Pattij M, van de Wetering R, Kusters R (2022) Enhanced digital transformation supporting capabilities through enterprise architecture management: a fsQCA perspective. Digit Bus 2(2):100036

Zhang J, Long J, von Schaewen AME (2021) How does digital transformation improve organizational resilience?—findings from PLS-SEM and fsQCA. Sustainability 13(20):11487

Greckhamer T, Furnari S, Fiss PC, Aguilera RV (2018) Studying configurations with qualitative comparative analysis: Best practices in strategy and organization research. Strateg Organ 16:482–495

Cao D, Wang Y, Berkeley N, Tjahjono B (2022) Configurational conditions and sustained competitive advantage: a fsQCA approach. Long Range Plan 55(4):102131

Teece DJ, Linden G (2017) Business models, value capture, and the digital enterprise. J Organ Des 6:1–14

Barney J (1991) Firm resources and sustained competitive advantage. J Manag 17(1):99–120

Peteraf MA (1993) The cornerstones of competitive advantage: a resource-based view. Strateg Manag J 14(3):179–191

Kesting P, Günzel-Jensen F (2015) SMEs and new ventures need business model sophistication. Bus Horiz 58(3):285–293

Suguna M, Shah B, Sivakami BU, Suresh M (2022) Factors affecting repurposing operations in micro small and medium enterprises during COVID-19 emergency. Op Manag Res 15(3–4):1181–1197

Brunswicker S, Ehrenmann F (2013) Managing open innovation in SMEs: a good practice example of a German software firm. Int J Ind Eng Manag 4(1):33

Krishnamurthy S (2020) The future of business education: a commentary in the shadow of the Covid-19 pandemic. J Bus Res 117:1–5

Asiaei K, Bontis N (2020) Translating knowledge management into performance–the role of performance measurement systems. Manag Res Rev 43(1):113–132

Tzortzaki AM (2014) Knowledge-based strategies for managers in the service sector. Manag Res Rev 37(10):858–879

Khuntia, J., Saldanha, T., Kathuria, A., & Tanniru, M. R. (2024). Digital service flexibility: a conceptual framework and roadmap for digital business transformation. Eur J Inf Syst 33(1):61-79.

Chen Y, Wang Y, Nevo S, Benitez J, & Kou G (2017) Improving strategic flexibility with information technologies: Insights for firm performance in an emerging economy. J Inf Technol 32(1):10–25. https://doi.org/10.1057/jit.2015.26

Depaoli P, Za S, Scornavacca E (2020) A model for digital development of SMEs: an interaction-based approach. J Small Bus Enterp Dev 27(7):1049–1068

Carcary M, Doherty E, ve Conway G (2016) A dynamic capability approach to digital transformation: a focus on key foundational themes. The European Conference On Information Systems Management Proceedings, Academic Conferences International Limited, s.20.

Reis J, Amorim M, Melão N, Cohen Y, Rodrigues M (2019) Digitalization: a literature review and research agenda. In: International Joint conference on industrial engineering and operations management (pp. 443-456). Springer, Cham

Malodia S, Mishra M, Fait M, Papa A, Dezi L (2023) To digit or to head? Designing digital transformation journey of SMEs among digital self-efficacy and professional leadership. J Bus Res 157:113547

Teece DJ (2014) A dynamic capabilities-based entrepreneurial theory of the multinational enterprise. J Int Bus Stud 45(1):8–37

Papadopoulos T, Baltas KN, Balta ME (2020) The use of digital technologies by small and medium enterprises during COVID-19: implications for theory and practice. Int J Inf Manag 55:102192

Nylén D, Holmström ve J (2015) Digital innovation strategy: a framework for diagnosing and improving digital product and service innovation. Bus Horizons 58(1):57–67

Chen CL, Lin YC, Chen WH, Chao CF, Pandia H (2021) Role of government to enhance digital transformation in small service business. Sustainability 13(3):1–26

Khan O, Daddi T, Iraldo F (2021) Sensing, seizing, and reconfiguring: key capabilities and organizational routines for circular economy implementation. J Clean Prod 287:125565

Annarelli A, Battistella C, Nonino F, Parida V, Pessot E (2021) Literature review on digitalization capabilities: co-citation analysis of antecedents, conceptualization, and consequences. Technol. Forecast. Soc. Change 166:120635

Troise C, Corvello V, Ghobadian A & O'Regan N (2022) How can SMEs successfully navigate VUCA environment: The role of agility in the digital transformation era. Technol Forecast Soc Change 174:121227

Ghezzi A, & Cavallo A (2020) Agile business model innovation in digital entrepreneurship: Lean startup approaches. J Bus Res 110:519-537

Birkinshaw J, Visnjic I, Best S (2018) Responding to a potentially disruptive technology: how big pharma embraced biotechnology. Calif Manag Rev 60(4):74–100

Bharadwaj A, El Sawy OA, Pavlou PA, ve Venkatraman NV (2013) Digital business strategy: toward a next generation of insights. MIS Q 37(2):471–482

Matalamäki MJ, Joensuu-Salo S (2022) Digitalization and strategic flexibility–a recipe for business growth. J Small Bus Enterp Dev 29(3):380–401

Garbellano S, Da Veiga MR (2019) Dynamic capabilities in Italian leading SMEs adopting industry 4 0. Meas Bus Excell 23(4):472–483

