qualitative research methodology example pdf

An Introduction to Qualitative Research

Cite this chapter.

• Robert A. Croker 3  

6781 Accesses

25 Citations

Qualitative research — when you first heard the term, your initial thought might have been, ‘What do qualitative researchers actually do?’ It may come as a surprise to you that you are already familiar with many of their activities, and you actually do them yourself — every day — as you watch and listen to what happens around you, and ask questions about what you have seen and heard.

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

Access this chapter

Subscribe and save.

• Get 10 units per month
• Download Article/Chapter or eBook
• 1 Unit = 1 Article or 1 Chapter
• Cancel anytime
• Available as PDF
• Read on any device
• Instant download
• Own it forever
• Compact, lightweight edition
• Dispatched in 3 to 5 business days
• Free shipping worldwide - see info
• Durable hardcover edition

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Unable to display preview.  Download preview PDF.

Similar content being viewed by others

What is “Qualitative” in Qualitative Research? Why the Answer Does not Matter but the Question is Important

Research Design and Methodology

What is qualitative in qualitative research.

Alvsesson, M., & Skoldberg, K. (2000). Reflexive methodology: New vistas for qualitative research . London: Sage Publications.

Google Scholar  

Bruner, J. (1990). Acts of meaning . Cambridge, MA: Harvard University Press.

Corbin, J., & Strauss, A. (2008). Basics of qualitative research (3rd ed.). Thousand Oaks, CA: Sage Publications.

Creswell, J. W. (2007). Qualitative inquiry and research design: Choosing among five traditions (2nd ed.). Thousand Oaks, CA: Sage Publications.

Creswell, J. W. (2009). Research design: Qualitative, quantitative, and mixed methods approaches (3rd ed.). Thousand Oaks, CA: Sage Publications.

Denzin, N. K., & Lincoln, Y. S. (2005). Introduction: The discipline and practice of qualitative research. In N. K. Denzin & Y. S. Lincoln (Eds.), The SAGE handbook of qualitative research (3rd ed.). Thousand Oaks, CA: Sage Publications.

Dey, I. (1993). Qualitative data analysis: A user-friendly guide for social scientists . London: Routledge.

Book   Google Scholar  

Glaser, B. (1992). Basics of grounded theory analysis . Mill Valley, CA: Sociology Press.

Glaser, B., & Strauss, A. (1967). The discovery of grounded theory . Chicago: Aldine.

Jones, D. M. (2002). Setting the stage: A look at the concepts of insider and outsider . Unpublished manuscript, University of Massachusetts, Amherst.

Lazaraton, A. (2003). Evaluative criteria for qualitative research in applied linguistics: Whose criteria and whose research? The Modern Language Journal , 1–12.

Marshall, C., & Rossman, G. B. (2006). Designing qualitative research (4th ed.). Thousand Oaks, CA: Sage Publications.

Merriam, S. B. (2002). Introduction to qualitative research. In S. B. Merriam & Associates (Eds.), Qualitative research in practice: Examples for discussion and analysis . San Francisco, CA: Jossey-Bass.

Merriam, S. B., & Associates (2002). Qualitative research in practice: Examples for discussion and analysis. San Francisco, CA: Jossey-Bass.

Patton, M. Q. (2002). Qualitative research and evaluation methods (3rd ed.). Thousand Oaks, CA: Sage Publications.

Peshkin, A. (1988). In search of subjectivity — one’s own. Educational Researcher , 17(7), 17–22.

Richards, K. (2003). Qualitative inquiry in TESOL . Basingstoke: Palgrave Macmillan.

Richards, L., & Morse, J. M. (2007). Readme first for a user’s guide to qualitative methods (2nd ed.). Thousand Oaks, CA: Sage Publications.

Richardson, L. (2000). Writing: A method of inquiry. In N. K. Denzin & Y. S. Lincoln (Eds.), Handbook of qualitative research (2nd ed.) (pp. 923–948). Thousand Oaks, CA: Sage Publications.

Rossman, G. B., & Rallis, S. F. (2003). Learning in the field: An introduction to qualitative research (2nd ed.). Thousand Oaks, CA: Sage Publications.

Schwandt, T. A. (2007). The Sage dictionary of qualitative inquiry (3rd ed.). Thousand Oaks, CA: Sage Publications.

Strauss, A., & Corbin, J. (1990). Basics of qualitative research . Newbury Park, CA: Sage Publications

Download references

Author information

Authors and affiliations.

Nanzan University, Nagoya, Japan

Robert A. Croker ( Associate Professor in the Faculty of Policy Studies )

You can also search for this author in PubMed   Google Scholar

Editor information

Editors and affiliations.

Sugiyama Jogakuen University, Japan

Juanita Heigham

Nanzan University, Japan

Robert A. Croker

Copyright information

© 2009 Robert A. Croker

About this chapter

Croker, R.A. (2009). An Introduction to Qualitative Research. In: Heigham, J., Croker, R.A. (eds) Qualitative Research in Applied Linguistics. Palgrave Macmillan, London. https://doi.org/10.1057/9780230239517_1

Download citation

DOI : https://doi.org/10.1057/9780230239517_1

Publisher Name : Palgrave Macmillan, London

Print ISBN : 978-0-230-21953-3

Online ISBN : 978-0-230-23951-7

eBook Packages : Palgrave Language & Linguistics Collection Education (R0)

Share this chapter

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

• Publish with us

Policies and ethics

• Find a journal
• Track your research

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.

Prevent plagiarism. Run a free check.

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 4, 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, "i thought ai proofreading was useless but..".

I've been using Scribbr for years now and I know it's a service that won't disappoint. It does a good job spotting mistakes”

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Open access
• Published: 02 September 2024

A qualitative dynamic analysis of the relationship between tourism and human development

• Pablo Juan Cárdenas-García   ORCID: orcid.org/0000-0002-1779-392X 1 ,
• Juan Gabriel Brida   ORCID: orcid.org/0000-0002-2319-5790 2 &
• Verónica Segarra   ORCID: orcid.org/0000-0003-0436-3303 2  

Humanities and Social Sciences Communications volume  11 , Article number:  1125 ( 2024 ) Cite this article

Metrics details

• Development studies

This study analyzes the dynamic relationship between tourism and human development in a sample of 123 countries between 1995–2019 using a symbolic time series methodological analysis, with the number of international tourist arrivals per capita as the tourism measurement variable and the Human Development Index as the development measurement variable. The objective was to determine if a higher level of tourism specialization is related to a higher level of economic development. The definition of economic regime is used and the concept of the distance between the dynamic trajectories of the different countries analyzed is introduced to create a minimum spanning tree. In this way, groups of countries are identified that display similar behavior in terms of tourism specialization and levels of human development. The results suggest that countries with a high level of tourism specialization have a higher level of development as compared to those in which tourism has a lower specific weight. However, the largest group of countries identified is characterized by low levels of tourism specialization and economic development, which appears to translate into a poverty trap. Therefore, policies related to tourism activity expansion should be created since higher tourism levels have been linked to higher levels of human development. In the case of less developed countries, however, these projects should be financed by international organizations so that these countries can escape the poverty trap in which they are currently found.

Similar content being viewed by others

Modeling the link between tourism and economic development: evidence from homogeneous panels of countries

The coordination pattern of tourism efficiency and development level in Guangdong Province under high-quality development

Analysis of spatial-temporal pattern, dynamic evolution and influencing factors of health tourism development in China

Introduction.

Traditionally, the Gross Domestic Product per capita (GDP per capita) is considered the go-to variable to determine a population’s economic development and is restricted exclusively to an economic measure (Todaro and Smith, 2020 ). Recently, however, studies on development have begun incorporating other noneconomic factors, such as education and health. These factors, together with the economic criteria, provide a baseline for measuring a population’s development in broader terms (World Bank, 1991 ; Lee, 2017 ). In the search for economic activities that enable economic growth and improve the level of economic development, many countries have been especially interested in tourist activity since it is an economic activity that has a strong potential for job creation, the generation of foreign currency, and revenue increase. In short, it may be able to boost economic growth in host regions (Brida et al., 2020 ). In some cases, the development of tourism has been found to contribute to reducing inequality (Chi, 2020 ; Nguyen et al. ( 2021 )) or reducing poverty (Garza-Rodriguez ( 2019 ); Folarin, Adeniyi ( 2019 )).

In fact, what is actually important in economic policies is not only the promotion of a country’s economic growth but also, the channeling of this economic growth into improved economic development in the territory (Croes, 2012 ). This latter concept is much broader and it serves to satisfy the needs and demands of the resident population, improving its quality of life (Ranis et al., 2000 ).

In terms of the analysis of the relationship between tourism and economic growth, many studies have researched this connection. Most of them agree that a causal relationship exists between both variables, that tourism influences growth (Balaguer and Cantavella-Jordá, 2002 ; Brida et al., 2016 ), that the economic cycle influences the development of tourism (Antonakakis et al., ( 2015 ); Sokhanvar et al., 2018 ), and that there is a bidirectional relationship between tourism and economic growth (Bojanic and Lo, 2016 ; Hussain-Shahzad et al. ( 2017 )).

Given that a relationship between tourism and economic growth has been proven in the economies of host countries and national governments, despite a lack of sufficient empirical evidence, various international organizations have been promoting tourism activity as a tool to facilitate the population’s development in those host regions that attract tourist flows to their territory (OECD, 2010 ; UNCTAD - United Nations Conference on Trade and Development ( 2011 )). Such has been the case with the relationship between tourism and economic growth, with the suggestion that tourism is a tool for economic development (Cárdenas-García and Pulido-Fernández, 2019 ).

Many studies have already analyzed the relationship between tourism and GDP per capita, finding long-term equilibrium relationships between the expansion of tourism and economic growth, whereby a higher level of tourists received means higher levels of economic growth (Akadiri et al., 2017 ). As previously mentioned, the economic development of a population, in a broad sense, and in addition to the economic variables, has to be linked to additional variables with a multidimensional content (Wahyuningsih et al., 2020 ). In this scenario, although some studies have measured development in a broader sense (Andergassen and Candela, 2013 ; Banerjee et al. 2018 ; Bojanic and Lo, 2016 ; Li et al., 2018 ), there is a clear lack of analysis of the relationship between tourism and economic development as a multidimensional variable.

In this regard, human development, and its measurement through the Human Development Index (HDI), is a multidimensional variable related to the living conditions of the resident population (income, education, and health), which has been used on many occasions (more than level of poverty or income inequality) to measure a country’s level of development (Cárdenas-García et al., 2015 ; Chattopadhyay et al., 2021 ; Croes et al., 2021 ). The link between tourism and human development arises from the economic growth generated by the expansion of tourist activity. This economic growth is used to develop policies that will improve the education and health levels of the host population (Alcalá-Ordóñez and Segarra, 2023 ).

This article analyzes the relationship between tourism and economic growth, measuring the economic growth of the countries in the broadest possible sense, with a link to the concept of human development (Cárdenas-García et al., 2015 ). As a novelty, a wide set of countries is used for this analysis. This overcomes the limitations of prior works that analyzed the relationship between tourism and human development using small country samples (Chattopadhyay et al., 2021 ).

Although distinct works have already analyzed the relationship between tourism and economic development, they tended to focus on the application of econometric tests to determine the type of causal relationship existing between these variables (Alcalá-Ordóñez and Segarra, 2023 ). This work takes a distinct approach, analyzing the qualitative dynamic behavior arising between tourism and human development. Different country groups are identified that have similar behavior within the group and, simultaneously, with differences as compared to the other groups. Thus it is possible to verify the relationship existing between tourism and human development in each of these country groups, to determine if a higher level of tourism specialization is linked to a higher level of human development.

This approach does not attempt to determine if a causal relationship exists by which tourism precedes the level of development. Rather, this approach of grouping countries aims to determine if, at similar levels of development, the country groups with a higher level of tourism specialization display higher levels of human development. This would suggest that tourism activity is an economic activity that promotes human development to a greater extent than other economic activities.

In this context, this study analyzes the dynamic relationship between tourism and economic development, considering development as a multidimensional variable. It uses a data panel consisting of 123 countries for the period between 1995–2019 and considers the diversity of countries in terms of tourism development and their economic development dynamics. To perform this dynamic analysis, the concept of economic regime is introduced (Brida, 2008 ; Cristelli et al., 2015 , Brida et al., 2020 ), and symbolic time series are used (Risso ( 2018 )).

This article contributes to the empirical literature examining the relationship between tourism and economic development. It analyzes the qualitative dynamic behavior of the countries without considering any particular model. Therefore, this analysis enables the identification of groups of countries with similar dynamics, for which economic models of the same type can be identified. The results of this study indicate that there are different groups of countries displaying similar dynamic behavior in terms of both tourism and development. These groups are characterized by their level of tourism specialization and economic development. Therefore, it is interesting to note the heterogeneity existing in the relationship between tourism and development, as well as the consequences that this situation has for both the empirical analysis and the political implications.

The rest of the document is organized as follows: the following section reviews the literature on the subject under study, section “Data” presents the data used, section “Methodology” details the methodology applied, section “Results” presents the results obtained, section “Discussion” includes a discussion of the paper, and, finally, section “Conclusions and policy implications” outlines the final conclusions and policy implications of the work.

Literature review

Economic growth versus economic development.

Traditionally, studies on development have focused on economic growth and have been based on the premise that the efficient allocation of resources maximizes growth and that the expansion of growth and consumption is a measure of population welfare (Easterly, 2002 ). However, the emergence of new studies at the end of the last century, beginning with the works by Sen ( 1990 , 1999 ), resulted in a change of focus for studies on development. They moved from an exclusive view of development linked to economic growth to the inclusion of new factors that connect it to the population’s living conditions (Croes et al., 2018 ).

Economic growth and development are distinct concepts that do not need to be linked. In other words, increased economic growth does not necessarily imply improved economic development (Croes et al., 2021 ). However, it is also true that economic growth, and the revenue generated, can be used to improve a population’s living conditions through better health care, infrastructures, and education (Banerjee et al., 2018 ; Cárdenas-García and Pulido-Fernández, 2019 ).

In this regard, the first studies to analyze the relationship between tourist activity and the economies of host countries focused exclusively on the relationship between tourism and economic growth, using a traditional view of development that is linked to economic variables.

Tourism and economic growth

Numerous studies have analyzed the relationship between tourism and economic growth. Therefore, it is a highly relevant research area in the economic analysis of tourist activity, with three streams of perfectly defined results in which these works may be grouped (Alcalá-Ordóñez et al., 2023; Brida et al., 2016 ).

Firstly, different studies have determined that tourism development drives economic growth, identified under the tourism-led economic growth hypothesis. Both the first study to analyze this causal relationship (Balaguer and Cantavella-Jorda, 2002), as well as the later studies (Brida et al., 2016 ; Castro-Nuño et al., 2013 ; Lin et al., 2019 , Pérez-Rodríguez et al., 2021 ; Ridderstaat et al., 2016 ), have confirmed the existence of this relationship.

Secondly, other studies determined that the evolution of the economic cycle has an influence on the development of tourism, identified under the economic-driven tourism growth. These studies indicate that those economies with a greater level of investment, stability in the price level, or lower level of unemployment determine the development of tourism (Antonakakis et al. ( 2015 ); Rivera, 2017 ; Sokhanvar et al., 2018 ; Tang, Tan ( 2018 )).

Finally, a third wave of studies determined that the relationship between the development of tourism and economic growth has a bidirectional character. These studies note that the relationship between both variables is a causal bidirectional relationship (Antonakakis et al., 2019 ; Bojanic and Lo, 2016 ; Chingarande and Saayman, 2018 ; Hussain-Shahzad et al. ( 2017 ); Ridderstaat et al., 2013 ).

Human Development as a measure of development

Since the end of the last century, the scientific literature has shown that the concept of development cannot be linked exclusively to variables of economic content. Instead, development should be considered along with other non-economic factors that are related to the population’s living conditions. Therefore, it is a multidimensional concept (Alcalá-Ordóñez and Segarra, 2023 ).

When measuring development using a multidimensional perspective, this concept is often linked to human development (Cárdenas et al., 2015 ; Chattopadhyay et al., 2021 ). In this regard, the HDI is a multidimensional indicator that, in addition to considering variables of economic content, in this case per capita income, also incorporates other non-economic factors, specifically, life expectancy and educational level of the population (United Nations Development Program, 2022 ).

The HDI offers some major advantages as a measure of development over other indicators, providing a more complete vision of society’s progress and focusing not only on economic factors but also on factors related to the population’s living conditions. This makes it possible to identify inequalities that need to be addressed to promote more equitable and sustainable development (Sharma et al., 2020 ; Tan et al., 2019 ). Moreover, since it was created by the United Nations Development Program for a large group of countries, it permits homogenous comparison-making between a broad base of countries at a global level (Cárdenas-García and Pulido-Fernández, 2019 ).

Tourism and human development

The expansion of tourism activity can influence the level of human development (Croes et al., 2021 ). The common link between these two variables is the economic impact generated by the expansion of tourist activity since this is a linked process, whereby a higher level of tourists results in an increase in income generated and thus, a higher level of economic growth (Brida et al., 2016 ). Countries can take advantage of this higher level of economic growth to develop specific policies aimed at improving the living conditions of the host population, thereby improving human development (Eluwole et al., 2022 ).

This link between tourism and human development has also been highlighted by the United Nations Tourism in its Millennium Development Goals of 2000, which declared that factors such as health and education are very important in economic development. It was suggested that tourism may improve human development given that it has an influence on these non-economic factors (UN Tourism, 2006 ).

The triple component of the HDI, the most frequently used indicator to measure economic development, has been considered in most of the studies analyzing the relationship between tourism and economic development (Alcalá-Ordóñez and Segarra, 2023 ).

Distinct studies have attempted to determine whether tourism is a tool for economic growth in host countries, although most of the studies have exclusively used economic content to measure the concept of development (Wahyuningsih et al., 2020 ). Therefore, there is a major lack of empirical studies that consider whether tourism influences development and that do so while considering development to be a multidimensional variable encompassing other factors (beyond those associated with the economy).

Some of these studies have outlined that the expansion of tourism has led to an increase in the level of development for host countries. This suggests that tourism has a positive unidirectional relationship with the living conditions of the population (Meyer and Meyer, 2016 ). Fahimi et al. ( 2018 ), examining microstates, found evidence supporting the idea that the expansion of tourism leads to an improvement in human capital. Other studies have also noted that this causal relationship between tourism and development exists, but only in developed countries (Banerjee et al., 2018 ; Bojanic and Lo, 2016 ). Some studies have suggested that only the least developed countries have benefited from the tourism industry in terms of increased economic development ratios (Cárdenas-García et al., 2015 ).

However, although it has been indicated that tourism influences economic growth, some authors have noted that tourism does not have an influence on the development of host countries (Rivera, 2017 ), or simply, that the expansion of this activity does not have any effect on human development (Croes et al., 2021 ).

As an intermediate position between these two schools of thought, some works have suggested that tourism has a positive influence on the development of the resident population, but this causal relationship is only found when certain factors exist in the host countries, such as infrastructure, environment, technology, and human capital (Andergassen and Candela, 2013 ; Cárdenas-García and Pulido-Fernández, 2019 ; Li et al., 2018 ).

Along these same lines, in a study using panel data from 133 countries, Chattopadhyay et al. ( 2021 ) determined that, although no global relationship exists between tourism and human development for all countries, the specific characteristics of each country (level of growth, degree of urbanization, or commercial openness) are determinants for tourism to improve human development levels.

Finally, other studies in the scientific literature have looked to determine whether the relationship between tourism and development is a bidirectional causal relationship, with papers affirming the existence of this relationship between tourism and development (Pulido-Fernández and Cárdenas-García, 2021 ).

Therefore, when examining the few studies that have analyzed the relationship between tourism and development, it may be concluded that contradictory and biased results exist. This may be due to the characteristics of the samples chosen, the variables used, and the methodology employed. Currently, there is no defined school of thought in the scientific literature with regard to the ability of tourism to improve living conditions for the resident population. This contrasts with the conclusions drawn regarding the relationship between tourism and economic growth.

This gap in the scientific literature provides an opportunity for new empirical studies that can analyze the relationship between tourism and development.

In this study, data from different sources of information were used with the objective of analyzing the relationship between tourism and economic development, in accordance with the methodology proposed in the following section. The data used in the present study are available for a total of 123 countries, covering all geographical areas worldwide. The specific data for these countries are as follows, including a web link to the availability of the data to provide greater transparency:

Tourist activity. The number of international tourists received was used as a variable for measuring tourist activity. For those countries for which this data was unavailable, the number of international visitors received was used, based on annual information provided by the United Nations Tourism between 1995 and the present (UN Tourism, 2022 ).