OECD (2017) Key issues for digital transformation in the G20. Berlin, Germany. Available at < https://www.oecd.org/g20/key-issues-for-digital-transformation-in the-g20.pdf > (Accessed August 2, 2022)

Teng X, Wu Z, Yang F (2022) Research on the relationship between digital transformation and performance of SMEs. Sustainability 14(10):6012

Mikalef P, Krogstie J, Pappas IO, Pavlou P (2020) Exploring the relationship between big data analytics capability and competitive performance: the mediating roles of dynamic and operational capabilities. Inf Manag 57(2):103169

Panagiotopoulos P, Protogerou A, Caloghirou Y (2023) Dynamic capabilities and ICT utilization in public organizations: an Empirical testing in local government. Long Range Plan 56(1):102251

Sousa MJ, Rocha Á (2019) Skills for disruptive digital business. J Bus Res 94:257–263. https://doi.org/10.1016/j.jbusres.2017.12.051.S

Marchiori DM, Rodrigues RG, Popadiuk S, Mainardes EW (2022) The relationship between human capital, information technology capability, innovativeness and organizational performance: an integrated approach. Technol Forecast Soc Change 177:121526

Nguyen TH, Newby M, Macaulay MJ (2015) Information technology adoption in small business: Confirmation of a proposed framework. J Small Bus Manag 53(1):207–227

Chatterjee D, Grewal R, Sambamurthy V (2002) Shaping up for e-commerce: institutional enablers of the organizational assimilation of web technologies. MIS Q 26(2):65–89

Becker W, Schmid O (2020) The right digital strategy for your business: an empirical analysis of the design and implementation of digital strategies in SMEs and LSEs. Bus Res 13(3):985–1005

Goodwin P, Wright G (2001) Enhancing strategy evaluation in scenario planning: a role for decision analysis. J Manag Stud 38(1):1–16

Leidner DE, Lo J, & Preston D (2011) An empirical investigation of the relationship of IS strategy with firm performance. J Strateg Inf Syst 20(4):419–437

Parida V, R€onnberg Sj€odin D, Lenka S, Wincent J (2015) Developing global service innovation capabilities how global manufacturers address the challenges of market heterogeneity. Res Technol Manag 58(5):35–44

Montealegre R (2002) A process model of capability development: lessons from the electronic commerce strategy at Bolsa de Valores de Guayaquil. Organ Sci 13(5):514–531

Matt C, Hess T, Benlian A (2015) Digital transformation strategies. Bus Inf Sys Eng 57:339–343

Ghobakhloo M (2020) Determinants of information and digital technology implementation for smart manufacturing. Int J Prod Res 58(8):2384–2405

Kane GC, Palmer D, Phillips AN, Kiron D, Buckley N (2015) Strategy, not technology, drives digital transformation. MIT Sloan Manag Rev 57(14):1–25

Çallı BA, Çallı L (2021) Relationships between digital maturity, organizational agility, and firm performance: an empirical investigation on SMEs. Bus Manag Stud Int J 9(2):486–502

Berghaus S, Back A (2016) Stages in digital business transformation: results of an empirical maturity study. In MCIS (pp. 22)

Verhoef PC, Broekhuizen T, Bart Y, Bhattacharya A, Dong JQ, Fabian N, Haenlein M (2019) Digital transformation: a multidisciplinary reflection and research agenda. J Bus Res 122(2021):889–901

Bouncken R, Kraus S, Roig-Tierno N (2019) Knowledge- and innovation-based business models for future growth: Digitalized business models and portfolio considerations. Rev Manag Sci 46:1–14

Nambisan S (2017) Digital entrepreneurship: Toward a digital technology perspective of entrepreneurship. Entrepr Theory Pract 41(6):1029–1055

Drnevich PL, Kriauciunas AP (2011) Clarifying the conditions and limits of the contributions of ordinary and dynamic capabilities to relative firm performance. Strateg Manag J 32(3):254–279

Akkaya B, Qaisar I (2021) Linking dynamic capabilities and market performance of SMEs: the moderating role of organizational agility. Istanb Bus Res 50(2):197–214

Bezci H İ (2015) Dinamik Kabiliyetlere Sahip İşletmelerin İnovasyon Hızı, Gebze Teknik Üniversitesi Sosyal Bilimler Enstitüsü (Master Thesis), Gebze, Kocaeli

McKelvie A, Davidsson P (2009) From resource base to dynamic capabilities: an investigation of new firms. Br J Manag 20:S63–S80

Dong JQ, Wu W (2015) Business value of social media technologies: evidence from online user innovation communities. J Strateg Inf Sys 24(2):113–127

Chen DQ, Preston DS, Swink M (2015) How the use of big data analytics affects value creation in supply chain management. J Manag Inf Sys 32(4):4–39

Wang G, Dou W, Zhu W, Zhou N (2015) The effects of firm capabilities on external collaboration and performance: the moderating role of market turbulence. J Bus Res 68(9):1928–1936

Street C, Gallupe B, Baker J (2017) Strategic alignment in smes: strengthening theoretical foundations. Commun Assoc Inf Sys 40(1):239–266

Heredia J, Castillo-Vergara M, Geldes C, Gamarra FMC, Flores A, Heredia W (2022) How do digital capabilities affect firm performance? The mediating role of technological capabilities in the “new normal.” J Innov Knowl 7(2):100171

Davidsson P, Honig B (2003) The role of social and human capital among nascent entrepreneurs. J Bus Ventur 18(3):301–331

Kor YY, Sundaramurthy C (2009) Experience-based human capital and social capital of outside directors. J Manag 35(4):981–1006

Chu Y, Chi M, Wang W, Luo B (2019) The impact of information technology capabilities of manufacturing enterprises on innovation performance: evidence from SEM and fsQCA. Sustainability 11(21):5946

Li L, Ye F, Zhan Y, Kumar A, Schiavone F, Li Y (2022) Unraveling the performance puzzle of digitalization: evidence from manufacturing firms. J Bus Res 149:54–64

Escriba-Esteve A, Sanchez-Peinado L, Sanchez-Peinado E (2008) Moderating influences on the firm’s strategic orientation performance relationship. Int Small Bus J 26:463–489

Wijewardena H, Nanayakkara G, De Zoysa A (2008) The owner/manager’s mentality and the financial performance of SMEs. J Small Bus Enterp Dev 15(1):150–161

Miller CC, Cardinal LB (1994) Strategic planning and firm performance: a synthesis of more than two decades of research. Acad Manag J 37(6):1649–1665

Woodside AG (2013) Moving beyond multiple regression analysis to algorithms: calling for adoption of a paradigm shift from symmetric to asymmetric thinking in data analysis and crafting theory. J Bus Res 66(4):463–472

Kraus S, Ribeiro-Soriano D, Schüssler M (2018) Fuzzy-set qualitative comparative analysis (fsQCA) in entrepreneurship and innovation research—the rise of a method. Int Entrep Manag J 14(1):15–33

Ragin CC (2008) Redesigning social inquiry: fuzzy sets and beyond, Illustrated edition. University of Chicago Press, Chicago

Book   Google Scholar  

Schneider CQ, Wagemann C (2012) Set-theoretic methods for the social sciences: a guide to qualitative comparative analysis Strategies for Social Inquiry. Cambridge University Press, Cambridge

Ragin CC, Davey S (2016) Fuzzy-Set/Qualitative Comparative Analysis. University of California, California

Pappas IO, Woodside AG (2021) Fuzzy-set Qualitative Comparative Analysis (fsQCA): guidelines for research practice in information systems and marketing. Int J Inf Manag 58:102310

Ragin CC (2006) Set relations in social research: evaluating their consistency and coverage. Polit Anal 14:291–310

Fiss PC (2011) Building better causal theories: A fuzzy set approach to typologies in organizational research. Acad Manag J 54:393–420

García-Castro R, Aguilera R, Ariño M (2013) Bundles of firm corporate governance practices: a fuzzy set analysis. Corp Gov Int Rev 21(4):390–407

Ordanini A, Parasuraman A, Rubera G (2014) When the recipe is more important than the ingredients: a qualitative comparative analysis (QCA) of service innovation configurations. J Serv Res 17:134–149

Fiss PC (2007) A set-theoretic approach to organizational configurations. Acad Manag Rev 32(4):1180–1198

Rubinson C (2019) Presenting qualitative comparative analysis: Notation, tabular layout, and visualization. Methodol Innov 12:2059799119862110

Khurana I, Dutta DK, Ghura AS (2022) SMEs and digital transformation during a crisis: the emergence of resilience as a second-order dynamic capability in an entrepreneurial ecosystem. J Bus Res 150:623–641

Gimpel G, Westerman G (2012) Shaping the future: Seven enduring principles for fast-changing industries. MIT Center for Digital Business

Vaia G, Arkhipova D, DeLone W (2022) Digital governance mechanisms and principles that enable agile responses in dynamic competitive environments. Eur J Inf Sys 31(6):662–680

Nasiri M, Saunila M, Ukko J (2022) Digital orientation, digital maturity, and digital intensity: determinants of financial success in digital transformation settings. Int J Op Prod Manag 42(13):274–298

Giotopoulos I, Kontolaimou A, Tsakanikas A (2022) Digital responses of SMEs to the COVID-19 crisis. Int J Entrep Behav Res 28(7):1751–1772

Bienhaus F, Haddud A (2018) Procurement 4.0: factors influencing the digitisation of procurement and supply chains. Bus Process Manag J 24(4):965–984

Greckhamer T, Misangyi VF, Fiss PC (2013) The two QCAs: From a small-N to a large-N set-theoretic approach. Config Theory Methods Organ Res 3:49–75

Gruber M, Heinemann F, Brettel M, Hungeling S (2010) Configurations of resources and capabilities and their performance implications: an exploratory study on technology ventures. Strateg Manag J 31(12):1337–1356

Download references

Author information

Authors and affiliations.

Faculty of Economics, Administrative and Social Sciences, MEF University, Istanbul, Turkey

Hande Karadağ

Fethiye Faculty of Business Administration, Muğla Sıtkı Koçman University, 48300, Fethiye - Muğla, Turkey

Faruk Şahin

Nazlı Karamollaoğlu

School of Engineering Sciences, Department of Industrial Engineering and Management, LUT University, Lahti, Finland

Minna Saunila

College of Accounting Sciences, University of South Africa (UNISA), Pretoria, South Africa

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Hande Karadağ .

Ethics declarations

Conflict of interest.