Data on international tourists received at a country level are available at https://www.unwto.org/tourism-data/global-and-regional-tourism-performance

Economic development. The HDI, developed by the United Nations Development Program and available annually from 1990 to the present day, was used as a variable for measuring economic development (United Nations Development Program, 2022 ).

Data from the HDI for each country are available at https://hdr.undp.org/data-center/human-development-index#/indicies/HDI

Total population. The de facto population was used as a measurement variable and counts all residents regardless of their legal status or citizenship. This information was provided by the World Bank and is available from 1960 to the present day, on an annual basis (World Bank, 2022 ).

Data on the population of the distinct countries are available and accessible at https://data.worldbank.org/indicator/sp.pop.totl .

Based on the data indicated above, the initial variables are transformed, specifically, in the case of tourism, through the use of the relativized per capita variable. A descriptive summary of the variables used in the analysis is presented in Table 1 . Finally, two variables have been used to analyze the relationship between tourism and economic development:

International tourists per inhabitant received in the country (number of international tourists / total population of the country), as a measure of tourism specialization. The unit of this variable is established at a relative value, by dividing the number of tourists by the population.

HDI of the country, as a measure of economic development. The unit of this variable is established at a relative value for each country, which, in all cases, is between 0 (lowest level of human development) and 1 (highest level of human development).

Regarding the tourist sector, the measurement of tourism is a subject that has generated great interest, and, on many occasions, the selection of different indicators leads to different results (Song and Wu, 2021 ). As a result, the results of the empirical analysis may be affected by the indicators used to represent the tourist demand (Fonseca and Sanchez-Rivero, 2020 ), with there being important differences between studies with respect to the tourism indicator. According to Rosselló-Nadal, He ( 2020 ), tourist arrivals or tourism expenditure are frequently used to measure tourist demand; however, when looking at the literature, differences in the results are found depending on the indicator considered. Indeed, in their study, which looked at 191 countries between 1998–2016, the authors found evidence that estimates may differ depending on the indicator used for the tourism demand of a destination (international tourist arrivals, or international tourist expenditure in this case). Other studies use indicators that do not measure the degree of tourist activity of a destination, as is the case for the number of tourist arrivals, the expenses, or the revenues. Instead, they consider an indicator that measures the degree of specialization that an economy has in tourism, for example, international tourist arrivals in per capita terms or expenditure or income as a percentage of GDP or exports. This work uses the number of international tourist arrivals, in relation to the population, and thus obtains the degree of tourism specialization of a destination (such as Dritsakis, 2012 ; Tang and Abosedra, 2016 ).

With regard to the measurement of economic development, the arrival of the HDI has resulted in a notable improvement in terms of GDP per capita, which is traditionally used to measure the progress of a country linked only to economic aspects (Lind, 2019 ). In fact, the HDI includes other noneconomic factors as it measures three key dimensions of development: a long and healthy life, being well-informed, and having a decent standard of living. This is why this index was created from the geometric mean of the normalized indices for each of the three dimensions indicated: (i) health: life expectancy at birth; (ii) education: years of schooling for adults and expected years of schooling for children; and (iii) standard of living: Gross National Income per capita (United Nations Development Program, 2022 ). Therefore, since the emergence of this index, there have been increasingly more studies that have incorporated HDI as a measurement of economic development. This variable has been shown to represent development better than other variables that are based exclusively on economic factors (Anand and Sen, 2000 ; Jalil and Kamaruddin, 2018 ; Ngoo and Tey, 2019 ; Ogwang and Abdou, 2003 ; Sajith and Malathi, 2020 ).

The time scale considered in this study covers the period between 1995–2019, in order to perform the broadest possible time analysis. On the one hand, there is an initial time restriction in terms of the data, given that the first data available on international tourist arrivals, provided by the United Nations Tourism, refer to the 1995 fiscal year. On the other hand, the data for the 2019 fiscal year are the latest in the time series analyzed. Therefore, the consequences of the COVID-19 crisis, which may have had a different impact at the country level, as well as the level of recovery in international tourist arrivals, do not affect the results of this work.

Methodology

In this work, an analysis is carried out involving the dynamics of two variables: tourism specialization and the HDI. Each of the countries considered in the analysis is represented by a two-dimensional time series of coordinates of these two variables.

In order to compare these dynamics and thereby find homogenous country groups sharing similar dynamics, it was first necessary to introduce a metric permitting this comparison. A fundamental issue in this analysis is that the units of measurement used for each variable are different and the relationship between them is unknown since tourism is measured in the number of tourists per inhabitant while the HDI is an index that varies between 0 and 1. Therefore, the frequently used Euclidean metrics are not valid for this analysis. For this reason, in this study, the problem was analyzed within the framework of complex systems by introducing the concept of “regimes”.

In economic literature, the term “regime” is used to characterize a type of behavior exhibited by one economy, which can be qualitatively distinguished from the “regime” that characterizes another economy. In this way, one regime is distinguished and differentiated from another, so that the economy as a whole may be considered a system of multiple regimes. Intuitively, an “economic regime” may be considered a set of rules governing the economy as a system and determining certain qualitative behaviors (Boehm and Punzo, 2001 ).

Regime changes, on the other hand, are associated with qualitative changes in the dynamics of an economy. Identifying and characterizing these regimes is a complex issue. For example, when working with mathematical models, a commonly used criterion is through Markov partitions (see Adler, 1998 ). Another widely used criterion when working with data is the division of the state space using various statistical indicators, such as the mean, median, etc. (see Brida and Punzo, 2003 ).

Firstly, a distance between countries was calculated to compare their trajectories; secondly, a symbolic time series analysis was used and the concept of “regime” was incorporated; as a result, the original two-dimensional series was transformed into a one-dimensional symbolic series. Then, a metric allowing for the comparison of the dynamic trajectories of the different countries was introduced; finally, a cluster analysis was performed to group the countries based on their dynamics.

The symbolic time series analysis methodology, still quite undeveloped in the field of economics, has been used in some previous works, such as that by Brida et al. ( 2020 ) that analyzes the relationship between tourism and economic growth. All analyses have been performed using RStudio software.

Time series symbolization

To identify the qualitatively relevant characteristics, the concepts of regime and regime dynamics were introduced (Brida, 2008 ; Brida et al., 2020 ). Each regime had its own economic performance model that made it qualitatively different from the rest. The partitioning of the space of tourism states and the development was established by means of annual averages of international arrivals per capita (x) and the HDI (y). The space was divided into four regions, which were determined by the annual averages of tourism and economic development, \({\bar{x}}_{t}\) and \({\bar{y}}_{t}\) respectively, with \(t=1,\ldots ,25\) . Using this partitioning of the states space into regimes, two types of dynamics are distinguished: one within each of the regimes and one of change between regimes. While the dynamic observed in each regime determines a performance model that differs from the models that act in the others, the dynamics of change from one region to another indicate where an economy is at each temporal moment. This dynamic describes performance in terms of tourism specialization and economic development in a qualitative way.

A change of regime of course signals some qualitative transformation. To explore these qualitative changes for every country, let us substitute a bi-dimensional time series \(\left\{\left({x}_{1},{y}_{1}\right),\,\left({x}_{2},{y}_{2}\right),\,\ldots ,\,\left({x}_{{\rm{T}}},{y}_{{\rm{T}}}\right)\right\}\) , by a sequence of symbols: \(s=\left\{{s}_{1},{s}_{2},\ldots ,{s}_{T}\right\}\) , such that \({s}_{t}=j\) if and only if \(\left({x}_{t},{y}_{t}\right)\) belongs to a selected state space region, \(\,{R}_{j}\) . It is defined four regions in the following way:

Regime 1: countries with above-average HDI and tourism specialization. In this regime, the most developed economies specializing in tourism are expected to be found. The majority of European countries are expected to be found in this regime; countries in other regions with a high level of tourism specialization could also be included.

Regime 2: countries with high HDI and low tourism specialization. In this regime, the most developed economies, but in which tourism activity has a less important weight in their economic base, are expected to be found. Some large countries such as the US and Germany are expected to be found in this regime. Other countries may also be found here even if they do not present similar levels of development as European countries, for example, they have higher levels in relative terms (above the sample average).

Regime 3: countries with low HDI and low tourism specialization. In this regime, economies with a lower level of development and where tourism activity is not relevant to their economic activity, are expected to be found. Countries such as China, other Asian countries, countries on the African continent, and countries in South America are expected to be included in this regime.

Regime 4: countries with low HDI and high tourism specialization. Countries with a lower level of development and a high level of tourism specialization, such as Caribbean countries and some island countries, are expected to be found in this regime.

Once the one-dimensional symbolic series is obtained, a metric is introduced that allows comparing the dynamics of the countries, and which in turn allows for obtaining homogeneous groups. Given the symbolic sequences \({\left\{{s}_{{it}}\right\}}_{t=1}^{t=T}\) and \({\{{s}_{{jt}}\}}_{t=1}^{t=T}\) the distance between two countries, i and j is given by.

Intuitively, the distance between two countries measures the number of years of regime non-coincidence during the period. If the distance between two countries is zero, the countries have been in the same regime for the entire period. On the contrary, if the distance between two countries is T, the countries have not coincided for any time during the analyzed period. If the distance between two countries is α, it means that they have not coincided for α years during the period. In other words, they have coincided for T-α years.

Using the defined distance, the hierarchical tree was created using the nearest neighbor cluster analysis method (Mantegna, 1999 ; Mantegna and Stanley, 2000 ). Using the algorithm by Kruskal ( 1956 ), the minimum spanning tree (MST) was created. This tree was created progressively, joining all the countries from the sample using a minimum distance. According to this algorithm, in the first step, the two countries whose series had the shortest distances were connected. In the second step, the countries with the second shortest distance were connected. This pattern continued until all countries were connected in one tree.

Symbolic time series analysis

Figure 1 shows the point cloud corresponding to 2019, with the respective averages of each variable. Each point represents a country in this year with its coordinates (Tourism, HDI). As is expected, the points are distributed in the four regions, showing that qualitatively the countries perform differently. A clustering in the second and third quadrants can be observed, indicating a clustering in the sections with a low level of tourism specialization, and, in turn, there are not many countries in the fourth quadrant. In other words, few countries have been considered to have a high level of tourism specialization but low levels of development, in the last year (Belize, Fiji, Jamaica, Saint Lucia, the Maldives, and Samoa).