The authors declare no conflict of interest.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Karadağ, H., Şahin, F., Karamollaoğlu, N. et al. Disentangling the dynamic digital capability, digital transformation, and organizational performance relationships in SMEs: a configurational analysis based on fsQCA. Inf Technol Manag (2024). https://doi.org/10.1007/s10799-024-00437-y

Download citation

Accepted : 10 August 2024

Published : 02 September 2024

DOI : https://doi.org/10.1007/s10799-024-00437-y

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Dynamic digital capability
• Digital transformation
• Small to medium-sized enterprises
• Organizational innovation; Configurational approach
• Find a journal
• Publish with us
• Track your research

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• Am J Pharm Educ
• v.84(1); 2020 Jan

A Review of the Quality Indicators of Rigor in Qualitative Research

Jessica l. johnson.

a William Carey University School of Pharmacy, Biloxi, Mississippi

Donna Adkins

Sheila chauvin.

b Louisiana State University, School of Medicine, New Orleans, Louisiana

Attributes of rigor and quality and suggested best practices for qualitative research design as they relate to the steps of designing, conducting, and reporting qualitative research in health professions educational scholarship are presented. A research question must be clear and focused and supported by a strong conceptual framework, both of which contribute to the selection of appropriate research methods that enhance trustworthiness and minimize researcher bias inherent in qualitative methodologies. Qualitative data collection and analyses are often modified through an iterative approach to answering the research question. Researcher reflexivity, essentially a researcher’s insight into their own biases and rationale for decision-making as the study progresses, is critical to rigor. This article reviews common standards of rigor, quality scholarship criteria, and best practices for qualitative research from design through dissemination.

INTRODUCTION

Within the past 20 years, qualitative research in health professions education has increased significantly, both in practice and publication. Today, one can pick up most any issue of a wide variety of health professions education journals and find at least one article that includes some type of qualitative research, whether a full study or the inclusion of a qualitative component within a quantitative or mixed methods study. Simultaneously, there have been recurrent calls for enhancing rigor and quality in qualitative research.

As members of the academic community, we share responsibility for ensuring rigor in qualitative research, whether as researchers who design and implement, manuscript reviewers who critique, colleagues who discuss and learn from each other, or scholarly teachers who draw upon results to enhance and innovate education. Therefore, the purpose of this article is to summarize standards of rigor and suggested best practices for designing, conducting, and reporting high-quality qualitative research. To begin, Denzin and Lincoln’s definition of qualitative research, a long-standing cornerstone in the field, provides a useful foundation for summarizing quality standards and best practices:

Qualitative research involves the studied use and collection of a variety of empirical materials – case study; personal experience; introspection; life story; interview; artifacts; cultural texts and productions; observational, historical, interactional, and visual texts – that describe the routine and problematic moments and meanings in individual lives. Accordingly, qualitative researchers deploy a wide range of interconnected interpretative practices, hoping always to get a better understanding of the subject matter at hand. It is understood, however, that each practice makes the world visible in a different way. Hence there is frequently a commitment to using more than one interpretative practice in any study. 1

In recent years, multiple publications have synthesized quality criteria and recommendations for use by researchers and peer reviewers alike, often in the form of checklists. 2-6 Some authors have raised concerns about the use of such checklists and adherence to strict, universal criteria because they do not afford sufficient flexibility to accommodate the diverse approaches and multiple interpretive practices often represented in qualitative studies. 7-11 They argue that a strict focus on using checklists of specific technical criteria may stifle the diversity and multiplicity of practices that are so much a part of achieving quality and rigor within the qualitative paradigm. As an alternative, some of these authors have published best practice guidelines for use by researchers and peer reviewers to achieve and assess methodological rigor and research quality. 12,13

Some journals within the field of health professions education have also established best practice guidance, as opposed to strict criteria or a checklist, for qualitative research. These have been disseminated as guiding questions or evaluation categories. In 2015, Academic Medicine produced an expanded second edition of a researcher/author manual that includes specific criteria with extensive explanations and examples. 14 Still others have disseminated best practice guidelines through a series of methodological articles within journal publications. 2

In this article, attributes of rigor and quality and suggested best practices are presented as they relate to the steps of designing, conducting, and reporting qualitative research in a step-wise approach.

BEST PRACTICES: STEP-WISE APPROACH

Step 1: identifying a research topic.

Identifying and developing a research topic is comprised of two major tasks: formulating a research question, and developing a conceptual framework to support the study. Formulating a research question is often stimulated by real-life observations, experiences, or events in the researcher’s local setting that reflect a perplexing problem begging for systematic inquiry. The research question begins as a problem statement or set of propositions that describe the relationship among certain concepts, behaviors, or experiences. Agee 15 and others 16,17 note that initial questions are usually too broad in focus and too vague regarding the specific context of the study to be answerable and researchable. Creswell reminds us that initial qualitative research questions guide inquiry, but they often change as the author’s understanding of the issue develops throughout the study. 16 Developing and refining a primary research question focused on both the phenomena of interest and the context in which it is situated is essential to research rigor and quality.

Glassick, Huber, and Maeroff identified six criteria applicable to assessing the quality of scholarship. 18,19 Now commonly referred to as the Glassick Criteria ( Table 1 ), these critical attributes outline the essential elements of any scholarly approach and serve as a general research framework for developing research questions and designing studies. The first two criteria, clear purpose and adequate preparation, are directly related to formulating effective research questions and a strong conceptual framework.

Glassick’s Criteria for Assessing the Quality of Scholarship of a Research Study 18

Generating and refining a qualitative research question requires thorough, systematic, and iterative review of the literature, and the use of those results to establish a clear context and foundation for the question and study design. Using an iterative approach, relevant concepts, principles, theories or models, and prior evidence are identified to establish what is known, and more importantly, what is not known. The iterative process contributes to forming a better research question, the criteria for which can be abbreviated by the acronym FINER, ie, f easible, i nteresting, n ovel, e thical, and r elevant, that is answerable and researchable, in terms of research focus, context specificity, and the availability of time, logistics, and resources to carry out the study. Developing a FINER research question is critical to study rigor and quality and should not be rushed, as all other aspects of research design depend on the focus and clarity of the research question(s) guiding the study. 15 Agee provides clear and worthwhile additional guidance for developing qualitative research questions. 15

Reflexivity, the idea that a researcher’s preconceptions and biases can influence decisions and actions throughout qualitative research activities, is a critical aspect of rigor even at the earliest stages of the study. A researcher’s background, beliefs, and experiences may affect any aspect of the research from choosing which specific question to investigate through determining how to present the results. Therefore, even at this early stage, the potential effect of researcher bias and any ethical considerations should be acknowledged and addressed. That is, how will the question’s influence on study design affect participants’ lives, position the researcher in relationship with others, or require specific methods for addressing potential areas of research bias and ethical considerations?

A conceptual framework is then actively constructed to provide a logical and convincing argument for the research. The framework defines and justifies the research question, the methodology selected to answer that question, and the perspectives from which interpretation of results and conclusions will be made. 5,6,20 Developing a well-integrated conceptual framework is essential to establishing a research topic based upon a thorough and integrated review of relevant literature (addressing Glassick criteria #1 and #2: clear purpose and adequate preparation). Key concepts, principles, assumptions, best practices, and theories are identified, defined, and integrated in ways that clearly demonstrate the problem statement and corresponding research question are answerable, researchable, and important to advancing thinking and practice.

Ringsted, Hodges, and Sherpbier describe three essential parts to an effective conceptual framework: theories and/or concepts and principles relevant to the phenomenon of interest; what is known and unknown from prior work, observations, and examples; and the researcher’s observations, ideas, and suppositions regarding the research problem statement and question. 21 Lingard describes four types of unknowns to pursue during literature review: what no one knows; what is not yet well understood; what controversy or conflicting results, understandings, or perspectives exist; and what are unproven assumptions. 22 In qualitative research, these unknowns are critical to achieving a well-developed conceptual framework and a corresponding rigorous study design.

Recent contributions from Ravitch and colleagues present best practices in developing frameworks for conceptual and methodological coherence within a study design, regardless of the research approach. 23,24 Their recommendations and arguments are highly relevant to qualitative research. Figure 1 reflects the primary components of a conceptual framework adapted from Ravitch and Carl 23 and how all components contribute to decisions regarding research design, implementation, and applications of results to future thinking, study, and practice. Notice that each element of the framework interacts with and influences other elements in a dynamic and interactive process from the beginning to the end of a research project. The intersecting bidirectional arrows represent direct relationships between elements as they relate to specific aspects of a qualitative research study.

Adaptation of Ravitch and Carl’s Components of a Conceptual Framework 23

Maxwell also provides useful guidance for developing an effective conceptual framework specific to the qualitative research paradigm. 17 The 2015 second edition of the Review Criteria for Research Manuscripts 14 and work by Ravitch and colleagues 23,24 provide specific guidance for applying the conceptual framework to each stage of the research process to enhance rigor and quality. Quality criteria for assessing a study’s problem statement, conceptual framework, and research question include the following: introduction builds a logical case and provides context for the problem statement; problem statement is clear and well-articulated; conceptual framework is explicit and justified; research purpose and/or question is clearly stated; and constructs being investigated are clearly identified and presented. 14,24,25 As best practice guidelines, these criteria facilitate quality and rigor while providing sufficient flexibility in how each is achieved and demonstrated.

While a conceptual framework is important to rigor in qualitative research, Huberman and Miles caution qualitative researchers about developing and using a framework to the extent that it influences qualitative design deductively because this would violate the very principles of induction that define the qualitative research paradigm. 25 Our profession’s recent emphasis on a holistic admissions process for pharmacy students provides a reasonable example of inductive and deductive reasoning and their respective applications in qualitative and quantitative research studies. Principles of inductive reasoning are applied when a qualitative research study examines a representative group of competent pharmacy professionals to generate a theory about essential cognitive and affective skills for patient-centered care. Deductive reasoning could then be applied to design a hypothesis-driven prospective study that compares the outcomes of two cohorts of students, one group admitted using traditional criteria and one admitted based on a holistic admissions process revised to value the affective skills of applicants. Essentially, the qualitative researcher must carefully generate a conceptual framework that guides the research question and study design without allowing the conceptual framework to become so rigid as to dictate a testable hypothesis, which is the founding principle of deductive reasoning. 26

Step 2: Qualitative Study Design

The development of a strong conceptual framework facilitates selection of appropriate study methods to minimize the bias inherent in qualitative studies and help readers to trust the research and the researcher (see Glassick criteria #3 in Table 1 ). Although researchers can employ great flexibility in the selection of study methods, inclusion of best practice methods for assuring the rigor and trustworthiness of results is critical to study design. Lincoln and Guba outline four criteria for establishing the overall trustworthiness of qualitative research results: credibility, the researcher ensures and imparts to the reader supporting evidence that the results accurately represent what was studied; transferability, the researcher provides detailed contextual information such that readers can determine whether the results are applicable to their or other situations; dependability, the researcher describes the study process in sufficient detail that the work could be repeated; confirmability, the researcher ensures and communicates to the reader that the results are based on and reflective of the information gathered from the participants and not the interpretations or bias of the researcher. 27