Cloud of points of the 123 countries for the year 2019.

Table 2 shows the percentage of time spent by each of the 123 countries analyzed in each of the previously defined regimes, showing that the large majority of the countries (80 countries) remained in the same regime for the entire period or, at least, for three-quarters of the period analyzed in the same regime (16 countries). In this regard, using the symbolization of the series, 4 clear groups were identified, made up of countries that remained in the same regime for the entire period:

Group 1: made up of countries that are in regime 1 for the entire period (high level of tourism specialization and high level of development): Austria, Bahamas, Barbados, Switzerland, Cyprus, Spain, France, Greece, Hong Kong, Ireland, Iceland, Italy, Luxembourg, Malta, Netherlands, Norway, Portugal, and Singapore.

Group 2: made up of countries that are in regime 2 for the entire period (low level of tourism specialization and high level of development): Germany, Argentina, Australia, Chile, South Korea, Costa Rica, Cuba, the United States, Russia, Iran, Japan, Kazakhstan, Kuwait, Mexico, Panama, United Kingdom, Romania, Trinidad and Tobago, and Ukraine.

Group 3: made up of countries that are in regime 3 for the entire period (low level of tourism specialization and low level of development): Azerbaijan, Benin, Bangladesh, Bolivia, Central African Republic, China, Congo, Algeria, Egypt, Gambia, Guatemala, Guyana, Honduras, Haiti, Indonesia, India, Cambodia, Laos, Lesotho, Morocco, Mali, Myanmar, Mongolia, Malawi, Namibia, Niger, Nicaragua, Nepal, Philippines, Papua New Guinea, Paraguay, Sudan, Sierra Leone, El Salvador, Togo, Tuvalu, Tanzania, Uganda, Vietnam, Zambia, and Zimbabwe.

Finally, Group 4, made up of Belize and the Maldives, which are in regime 4 for the entire period (high level of tourism specialization and low level of development):

It is worth noting that according to the results obtained, regime changes can be difficult to observe. This could be a result of the fact that a regime change implies a structural change in the economy and in such a period as the one analyzed in this study (25 years), the observation of a structural change may be circumstantial in nature. In other words, the timing of structural changes seems to be slower than the tick of the chosen clock; in this case, an annual tick.

Within the group of countries that always remain in regime 1, two groups of countries can be identified. One of the groups is that in which tourism is an essential sector for the economy (like in the case of the Bahamas or Barbados, which have tourism contribution rates to GDP of above 25%), and in which tourism seems to have an influence in the high level of development. The other group is that in which, while tourism is not necessarily an essential sector for the economy, due to the existence of other economic activities, it is an important sector for development (such as Spain or Portugal, with tourism contribution rates to GDP of above 10%).

Within the group of countries that always remain in regime 2, there are fundamentally countries in which tourism has a marginal weight in relation to the level of population (like in the case of Germany, the US, and Japan), due to the lack of or little exploitation of the country’s tourism resources, which would result in development seeming to be related to other economic activities.

Within the group of countries that always remain in regime 3, there is a large group consisting of 41 countries (a third of the sample) that seem to be in a poverty trap, due to the low level of development and low level of tourism specialization. This is in such a way that the low level of development hinders the expansion of tourism activity, and, in turn, this lack of tourism development makes it difficult to increase the levels of development.

Finally, within the group of countries that always remain in regime 4, there are only two countries found, which are characterized by a high level of tourism specialization but have not transformed this into an improvement in development, possibly due to the existence of certain factors that hinder this relationship.

Therefore, the first issue to note is the little mobility that countries have in terms of their classification between the different regimes, given that 80 countries (two-thirds of the sample) remained in the same regime during the 25 years analyzed, which seems to show that the variables are somewhat stable, and thus justifies the fact that no major changes were observed during the period analyzed. This behavior reveals that the homogeneity in the tourism and development dynamic is the rule and not the exception.

In fact, only 27 countries, out of the 123 countries analyzed, are in a different regime for at least a quarter of the period: Albania, Armenia, Bulgaria, Brazil, Botswana, Canada, Colombia, Slovakia, Eswatini, Finland, Fiji, Hungary, Jamaica, Jordan, Lithuania, Latvia, Moldova, Malaysia, New Zealand, Peru, Saint Lucia, Sweden, Thailand, Tonga, Tunisia, Turkey, and Samoa.

In this regard, Fig. 2 shows the time evolution of the symbolic series for some selected countries. As can be noted, there are some countries, like Brazil, that always have a low level of tourism specialization and alternate between periods of high and low economic development, with it seeming as though there is consolidation as being a low HDI country in recent years (until 2002, Brazil had an above average level of development but, after it was hit by a crisis, the country moved to the low development regime. Then, in 2013, it managed to return to the high HDI regime, albeit temporarily as in 2016, in the midst of a political and economic crisis, it returned to the low development regime, where it currently remains). This is similar to what happened in Fiji, insofar as it was almost always specialized in tourism and alternated HDI, consolidating itself in Regime 4 of the low HDI. As such, it seems as though certain countries define their behavior according to the degree of tourism specialization; in this case, not particularly specialized countries.

Top panel: Brazil (left) and Fiji (right). Bottom panel: Latvia (left) and Eswatini (right).

However, the behavior of Latvia or Eswatini seems to be determined by HDI and not by tourism specialization. As to be expected, Latvia remained always in regimes 1 and 2 with a high HDI while Eswatini remained in regimes 3 and 4 with a low HDI. In both cases, they alternated periods of high and low specialization in tourism.

Grouping homogeneous countries

In the case analyzed, there are many countries with zero distance. These are the countries that have the same symbolic representation, that is, the regimes dynamics are coincidental given that these countries always remain in the same regime. Therefore, there are three groups that start to form with countries that have zero distance (countries that are always placed in regimes 1, 2, and 3), and a small group, formed by Belize and the Maldives, which are the only countries that remained in regime 4 for the entire period analyzed. According to this algorithm, 6 groups were obtained, while some countries were not included in any of the groups as they were considered to be “outliers”.

Specifically, there was a graph with 123 nodes corresponding to each country and 122 links; however, given that there were several countries with the same dynamic (the distance between these countries is zero), each of these groups is represented in a single node; that is, the countries that always remained in regime 1 were considered together as one single node, with the same happening for the remaining three groups of countries with identical dynamics (groups 2, 3, and 4). Therefore, in this case, there is a node representing 18 countries from group A and another node (both pink) that represents multiple countries; the Czech Republic, Estonia, Croatia, Mauritius, and Slovenia, which all share the same dynamic (they always remain in regime 1, except in 1995). There is a node representing 19 countries from group B (light blue), another node representing 41 countries from group C (green), and a final node representing Belize and Maldives in group D. In this way, 80 countries are represented in four nodes. To complete the tree, 38 other nodes, each corresponding to a country, were established. Using Kruskal’s algorithm ( 1956 ), the MST is built, in which all nodes are connected in a single tree from the minimum distances. In this way, a tree is created having links that connect the nodes to represent the minimum distances between them (a longer arrow indicates a longer distance).

Figure 3 shows the MST. It is worth noting the central position that these multiple nodes have within the groups, that is, nodes that represent a group of countries with the same dynamics. The structure of the MST seems to be almost linear; moreover, while group C (green) is the most numerous, it is also the most compact of the large groups.

(Nodes: Pink group A/Light blue group B/Green group C/Yellow group D/Orange group E/Blue group F/Red Outliers. Distances according to arrow color: black 1/red 2/light blue 3/green 4/blue 5/orange 6/pink 7/gray 8/violet 9).

Figure 4 shows the geographic distribution of the different groups. There are 6 groups (3 large and 3 small), while some countries are not included in any of these groups, as they are considered to be “outliers”:

Group A: Albania, Austria, Belgium, Bulgaria, Bahamas, Barbados, Switzerland, Cyprus, Czech Republic, Denmark, Spain, Estonia, Finland, France, Greece, Hong Kong, Croatia, Hungary, Iceland, Ireland, Italy, Lithuania, Luxembourg, Latvia, Malta, Mauritius, Malaysia, Netherlands, Norway, New Zealand, Portugal, Qatar, Singapore, Slovakia, Slovenia, and Uruguay. This group is made up of countries that predominantly remained in regime 1, that is, in general, these are countries with a high tourism specialization and high economic development.

Group B: Argentina, Australia, Brazil, Chile, Colombia, Costa Rica, Cuba, Germany, Ecuador, United Kingdom, Iran, Israel, Jordan, Japan, Kazakhstan, South Korea, Kuwait, Sri Lanka, Mexico, North Macedonia, Panama, Peru, Poland, Romania, Russia, Tonga, Trinidad and Tobago, Ukraine, United States. This group is made up of countries that predominantly remained in regime 2, that is, in general, these are countries with a low tourism specialization and high economic development.

Group C: Azerbaijan, Benin, Bangladesh, Bolivia, Central African Republic, China, Congo, Dominican Republic, Algeria, Egypt, Gambia, Guatemala, Guyana, Honduras, Haiti, Indonesia, India, Cambodia, Laos, Lesotho, Mali, Morocco, Myanmar, Mongolia, Malawi, Namibia, Niger, Nicaragua, Nepal, Philippines, Papua New Guinea, Paraguay, Sudan, Sierra Leone, El Salvador, Togo, Tuvalu, Tanzania, Uganda, Vietnam, South Africa, Zambia, and Zimbabwe. This group is made up of countries that remained the majority of the time in regime 3, that is, in general, these are countries with a low tourism specialization and low economic development. With the exception of the Dominican Republic and South Africa (96% and 92%, respectively), all countries remained in regime 3 for the entire period.

Group D: Belize and the Maldives. This group is made up of the two countries that always remained in regime 4, that is, in general, these are countries with a high tourism specialization and low economic development.

Group E: Armenia, Moldova, Thailand, and Turkey. This group has the particular characteristic of having low tourism specialization throughout the period but alternating between a high level of development (regime 2) and a low level of development (regime 3).

Group F: Botswana, Jamaica, and Tunisia. This group is made up of countries that fundamentally remained in regime 4, that is, these are countries with a high tourism specialization and low economic development, however, unlike group D, they moved during the period analyzed through other regimes.

Outliers: Canada, Fiji, Saint Lucia, Sweden, Eswatini, and Samoa. These countries presented different dynamics and were not integrated into any of the previously-defined groups.