Specific best practice methods used in the sampling and data collection processes to increase the rigor and trustworthiness of qualitative research include: clear rationale for sampling design decisions, determination of data saturation, ethics in research design, member checking, prolonged engagement with and persistent observation of study participants, and triangulation of data sources. 28

Qualitative research is focused on making sense of lived, observed phenomenon in a specific context with specifically selected individuals, rather than attempting to generalize from sample to population. Therefore, sampling design in qualitative research is not random but defined purposively to include the most appropriate participants in the most appropriate context for answering the research question. Qualitative researchers recognize that certain participants are more likely to be “rich” with data or insight than others, and therefore, more relevant and useful in achieving the research purpose and answering the question at hand. The conceptual framework contributes directly to determining sample definitions, size, and recruitment of participants. A typical best practice is purposive sampling methods, and when appropriate, convenience sampling may be justified. 29

Purposive sampling reflects intentional selection of research participants to optimize data sources for answering the research question. For example, the research question may be best answered by persons who have particular experience (critical case sampling) or certain expertise (key informant sampling). Similarly, additional participants may be referred for participation by active participants (snowball sampling) or may be selected to represent either similar or opposing viewpoints (confirming or disconfirming samples). Again, the process of developing and using a strong conceptual framework to guide and justify methodological decisions, in this case defining and establishing the study sample, is critical to rigor and quality. 30 Convenience sampling, using the most accessible research participants, is the least rigorous approach to defining a study sample and may result in low accuracy, poor representativeness, low credibility, and lack of transferability of study results.

Qualitative studies typically reflect designs in which data collection and analysis are done concurrently, with results of ongoing analysis informing continuing data collection. Determination of a final sample size is largely based on having sufficient opportunity to collect relevant data until new information is no longer emerging from data collection, new coding is not feasible, and/or no new themes are emerging; that is, reaching data saturation , a common standard of rigor for data collection in qualitative studies . Thus, accurately predicting a sample size during the planning phases of qualitative research can be challenging. 30 Care should be taken that sufficient quantity (think thick description) and quality (think rich description) of data have been collected prior to concluding that data saturation has been achieved. A poor decision regarding sample size is a direct consequence of sampling strategy and quality of data generated, which leaves the researcher unable to fully answer the research question in sufficient depth. 30

Though data saturation is probably the most common terminology used to describe the achievement of sufficient sample size, it does not apply to all study designs. For example, one could argue that in some approaches to qualitative research, data collection could continue infinitely if the event continues infinitely. In education, we often anecdotally observe variations in the personality and structure of a class of students, and as generations of students continue to evolve with time, so too would the data generated from observing each successive class. In such situations, data saturation might never be achieved. Conversely, the number of participants available for inclusion in a sample may be small and some risk of not reaching data saturation may be unavoidable. Thus, the idea of fully achieving data saturation may be unrealistic when applied to some populations or research questions. In other instances, attrition and factors related to time and resources may contribute to not reaching data saturation within the limits of the study. By being transparent in the process and reporting of results when saturation may not have been possible, the resulting data may still contribute to the field and to further inquiry. Replication of the study using other samples and conducting additional types of follow-up studies are other options for better understanding the research phenomenon at hand. 31

In addition to defining the sample and selecting participants, other considerations related to sampling bias may impact the quantity and quality of data generated and therefore the quality of the study result. These include: methods of recruiting, procedures for informed consent, timing of the interviews in relation to experience or emotion, procedures for ensuring participant anonymity/confidentiality, interview setting, and methods of recording/transcribing the data. Any of these factors could potentially change the nature of the relationship between the researcher and the study participants and influence the trustworthiness of data collected or the study result. Thus, ongoing application of previously mentioned researcher reflexivity is critical to the rigor of the study and quality of sampling. 29,30

Common qualitative data collection methods used in health professions education include interview, direct observation methods, and textual/document analysis. Given the unique and often highly sensitive nature of data being collected by the researcher, trustworthiness is an essential component of the researcher-participant relationship. Ethical conduct refers to how moral principles and values are part of the research process. Participants’ perceptions of ethical conduct are fundamental to a relationship likely to generate high quality data. During each step of the research process, care must be taken to protect the confidentiality of participants and shield them from harm relating to issues of respect and dignity. Researchers must be respectful of the participants’ contributions and quotes, and results must be reported truthfully and honestly. 8

Interview methods range from highly structured to increase dependability or completely open-ended to allow for interviewers to clarify a participant’s response for increased credibility and confirmability. Regardless, interview protocols and structure are often modified or refined, based on concurrent data collection and analysis processes to support or refute preliminary interpretations and refine focus and continuing inquiry. Researcher reflexivity, or acknowledgement of researcher bias, is absolutely critical to the credibility and trustworthiness of data collection and analysis in such study designs. 32

Interviews should be recorded and transcribed verbatim prior to coding and analysis. 28 Member checking, a common standard of rigor, is a practice to increase study credibility and confirmability that involves asking a research subject to verify the transcription of an interview. 1,16,28 The research subject is asked to verify the completeness and accuracy of an interview transcript to ensure the transcript truthfully reflects the meaning and intent of the subject’s contribution.