(Note: Pink: group A/Light blue: group B/Green: group C/Yellow: group D/Orange: group E/Blue: group F/Red: Outliers).

As can be seen, group A, which consists of countries with a high tourism specialization and high economic development, is basically made up of European countries, some Asian countries, and Uruguay (the only country in the Americas to be part of this group).

The countries in group B, that is, those countries with a good level of economic development, but a low specialization in the sector, are more geographically dispersed. This group consists of some European countries (in particular, Eastern European countries), a large part of Latin America and the Caribbean, as well as the US, Australia, and some Asian countries.

Group C, that is, those countries with a low tourism specialization and low economic development, consists of the vast majority of African countries, as well as a significant number of Asian countries, in addition to Bolivia and Paraguay in Latin America, as well as some countries in Central America.

The countries in Group D, that is, those countries that had a high tourism specialization but a low level of economic development throughout the period analyzed, as well as those in Group F, which were also in this regime for most of the period, do not have a uniform geographic pattern, since they are located on different continents.

Finally, the countries in Group E, that is, those countries with a low tourism specialization and alternating levels of economic development, are also geographically dispersed between Europe and Asia.

As can be seen in Table 3 both Group A and Group B are made up of countries with a high level of development; however, the countries in Group A, which also have a high level of tourism specialization, on average, have a significantly higher level of development than the countries in Group B, where the level of tourism specialization is low. These results appear to show that in terms of those countries specialized in tourism (Group A), the link with development is higher than for those countries that have achieved high levels of development due to the development of other economic activities.

Similar results can be found when comparing the data from Group C (countries with a low level of tourism specialization and low level of economic development) with the data from Groups D and F (countries with a high level of tourism specialization and low level of economic development). This is because, despite the level of development being low in all the countries, in the Group D and F countries, the level of development is significantly higher than in Group C countries. This appears to show that for those countries specialized in tourism (Groups D and F), the link with development is greater than for those countries that rely on other sectors as the basis of their economy.

Tourism’s relevance lies not only in its contribution to economic growth but also in the fact that the improved economic growth generated by the expansion of tourism activity may translate into improved living conditions for the host population. Due to this chained process, many countries have opted for this economic activity with the aim of improving income, education, and health. In short, they hope to increase their levels of human development.

Although distinct works have analyzed the relationship between tourism and human development by applying causality tests to determine the type of relationship between these variables, this study adopts a different approach. It analyzes the qualitative dynamic behavior between tourism and human development, to identify clusters of countries that display similar behavior with regard to this relationship.

Firstly, it is necessary to note the little movement there is of the countries between the different regimes, which indicates great stability, given that 80 countries (two-thirds of the sample) remained in the same regime throughout the entire period analyzed (1995–2019). These results regarding the stability of the countries in the different regimes differ significantly from the results obtained in other studies that have used the same technique for the analysis of the dynamic relationship between variables (Brida et al., 2020 ). This is because even when there is a movement of the countries between regimes, this happens, at most, between two or three regimes (Jordan and Samoa are the only exceptions, passing through all four regimes).

Furthermore, the results appear to show that groups of countries with a higher level of tourism specialization have higher levels of human development. Therefore, tourism is configured as an effective tool to improve development levels, as previously stated in works such as that of Cárdenas-García et al. ( 2015 ) conducting a joint analysis with data from 144 countries or Bojanic and Lo ( 2016 ), whose global analysis referred to a sample of 187 countries.

Specifically, these results are found both in the group of countries with the highest level of development, (countries of Group A versus the countries of Group B), as previously revealed in works such as that of Meyer and Meyer ( 2016 ) analyzing South Africa and that of Tan et al. ( 2019 ) analyzing Malaysia. These results were also found in the case of countries with a lower level of development (countries of Group D and F as compared to the countries of Group C), as previously suggested by works, such as that of Sharma et al. ( 2020 ) examining India or Croes ( 2012 ) analyzing Nicaragua.

However, despite these majority results, countries have been identified that, despite having an important tourism specialization (Belize, Botswana, Jamaica, Maldives, and Tunisia), had a low level of human development. This has not allowed for the high level of tourism specialization to become a tool to improve the living conditions of the population in these countries.

This exception may be due to the link between tourism and human development, which, in addition to being affected by the level of tourism specialization, also depends on the destination’s characteristics. These characteristics include the provision of infrastructure, the level of education, and the existing investment climate in the receiving countries, as previously suggested by Cárdenas-García and Pulido-Fernández ( 2019 ), or by the level of economic growth, the development of the urbanization process, or the degree of commercial openness of the receiving countries, as identified by Chattopadhyay et al. ( 2021 ).

Conclusions and policy implications

Distinct international organizations have shown that what is really important is not the contribution of tourism to economic growth, but rather, that this economic growth generated by the expansion of tourism activity permits the improvement of living conditions of the host population (EC, 2018 ; IADB - Inter-American Development Bank ( 2020 ); UNCTAD - United Nations Conference on Trade and Development ( 2020 )).

Given the importance of economic development for the host countries, empirical studies that analyze the relationship between tourism and economic development have begun to emerge. These works mainly link the multidimensional concept of development with human development, measured by the HDI. Here, the link between tourism and human development is produced through the economic growth generated by the expansion of tourism activity. This economic growth is used to develop policies to improve the host population’s education and health levels.

However, few such studies exist, and the scientific literature does not reveal a defined trend with regard to this relationship. Furthermore, most of these existing works rely on causality analyses to determine whether there is a relationship between tourism and human development. They do not analyze whether having a higher degree of tourism specialization, for groups of countries with similar levels of development, implies a higher level of human development, which would suggest that tourism promotes development to a greater extent than other economic activities.

Due to the methodology used, this empirical work cannot determine the type of relationship existing between tourism and development, that is, whether there is a unidirectional or bidirectional relationship between both variables. However, it does allow us to determine if countries with a higher level of tourism specialization have a higher level of development than those specializing in other productive activities.

This study aimed to contribute to the empirical discussion about the relationship between tourism and development through the use of a non-parametric and non-linear approach; specifically, the qualitative dynamic behavior of these two variables was compared using the definition of economic regime and clustering tools based on the concept of hierarchical and MST (Mantegna, 1999 ; Kruskal, 1956 ).

The results seem to indicate that tourism is an economic activity that can promote human development more than other economic activities. Indeed, at similar levels of human development, both in the case of countries with a high level of development (countries in Group A versus countries in Group B) and in the case of countries with a low level of development (countries in Groups D-F versus countries in Group C), the country groups with a higher level of tourism specialization have higher human development values than those countries specialized in other productive activities.

Therefore, public administrations should develop specific actions to increase the level of tourism specialization since tourism is a strategic tool that improves human development levels, as compared to other economic activities. It is necessary to invest in the improvement and expansion of tourism infrastructure, including the improvement of transportation systems in host destinations, increasing and improving the supply of accommodations and basic tourism-related services. Moreover, an attractive offer should be provided, both in terms of resources and attraction factors. This includes complementary services to attract a greater number of tourist flows, while developing destination promotion campaigns and, therefore, ensuring greater tourism specialization.

It should also be noted that, of the identified country groups, the most numerous one is that which includes countries from Group C, which is made up of 43 countries (approximately a third of the sample). This cluster is characterized by low tourism specialization and a low level of economic development, which seems to translate into a poverty trap, given that the low level of development prevents the expansion of the tourism activity, and, in turn, this lack of tourism development makes it difficult to increase the levels of development.

Policies should be developed that consider the lack of financial resources of these countries to carry out investment projects. International organizations and institutions linked to development, such as the United Nations Development Program, Inter-American Development Bank, or World Bank, should finance specific projects so that these countries may receive investments related to the improvement and expansion of tourism infrastructure, so as to improve human development through this activity. Suitable regulatory frameworks should be established in these countries, to encourage public-private collaboration for the development of tourism projects. In this way, private investments could make up for the lack of public financing in these destinations.

The analysis performed in this work has also identified groups of countries that, despite their high degree of tourism specialization, do not have high levels of human development (Belize, Botswana, Jamaica, Maldives, and Tunisia). This highlights the importance of identifying factors or characteristics that provide the destination with ideal initial conditions to permit the economic impacts generated by the expansion of tourism to be channeled into an improvement in human development. In addition to being conditioned by the host country’s level of tourism specialization, the link between tourism and human development also depends on infrastructure provision, education level, investment climate, urbanization level, and the degree of commercial openness. Although this current of scientific literature has not been widely studied, it has been addressed by some works analyzing the relationship between tourism and human development (Cárdenas-García and Pulido-Fernández, 2019 ; Chattopadhyay et al., 2021 ).

Policies established by public administrations should consider a dual objective: on the one hand, investing in the improvement and expansion of the tourism infrastructure and, on the other hand, increasing and improving the factors found to be determinant in configuring tourism as a tool for human development. Given that there are entities investing in projects linked to tourism aimed at improving the living conditions of the resident population, the failure to act on the determinant factors of this relationship could result in inefficient policies in terms of the allocation of resources linked to improved development.

Finally, this study has certain limitations, including the variables used to measure tourism specialization and economic development. With regard to tourism, it has been shown that changing the indicator used leads to differences in the results obtained. In terms of economic development, while other factors such as poverty level, quality of life, or income inequality are related to development, human development, and its measurement through HDI, is the most frequently used indicator to measure it. Moreover, the short period analyzed (1995–2019) is another limitation. There is a restriction in the initial period used since it is the first year in which data were available on development and this may determine the small variability between countries among the different regimes. Another limitation lies in the fact that it does not analyze the characteristics of the destination as a determinant in the relationship between tourism and human development, in accordance with the new current of the scientific literature. In terms of methodology, the choice of the measure used for the symbolization of the series can affect the results. For example, the mean may be influenced by outliers in the data, and this can be relevant for certain variables, such as tourism, which displays a high degree of variation. It would be interesting to perform the same exercise using other measures for the symbolization of the series, such as the truncated mean, the median, or some type of threshold.

Future lines of research may highlight the fact that this study consists of an analysis at the country level, although it is clear that the impacts of tourism are produced in the territory at the regional and local levels. As a result, it may be interesting to replicate this work at the regional level using different countries as an analysis, depending on the availability of such data.

Moreover, as a continuation of this study, in addition to the degree of tourism specialization, it may be interesting to analyze the type of tourism received by each of the groups of countries that have been identified. In other words, to examine whether the characteristics of the type of tourism received (accommodations, motivations, or level of expenditure) in each cluster also determine the relationship between tourism and human development. Furthermore, it may be interesting to introduce the influence of other factors on the relationship between tourism and development into the analysis of this relationship, as discussed previously in the limitations.

Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

Adler R (1998) Symbolic dynamics and Markov partitions. Bull Am Math Soc 35(1):1–56

Article   MathSciNet   Google Scholar  

Alcalá-Ordóñez A, Segarra V (2023) Tourism and economic development: a literature review to highlight main empirical findings. Tour Econ. https://doi.org/10.1177/13548166231219638

Akadiri SS, Akadiri AC, Alola UV (2017) Is there growth impact of tourism? Evidence from selected small island states. Curr Issues Tour 22(12):1480–1498. https://doi.org/10.1080/13683500.2017.1381947

Article   Google Scholar  

Anand S, Sen A (2000) The income component of the human development index. J Hum Dev 1(1):83–106. https://doi.org/10.1080/14649880050008782

Andergassen R, Candela G (2013) Less developed countries, tourism investments and local economic development. Rev Dev Econ 17(1):16–33. https://doi.org/10.1111/rode.12012

Antonakakis N, Dragouni M, Filis G (2015) How strong is the linkage between tourism and economic growth in Europe? Econ Model 44:142–155. https://doi.org/10.1016/j.econmod.2014.10.018

Antonakakis N, Dragouni M, Eeckels B, Filis G (2019) The tourism and economic growth enigma: examining an ambiguous relationship through multiple prisms. J Travel Res 58(1):3–24. https://doi.org/10.1177/0047287517744671

Balaguer J, Cantavella-Jordá M (2002) Tourism as a long-run economic growth factor: the Spanish case. Appl Econ 34:877–884. https://doi.org/10.1080/00036840110058923

Banerjee O, Cicowiez M, Morris EJ, Moreda A (2018) Boosting tourism’s contribution to growth and development: analysis of the evidence. Rev Dev Econ 22(3):1296–1320. https://doi.org/10.1111/rode.12385

Boehm B, Punzo LF (2001) Investment-productivity fluctuations and structural change. in Punzo, L.F. (ed), Cycles, growth and structural change, Routledge, London

Bojanic DC, Lo M (2016) A comparison of the moderating effect of tourism reliance on the economic development for islands and other countries. Tour Manag 53:207–214. https://doi.org/10.1016/j.tourman.2015.10.006

Brida JG, Punzo LF (2003) Symbolic time series analysis and dynamic regimes. Struct Change Econ Dyn 14(2):159–183

Brida JG (2008) The dynamic regime concept in Economics. Int J Econ Res 5(1):55–76

ADS   Google Scholar  

Brida JG, Cortes-Jimenez I, Pulina M (2016) Has the tourism-led growth hypothesis been validated? A literature review. Curr Issues Tour 19(5):394–430. https://doi.org/10.1080/13683500.2013.868414

Brida JG, Matesanz-Gómez M, Segarra V (2020) On the empirical relationship between tourism and economic growth. Tour Manag 81:104131. https://doi.org/10.1016/j.tourman.2020.104131

Cárdenas-García PJ, Pulido-Fernández JI (2019) Tourism as an economic development tool. Key factors. Curr Issues Tour 22(17):2082–2108. https://doi.org/10.1080/13683500.2017.1420042

Cárdenas-García PJ, Sánchez-Rivero M, Pulido-Fernández JI (2015) Does tourism growth influence economic development? J Travel Res 54(2):206–221. https://doi.org/10.1177/0047287513514297

Castro-Nuño M, Molina-Toucedo JA, Pablo-Romero MP (2013) Tourism and GDP: a meta-analysis of panel data studies. J Travel Res 52(6):745–758. https://doi.org/10.1177/0047287513478500

Chattopadhyay M, Kumar A, Ali S, Mitra SK (2021) Human development and tourism growth’s relationship across countries: a panel threshold analysis. J Sustain Tour 30(6):1384–1402. https://doi.org/10.1080/09669582.2021.1949017

Chi J (2020) Revisiting the tourism-inequality nexus: evidence from a panel of developed and developing economies. Curr Issues Tour 24(6):755–767. https://doi.org/10.1080/13683500.2020.1743243

Chingarande A, Saayman A (2018) Critical success factors for tourism‐led growth. Int J Tour Res 20(6):800–818. https://doi.org/10.1002/jtr.2233

Cristelli M, Tacchella A, Pietronero L (2015) The heterogeneous dynamics of economic complexity. PloS One 10(2):e0117174. https://doi.org/10.1371/journal.pone.0117174

Article   CAS   PubMed   PubMed Central   Google Scholar  

Croes R (2012) Assessing tourism development from Sen’s capability approach. J Travel Res 51(5):542–554. https://doi.org/10.1177/0047287511431323

Article   ADS   Google Scholar  

Croes R, Ridderstaat J, Van Niekerk M (2018) Connecting quality of life, tourism specialization, and economic growth in small island destinations: The case of Malta. Tour Manag 65:212–223. https://doi.org/10.1016/j.tourman.2017.10.010

Croes R, Ridderstaat J, Bak M, Zientara P (2021) Tourism specialization, economic growth, human development and transition economies: the case of Poland. Tour Manag 82:104181. https://doi.org/10.1016/j.tourman.2020.104181

Dritsakis N (2012) Tourism development and economic growth in seven Mediterranean countries: a panel data approach. Tour Econ 18(4):801–816. https://doi.org/10.5367/te.2012.0140

Easterly W (2002) The elusive quest for growth: Economists’ adventures and misadventures in the tropics. The MIT Press, Cambridge, MA

Google Scholar  

EC - European Commission (2018) Tourism and Transport: Key to Development. Luxembourg: European Commission

Eluwole KK, Bekun FV, Lasisi TT (2022) Fresh insights into tourism-led economic growth nexus: a systematic literature network analysis approach. Asia Pac J Tour Res 27(4):374–410. https://doi.org/10.1080/10941665.2022.2075775

Fahimi A, Akadiri SS, Seraj M, Akadiri AC (2018) Testing the role of tourism and human capital development in economic growth. A panel causality study of micro states. Tour Manag Perspect 28:62–70. https://doi.org/10.1016/j.tmp.2018.08.004

Folarin O, Adeniyi O (2019) Does tourism reduce poverty in sub-Saharan African Countries? J Travel Res 59(1):140–155. https://doi.org/10.1177/0047287518821736

Fonseca N, Sánchez Rivero M (2020) Granger causality between tourism and income: a meta-regression analysis. J Travel Res 59(4):642–660. https://doi.org/10.1177/0047287519851189

Garza-Rodriguez J (2019) Tourism and poverty reduction in Mexico: an ARDL cointegration approach. Sustainability 11:845. https://doi.org/10.3390/su11030845

Hussain-Shahzad SJ, Shahbaz M, Ferrer R, Kumar RR (2017) Tourism-led growth hypothesis in the top ten tourist destinations: new evidence using the quantile-on-quantile approach. Tour Manag 60:223–232. https://doi.org/10.1016/j.tourman.2016.12.006

IADB - Inter-American Development Bank (2020) Tourism Sector Assessment, Strategy, and Road Map for Development of the Caribbean Region. IADB, Washington DC

Jalil SA, Kamaruddin MN (2018) Examining the relationship between human development index and socio-economic variables: a panel data analysis. J Int Bus Econ Entrep 3(2):37–44. https://doi.org/10.24191/jibe.v3i2.14431

Kruskal JB (1956) On the shortest spanning tree of a graph and the traveling salesman problem. Proc Am Math Soc 7:48–50

Lee YS (2017) General theory of law and development. Cornell Int Law Rev 50(3):432–435

CAS   Google Scholar  

Li KX, Jin M, Shi W (2018) Tourism as an important impetus to promoting economic growth: a critical review. Tour Manag Perspect 26:135–142. https://doi.org/10.1016/j.tmp.2017.10.002

Lin VS, Yang Y, Li G (2019) Where can tourism-led growth and economy-driven tourism growth occur? J Travel Res 58(5):760–773. https://doi.org/10.1177/0047287518773919

Lind N (2019) A development of the human development index. Soc Indic Res 146:409–423. https://doi.org/10.1007/s11205-019-02133-90

Mantegna RN (1999) Hierarchical structure in financial markets. Eur Phys J B 11:193–197. https://doi.org/10.1007/s100510050929

Article   ADS   CAS   Google Scholar  

Mantegna RN, Stanley HE (2000) An introduction to Econophysics: Correlations and complexity in finance. Cambridge University Press, UK

Meyer DF, Meyer N (2016) The relationship between the tourism sector and local economic development (Led): the case of the Vaal Triangle region, South Africa. J Environ Manag Tour 3(15):466–472

Ngoo YT, Tey NP (2019) Human development index as a predictor of life satisfaction. J Popul Soc Stud 27(1):70–86. https://doi.org/10.25133/JPSSV27N1.005

Nguyen C, Schinckus C, Su T, Chong F (2021) The influence of tourism on income inequality. J Travel Res 60(7):1426–1444. https://doi.org/10.1177/0047287520954538

OECD - Organization for Economic Cooperation and Development (2010) Tourism Trends & Policies 2010. OECD, Paris

Ogwang T, Abdou A (2003) The choice of principal variables for computing some measures of human well-being. Soc Indic Res 64(1):139–152. https://doi.org/10.1023/A:1024788123572

Pérez-Rodríguez J, Rachinger H, Santana-Gallego M (2021) Testing the validity of the tourism-led growth hypothesis under long-range dependence. Curr Issues Tour 24(6):768–793. https://doi.org/10.1080/13683500.2020.1744537

Pulido-Fernández JI, Cárdenas-García PJ (2021) Analyzing the bidirectional relationship between tourism growth and economic development. J Travel Res 60(3):583–602. https://doi.org/10.1177/0047287520922316

Ranis G, Stewart F, Ramirez A (2000) Economic growth and human development. World Dev 28(2):197–219. https://doi.org/10.1016/S0305-750X(99)00131-X

Ridderstaat J, Croes R, Nijkamp P (2013) Tourism and long-run economic growth in Aruba. Int J Tour Res 16(5):472–487. https://doi.org/10.1002/jtr.1941

Ridderstaat J, Croes R, Nijkamp P (2016) The tourism development–quality of life nexus in a small island destination. J Travel Res 55(1):79–94. https://doi.org/10.1177/0047287514532372