Prolonged engagement involves the researcher gaining familiarity and understanding of the culture and context surrounding the persons or situations being studied. This strategy supports reflexivity, allowing the researcher to determine how they themselves may be a source of bias during the data collection process by altering the nature of how individuals behave or interact with others in the presence of the researcher. Facial expressions, spoken language, body language, style of dress, age, race, gender, social status, culture, and the researcher’s relationship with the participants may potentially influence either participants’ responses or how the researcher interprets those responses. 33 “Fitting in” by demonstrating an appreciation and understanding of the cultural norms of the population being studied potentially allows the researcher to obtain more open and honest responses from participants. However, if the research participants or topic are too familiar or personal, this may also influence data collection or analysis and interpretation of the results. 33 The possible applications of this section to faculty research with student participants in the context of pharmacy education are obvious, and researcher reflexivity is critical to rigor.

Some researchers using observational methods adopt a strategy of direct field observation, while others play partial or full participant roles in the activity being observed. In both observation scenarios, it is impossible to separate the researcher from the environment, and researcher reflexivity is essential. The pros and cons of observation approach, relative to the research question and study purpose, should be evaluated by the researcher, and the justification for the observational strategy selected should be made clear. 34 Regardless of the researcher’s degree of visibility to the study participants, persistent observation of the targeted sample is critical to the confirmability standard and to achieving data saturation. That is, study conclusions must be clearly grounded in persistent phenomena witnessed during the study, rather than on a fluke event. 28

Researchers acknowledge that observational methodologies are limited by the reality that the researcher carries a bias in determining what is observed, what is recorded, how it is recorded, and how it is transcribed for analysis. A study’s conceptual framework is critical to achieving rigor and quality and provides guidance in developing predetermined notions or plans for what to observe, how to record, and how to minimize the influence of potential bias. 34 Researcher notes should be recorded as soon as possible after the observation event to optimize accuracy. The more detailed and complete the notes, the more accurate and useful they can be in data analysis or in auditing processes for enhancing rigor in the interpretation phase of the study. 34

Triangulation is among the common standards of rigor applied within the qualitative research paradigm. Data triangulation is used to identify convergence of data obtained through multiple data sources and methods (eg, observation field notes and interview transcripts) to avoid or minimize error or bias and optimize accuracy in data collection and analysis processes. 33,35,36

Again, researcher practice in reflexivity throughout research processes is integral to rigor in study design and implementation. Researchers must demonstrate attention to appropriate methods and reflective critique, which are represented in both core elements of the conceptual framework ( Figure 1 ) and Glassick criteria ( Table 1 ). In so doing, the researcher will be well-prepared to justify sampling design and data collection decisions to manuscript reviewers and, ultimately, readers.

Step 3: Data Analysis

In many qualitative studies, data collection runs concurrently with data analysis. Specific standards of rigor are commonly used to ensure trustworthiness and integrity within the data analysis process, including use of computer software, peer review, audit trail, triangulation, and negative case analysis.

Management and analyses of qualitative data from written text, observational field notes, and interview transcriptions may be accomplished using manual methods or the assistance of computer software applications for coding and analysis. When managing very large data sets or complex study designs, computer software can be very helpful to assist researchers in coding, sorting, organizing, and weighting data elements. Software applications can facilitate ease in calculating semi-quantitative descriptive statistics, such as counts of specific events, that can be used as evidence that the researcher’s analysis is based on a representative majority of data collected ( inclusivism ) rather than focusing on selected rarities ( anecdotalism ). Using software to code data can also make it easier to identify deviant cases, detect coding errors, and estimate interrater reliability among multiple coders. 37 While such software helps to manage data, the actual analyses and interpretation still reside with the researcher.

Peer review, another common standard of rigor, is a process by which researchers invite an independent third-party researcher to analyze a detailed audit trail maintained by the study author. The audit trail methodically describes the step-by-step processes and decision-making throughout the study. Review of this audit trail occurs prior to manuscript development and enhances study confirmability. 1,16 The peer reviewer offers a critique of the study methods and validation of the conclusions drawn by the author as a thorough check on researcher bias.

Triangulation also plays a role in data analysis, as the term can also be used to describe how multiple sources of data can be used to confirm or refute interpretation, assertions, themes, and study conclusions. If a theme or theory can be arrived at and validated using multiple sources of data, the result of the study has greater credibility and confirmability. 16,33,36 Should any competing or controversial theories emerge during data collection or analysis, it is vital to the credibility and trustworthiness of the study that the author disclose and explore those negative cases. Negative case analysis refers to actively seeking out and scrutinizing data that do not fit or support the researcher’s interpretation of the data. 16

The use of best practices applying to data collection and data analysis facilitates the full examination of data relative to the study purpose and research question and helps to prevent premature closure of the study. Rather than stopping at the initial identification of literal, first-level assertion statements and themes, authors must progress to interpreting how results relate to, revise, or expand the conceptual framework, or offer an improved theory or model for explaining the study phenomenon of interest. Closing the loop on data collection is critical and is achieved when thorough and valid analysis can be linked back to the conceptual framework, as addressed in the next section.

Step 4: Drawing Valid Conclusions

Lingard and Kennedy 38 succinctly state that the purpose of qualitative research is to deepen one’s understanding of specific perspectives, observations, experiences, or events evidenced through the behaviors or products of individuals and groups as they are situated in specific contexts or circumstances. Conclusions generated from study results should enhance the conceptual framework, or contribute to a new theory or model development, and are most often situated within the discussion and conclusion sections of a manuscript.