Risso WA (2018) Tourism and economic growth: a worldwide study. Tour Anal 23(1):123–135. https://doi.org/10.3727/108354218X15143857349828

Rivera MA (2017) The synergies between human development, economic growth, and tourism within a developing country: an empirical model for Ecuador. J Destination Mark Manag 6:221–232. https://doi.org/10.1016/j.jdmm.2016.04.002

Rosselló-Nadal J, He J (2020) Tourist arrivals versus tourist expenditures in modelling tourism demand. Tour Econ 26(8):1311–1326. https://doi.org/10.1177/1354816619867810

Sajith GG, Malathi K (2020) Applicability of human development index for measuring economic well-being: a study on GDP and HDI indicators from Indian context. Indian Econ J 68(4):554–571. https://doi.org/10.1177/0019466221998620

Sen A (1990) Development as capability expansion. In Griffin K, Knight J (Eds.), Human development and the international development strategy for the 1990s (pp. 41–58). London, UK: McMillan

Sen A (1999) Development as freedom. Anchor Books, New York, NY

Sharma M, Mohapatra G, Giri AK (2020) Beyond growth: does tourism promote human development in India? Evidence from time series analysis. J Asian Financ Econ Bus 7(12):693–702. https://doi.org/10.13106/jafeb.2020.vol7.no12.693

Sokhanvar A, Çiftçioğlu S, Javid E (2018) Another look at tourism- economic development nexus. Tour Manag Perspect 26:97–106. https://doi.org/10.1016/j.tmp.2018.03.002

Song H, Wu DC (2021) A critique of tourism-led economic growth studies. J Travel Res 61(4):719–729. https://doi.org/10.1177/004728752110185

Tang C, Abosedra S (2016) Does tourism expansion effectively spur economic growth in Morocco and Tunisia? Evidence from time series and panel data. J Policy Res Tour Leis Events 8(2):127–145. https://doi.org/10.1080/19407963.2015.1113980

Tang CF, Tan EC (2018) Tourism-led growth hypothesis: a new global evidence. Cornell Hosp Q 59(3):304–311. https://doi.org/10.1177/1938965517735743

Tan YT, Gan PT, Hussin MYM, Ramli N (2019) The relationship between human development, tourism and economic growth: Evidence from Malaysia. Res World Econ 10(5):96–103. https://doi.org/10.5430/rwe.v10n5p96

Todaro MP, Smith SC (2020) Economic Development. 13th Edition. Addison Wesley, Boston

UNCTAD - United Nations Conference on Trade and Development (2011) Fourth United Nations Conference on Least Developed Countries. UNCTAD, Istanbul

UNCTAD - United Nations Conference on Trade and Development (2020) Economic Development in Africa Report 2020: Tackling Illicit Financial Flows for Sustainable Development in Africa. UNCTAD, Geneva

Book   Google Scholar  

United Nations Development Programme (2022) Human Development Reports 2021/2022. United Nations Development Programme, New York

UN Tourism (2006) UNWTO’s declaration on tourism and the millennium goals: Harnessing tourism for the millennium development goals. United Nations Tourism, Madrid

UN Tourism (2022) Tourism Data Dashboard. United Nations Tourism, Madrid

Wahyuningsih D, Yunaningsih A, Priadana MS, Wijaya A, Darma DC, Amalia S (2020) The dynamics of economic growth and development inequality in Borneo Island, Indonesia. J Appl Econ Sci ume XV(67):135–143. https://doi.org/10.14505/jaes.v15.1(67).12 . Spring 1

World Bank (1991) World Development Report 1991: The Challenge of Development. Washington, D.C: World Bank

World Bank (2022) World Bank Open Data. Washington, D.C.: World Bank

Download references

Author information

Authors and affiliations.

Department of Economics, University of Jaén, Campus Las Lagunillas s/n, Jaén, Spain

Pablo Juan Cárdenas-García

GIDE, Faculty of Economics, University of the Republic, Gonzalo Ramírez 1926, Montevideo, Uruguay

Juan Gabriel Brida & Verónica Segarra

You can also search for this author in PubMed   Google Scholar

Contributions

PJCG: conceptualization, funding acquisition, investigation, project administration, validation, resources, original draft writing, final version writing. JGB: conceptualization, investigation, formal analysis, methodology, validation, visualization. VS: conceptualization, investigation, data curation, formal analysis, methodology, resources, software, validation, original draft writing, final version writing.

Corresponding author

Correspondence to Pablo Juan Cárdenas-García .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Ethical approval

This article does not contain any studies with human participants performed by any of the authors.

Informed consent

Additional information.

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

Supplementary information

Rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/ .

Reprints and permissions

About this article

Cite this article.

Cárdenas-García, P.J., Brida, J.G. & Segarra, V. A qualitative dynamic analysis of the relationship between tourism and human development. Humanit Soc Sci Commun 11 , 1125 (2024). https://doi.org/10.1057/s41599-024-03663-5

Download citation

Received : 11 March 2024

Accepted : 21 August 2024

Published : 02 September 2024

DOI : https://doi.org/10.1057/s41599-024-03663-5

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

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

• Databank Solution
• Credit Risk Solution
• Valuation Analytics Solution
• ESG Sustainability Solution
• Quantitative Finance Solution
• Regulatory Technology Solution
• Product Introduction
• Service Provide Method
• Learning Video

Quantitative Data Analysis Guide: Methods, Examples & Uses

This guide will introduce the types of data analysis used in quantitative research, then discuss relevant examples and applications in the finance industry.

Table of Contents

An Overview of Quantitative Data Analysis

What is quantitative data analysis and what is it for .

Quantitative data analysis is the process of interpreting meaning and extracting insights from numerical data , which involves mathematical calculations and statistical reviews to uncover patterns, trends, and relationships between variables.

Beyond academic and statistical research, this approach is particularly useful in the finance industry. Financial data, such as stock prices, interest rates, and economic indicators, can all be quantified with statistics and metrics to offer crucial insights for informed investment decisions. To illustrate this, here are some examples of what quantitative data is usually used for:

• Measuring Differences between Groups: For instance, analyzing historical stock prices of different companies or asset classes can reveal which companies consistently outperform the market average.
• Assessing Relationships between Variables: An investor could analyze the relationship between a company’s price-to-earnings ratio (P/E ratio) and relevant factors, like industry performance, inflation rates, interests, etc, allowing them to predict future stock price growth.
• Testing Hypotheses: For example, an investor might hypothesize that companies with strong ESG (Environment, Social, and Governance) practices outperform those without. By categorizing these companies into two groups (strong ESG vs. weak ESG practices), they can compare the average return on investment (ROI) between the groups while assessing relevant factors to find evidence for the hypothesis. 

Ultimately, quantitative data analysis helps investors navigate the complex financial landscape and pursue profitable opportunities.

Quantitative Data Analysis VS. Qualitative Data Analysis

Although quantitative data analysis is a powerful tool, it cannot be used to provide context for your research, so this is where qualitative analysis comes in. Qualitative analysis is another common research method that focuses on collecting and analyzing non-numerical data , like text, images, or audio recordings to gain a deeper understanding of experiences, opinions, and motivations. Here’s a table summarizing its key differences between quantitative data analysis:

Due to their characteristics, quantitative analysis allows you to measure and compare large datasets; while qualitative analysis helps you understand the context behind the data. In some cases, researchers might even use both methods together for a more comprehensive understanding, but we’ll mainly focus on quantitative analysis for this article.

The 2 Main Quantitative Data Analysis Methods

Once you have your data collected, you have to use descriptive statistics or inferential statistics analysis to draw summaries and conclusions from your raw numbers. 

As its name suggests, the purpose of descriptive statistics is to describe your sample . It provides the groundwork for understanding your data by focusing on the details and characteristics of the specific group you’ve collected data from. 

On the other hand, inferential statistics act as bridges that connect your sample data to the broader population you’re truly interested in, helping you to draw conclusions in your research. Moreover, choosing the right inferential technique for your specific data and research questions is dependent on the initial insights from descriptive statistics, so both of these methods usually go hand-in-hand.

Descriptive Statistics Analysis

With sophisticated descriptive statistics, you can detect potential errors in your data by highlighting inconsistencies and outliers that might otherwise go unnoticed. Additionally, the characteristics revealed by descriptive statistics will help determine which inferential techniques are suitable for further analysis.

Measures in Descriptive Statistics

One of the key statistical tests used for descriptive statistics is central tendency . It consists of mean, median, and mode, telling you where most of your data points cluster:

• Mean: It refers to the “average” and is calculated by adding all the values in your data set and dividing by the number of values.
• Median: The middle value when your data is arranged in ascending or descending order. If you have an odd number of data points, the median is the exact middle value; with even numbers, it’s the average of the two middle values. 
• Mode: This refers to the most frequently occurring value in your data set, indicating the most common response or observation. Some data can have multiple modes (bimodal) or no mode at all.

Another statistic to test in descriptive analysis is the measures of dispersion , which involves range and standard deviation, revealing how spread out your data is relative to the central tendency measures:

• Range: It refers to the difference between the highest and lowest values in your data set. 
• Standard Deviation (SD): This tells you how the data is distributed within the range, revealing how much, on average, each data point deviates from the mean. Lower standard deviations indicate data points clustered closer to the mean, while higher standard deviations suggest a wider spread.

The shape of the distribution will then be measured through skewness. 

• Skewness: A statistic that indicates whether your data leans to one side (positive or negative) or is symmetrical (normal distribution). A positive skew suggests more data points concentrated on the lower end, while a negative skew indicates more data points on the higher end.

While the core measures mentioned above are fundamental, there are additional descriptive statistics used in specific contexts, including percentiles and interquartile range.

• Percentiles: This divides your data into 100 equal parts, revealing what percentage of data falls below a specific value. The 25th percentile (Q1) is the first quartile, the 50th percentile (Q2) is the median, and the 75th percentile (Q3) is the third quartile. Knowing these quartiles can help visualize the spread of your data.
• Interquartile Range (IQR): This measures the difference between Q3 and Q1, representing the middle 50% of your data.