The discussion section should include interpretation of the results and recommendations for practice. Interpretations should go beyond first-level results or literal description of observed behaviors, patterns, and themes from analysis. The author’s challenge is to provide a complete and thorough examination and explanation of how specific results relate to each other, contribute to answering the research question, and achieve the primary purpose of the research endeavor. The discussion should “close the loop” by integrating study results and analysis with the original conceptual framework. The discussion section should also provide a parsimonious narrative or graphical explanation and interpretation of study results that enhances understanding of the targeted phenomena.

The conclusion section should provide an overall picture or synopsis of the study, including its important and unique contributions to the field from the perspective of both conceptual and practical significance. The conclusion should also include personal and theoretical perspectives and future directions for research. Together, the discussion and conclusion should include responses to the larger questions of the study’s contributions, such as: So what? Why do these results matter? What next?

The strength of conclusions is dependent upon the extent to which standards of rigor and best practices were demonstrated in design, data collection, data analysis, and interpretation, as described in previous sections of this article. 4,12,17,23,24 Quality and rigor expectations for drawing valid conclusions and generating new theories are reflected in the following essential features of rigor and quality, which include: “Close the loop” to clearly link research questions, study design, data collection and analysis, and interpretation of results. Reflect effective integration of the study results with the conceptual framework and explain results in ways that relate, support, elaborate, and/or challenge conclusions of prior scholarship. Descriptions of new or enhanced frameworks or models are clear and effectively grounded in the study results and conclusions. Practical or theoretical implications are effectively discussed, including guidance for future studies. Limitations and issues of reflexivity and ethics are clearly and explicitly described, including references to actions taken to address these areas. 3,4,12,14

Step 5: Reporting Research Results

Key to quality reporting of qualitative research results are clarity, organization, completeness, accuracy, and conciseness in communicating the results to the reader of the research manuscript. O’Brien and others 4 proposed a standardized framework specifically for reporting qualitative studies known as the Standards for Reporting Qualitative Research (SRQR, Table 2 ). This framework provides detailed explanations of what should be reported in each of 21 sections of a qualitative research manuscript. While the SRQR does not explicitly mention a conceptual framework, the descriptions and table footnote clarification for the introduction and problem statement reflect the essential elements and focus of a conceptual framework. Ultimately, readers of published work determine levels of credibility, trustworthiness, and the like. A manuscript reviewer, the first reader of a study report, has the responsibility and privilege of providing critique and guidance to authors regarding achievement of quality criteria, execution and reporting of standards of rigor, and the extent to which meaningful contributions to thinking and practice in the field are presented. 13,39

An Adaptation of the 21 Elements of O’Brien and Colleagues’ Standards for Reporting Qualitative Research (SRQR) 4

Authors must avoid language heavy with connotations or adjectives that insert the researcher’s opinion into the database or manuscript. 14,40 The researcher should be as neutral and objective as possible in interpreting data and in presenting results. Thick and rich descriptions, where robust descriptive language is used to provide sufficient contextual information, enable the reader to determine credibility, transferability, dependability, and confirmability .

The process of demonstrating the credibility of research is rooted in honest and transparent reporting of how biases and other possible confounders were identified and addressed throughout study processes. Such reporting, first described within the study’s conceptual framework, should be revisited in reporting the work. Confounders may include the researcher’s training and previous experiences, personal connections to the background theory, access to the study population, and funding sources. These elements and processes are best represented in Glassick’s criteria for effective presentation and reflective critique ( Table 1 , criteria 5 and 6). Transferability is communicated, in part, through description of sampling factors such as: geographical location of the study, number and characteristics of participants, and the timeframe of data collection and analysis. 40 Such descriptions also contribute to the credibility of the results and readers’ determination of transfer to their and other contexts. To ensure dependability, the research method must be reported in detail such that the reader can determine proper research practices have been followed and that future researchers can repeat the study. 40 The confirmability of the results is influenced by reducing or at a minimum explaining any researcher influence on the result by applying and meeting standards of rigor such as member checking, triangulation, and peer review. 29,33

In qualitative studies, the researcher is often the primary instrument for data collection. Any researcher biases not adequately addressed or errors in judgement can affect the quality of data and subsequent research results. 33 Thus, due to the creative interpretative and contextually bound nature of qualitative studies, the application of standards of rigor and adherence to systematic processes well-documented in an audit trail are essential. The application of rigor and quality criteria extend beyond the researcher and are also important to effective peer review processes within a study and for scholarly dissemination. The goal of rigor in qualitative research can be described as ensuring that the research design, method, and conclusions are explicit, public, replicable, open to critique, and free of bias. 41 Rigor in the research process and results are achieved when each element of study methodology is systematic and transparent through complete, methodical, and accurate reporting. 33 Beginning the study with a well-developed conceptual framework and active use of both researcher reflexivity and rigorous peer review during study implementation can drive both study rigor and quality.

As the number of published qualitative studies in health professions educational research increases, it is important for our community of health care educators to keep in mind the unique aspects of rigor in qualitative studies presented here. Qualitative researchers should select and apply any of the above referenced study methods and research practices, as appropriate to the research question, to achieve rigor and quality. As in any research paradigm, the goal of quality and rigor in qualitative research is to minimize the risk of bias and maximize the accuracy and credibility of research results. Rigor is best achieved through thoughtful and deliberate planning, diligent and ongoing application of researcher reflexivity, and honest communication between the researcher and the audience regarding the study and its results.