Example of Descriptive Quantitative Data Analysis 

Let’s illustrate these concepts with a real-world example. Imagine a financial advisor analyzing a client’s portfolio. They have data on the client’s various holdings, including stock prices over the past year. With descriptive statistics they can obtain the following information:

• Central Tendency: The mean price for each stock reveals its average price over the year. The median price can further highlight if there were any significant price spikes or dips that skewed the mean.
• Measures of Dispersion: The standard deviation for each stock indicates its price volatility. A high standard deviation suggests the stock’s price fluctuated considerably, while a low standard deviation implies a more stable price history. This helps the advisor assess each stock’s risk profile.
• Shape of the Distribution: If data allows, analyzing skewness can be informative. A positive skew for a stock might suggest more frequent price drops, while a negative skew might indicate more frequent price increases.

By calculating these descriptive statistics, the advisor gains a quick understanding of the client’s portfolio performance and risk distribution. For instance, they could use correlation analysis to see if certain stock prices tend to move together, helping them identify expansion opportunities within the portfolio.

While descriptive statistics provide a foundational understanding, they should be followed by inferential analysis to uncover deeper insights that are crucial for making investment decisions.

Inferential Statistics Analysis

Inferential statistics analysis is particularly useful for hypothesis testing , as you can formulate predictions about group differences or potential relationships between variables , then use statistical tests to see if your sample data supports those hypotheses.

However, the power of inferential statistics hinges on one crucial factor: sample representativeness . If your sample doesn’t accurately reflect the population, your predictions won’t be very reliable. 

Statistical Tests for Inferential Statistics

Here are some of the commonly used tests for inferential statistics in commerce and finance, which can also be integrated to most analysis software:

• T-Tests: This compares the means, standard deviation, or skewness of two groups to assess if they’re statistically different, helping you determine if the observed difference is just a quirk within the sample or a significant reflection of the population.
• ANOVA (Analysis of Variance): While T-Tests handle comparisons between two groups, ANOVA focuses on comparisons across multiple groups, allowing you to identify potential variations and trends within the population.
• Correlation Analysis: This technique tests the relationship between two variables, assessing if one variable increases or decreases with the other. However, it’s important to note that just because two financial variables are correlated and move together, doesn’t necessarily mean one directly influences the other.
• Regression Analysis: Building on correlation, regression analysis goes a step further to verify the cause-and-effect relationships between the tested variables, allowing you to investigate if one variable actually influences the other.
• Cross-Tabulation: This breaks down the relationship between two categorical variables by displaying the frequency counts in a table format, helping you to understand how different groups within your data set might behave. The data in cross-tabulation can be mutually exclusive or have several connections with each other. 
• Trend Analysis: This examines how a variable in quantitative data changes over time, revealing upward or downward trends, as well as seasonal fluctuations. This can help you forecast future trends, and also lets you assess the effectiveness of the interventions in your marketing or investment strategy.
• MaxDiff Analysis: This is also known as the “best-worst” method. It evaluates customer preferences by asking respondents to choose the most and least preferred options from a set of products or services, allowing stakeholders to optimize product development or marketing strategies.
• Conjoint Analysis: Similar to MaxDiff, conjoint analysis gauges customer preferences, but it goes a step further by allowing researchers to see how changes in different product features (price, size, brand) influence overall preference.
• TURF Analysis (Total Unduplicated Reach and Frequency Analysis): This assesses a marketing campaign’s reach and frequency of exposure in different channels, helping businesses identify the most efficient channels to reach target audiences.
• Gap Analysis: This compares current performance metrics against established goals or benchmarks, using numerical data to represent the factors involved. This helps identify areas where performance falls short of expectations, serving as a springboard for developing strategies to bridge the gap and achieve those desired outcomes.
• SWOT Analysis (Strengths, Weaknesses, Opportunities, and Threats): This uses ratings or rankings to represent an organization’s internal strengths and weaknesses, along with external opportunities and threats. Based on this analysis, organizations can create strategic plans to capitalize on opportunities while minimizing risks.
• Text Analysis: This is an advanced method that uses specialized software to categorize and quantify themes, sentiment (positive, negative, neutral), and topics within textual data, allowing companies to obtain structured quantitative data from surveys, social media posts, or customer reviews.

Example of Inferential Quantitative Data Analysis

If you’re a financial analyst studying the historical performance of a particular stock, here are some predictions you can make with inferential statistics:

• The Differences between Groups: You can conduct T-Tests to compare the average returns of stocks in the technology sector with those in the healthcare sector. It can help assess if the observed difference in returns between these two sectors is simply due to random chance or if it’s statistically significant due to a significant difference in their performance.
• The Relationships between Variables: If you’re curious about the connection between a company’s price-to-earnings ratio (P/E ratios) and its future stock price movements, conducting correlation analysis can let you measure the strength and direction of this relationship. Is there a negative correlation, suggesting that higher P/E ratios might be associated with lower future stock prices? Or is there no significant correlation at all?

Understanding these inferential analysis techniques can help you uncover potential relationships and group differences that might not be readily apparent from descriptive statistics alone. Nonetheless, it’s important to remember that each technique has its own set of assumptions and limitations . Some methods are designed for parametric data with a normal distribution, while others are suitable for non-parametric data. 

Guide to Conduct Data Analysis in Quantitative Research

Now that we have discussed the types of data analysis techniques used in quantitative research, here’s a quick guide to help you choose the right method and grasp the essential steps of quantitative data analysis.

How to Choose the Right Quantitative Analysis Method?

Choosing between all these quantitative analysis methods may seem like a complicated task, but if you consider the 2 following factors, you can definitely choose the right technique:

Factor 1: Data Type

The data used in quantitative analysis can be categorized into two types, discrete data and continuous data, based on how they’re measured. They can also be further differentiated by their measurement scale. The four main types of measurement scales include: nominal, ordinal, interval or ratio. Understanding the distinctions between them is essential for choosing the appropriate statistical methods to interpret the results of your quantitative data analysis accurately.

Discrete data , which is also known as attribute data, represents whole numbers that can be easily counted and separated into distinct categories. It is often visualized using bar charts or pie charts, making it easy to see the frequency of each value. In the financial world, examples of discrete quantitative data include:

• The number of shares owned by an investor in a particular company
• The number of customer transactions processed by a bank per day
• Bond ratings (AAA, BBB, etc.) that represent discrete categories indicating the creditworthiness of a bond issuer
• The number of customers with different account types (checking, savings, investment) as seen in the pie chart below:

Discrete data usually use nominal or ordinal measurement scales, which can be then quantified to calculate their mode or median. Here are some examples:

• Nominal: This scale categorizes data into distinct groups with no inherent order. For instance, data on bank account types can be considered nominal data as it classifies customers in distinct categories which are independent of each other, either checking, savings, or investment accounts. and no inherent order or ranking implied by these account types.
• Ordinal: Ordinal data establishes a rank or order among categories. For example, investment risk ratings (low, medium, high) are ordered based on their perceived risk of loss, making it a type or ordinal data.

Conversely, continuous data can take on any value and fluctuate over time. It is usually visualized using line graphs, effectively showcasing how the values can change within a specific time frame. Examples of continuous data in the financial industry include:

• Interest rates set by central banks or offered by banks on loans and deposits
• Currency exchange rates which also fluctuate constantly throughout the day
• Daily trading volume of a particular stock on a specific day
• Stock prices that fluctuate throughout the day, as seen in the line graph below:

Source: Freepik

The measurement scale for continuous data is usually interval or ratio . Here is breakdown of their differences:

• Interval: This builds upon ordinal data by having consistent intervals between each unit, and its zero point doesn’t represent a complete absence of the variable. Let’s use credit score as an example. While the scale ranges from 300 to 850, the interval between each score rating is consistent (50 points), and a score of zero wouldn’t indicate an absence of credit history, but rather no credit score available. 
• Ratio: This scale has all the same characteristics of interval data but also has a true zero point, indicating a complete absence of the variable. Interest rates expressed as percentages are a classic example of ratio data. A 0% interest rate signifies the complete absence of any interest charged or earned, making it a true zero point.

Factor 2: Research Question

You also need to make sure that the analysis method aligns with your specific research questions. If you merely want to focus on understanding the characteristics of your data set, descriptive statistics might be all you need; if you need to analyze the connection between variables, then you have to include inferential statistics as well.

How to Analyze Quantitative Data 

Step 1: data collection  .

Depending on your research question, you might choose to conduct surveys or interviews. Distributing online or paper surveys can reach a broad audience, while interviews allow for deeper exploration of specific topics. You can also choose to source existing datasets from government agencies or industry reports.

Step 2: Data Cleaning

Raw data might contain errors, inconsistencies, or missing values, so data cleaning has to be done meticulously to ensure accuracy and consistency. This might involve removing duplicates, correcting typos, and handling missing information.

Furthermore, you should also identify the nature of your variables and assign them appropriate measurement scales , it could be nominal, ordinal, interval or ratio. This is important because it determines the types of descriptive statistics and analysis methods you can employ later. Once you categorize your data based on these measurement scales, you can arrange the data of each category in a proper order and organize it in a format that is convenient for you.

Step 3: Data Analysis

Based on the measurement scales of your variables, calculate relevant descriptive statistics to summarize your data. This might include measures of central tendency (mean, median, mode) and dispersion (range, standard deviation, variance). With these statistics, you can identify the pattern within your raw data. 

Then, these patterns can be analyzed further with inferential methods to test out the hypotheses you have developed. You may choose any of the statistical tests mentioned above, as long as they are compatible with the characteristics of your data.

Step 4. Data Interpretation and Communication 

Now that you have the results from your statistical analysis, you may draw conclusions based on the findings and incorporate them into your business strategies. Additionally, you should also transform your findings into clear and shareable information to facilitate discussion among stakeholders. Visualization techniques like tables, charts, or graphs can make complex data more digestible so that you can communicate your findings efficiently. 

Useful Quantitative Data Analysis Tools and Software 

We’ve compiled some commonly used quantitative data analysis tools and software. Choosing the right one depends on your experience level, project needs, and budget. Here’s a brief comparison: 

Quantitative Data in Finance and Investment

So how does this all affect the finance industry? Quantitative finance (or quant finance) has become a growing trend, with the quant fund market valued at $16,008.69 billion in 2023. This value is expected to increase at the compound annual growth rate of 10.09% and reach $31,365.94 billion by 2031, signifying its expanding role in the industry.

What is Quant Finance?

Quant finance is the process of using massive financial data and mathematical models to identify market behavior, financial trends, movements, and economic indicators, so that they can predict future trends.These calculated probabilities can be leveraged to find potential investment opportunities and maximize returns while minimizing risks.

Common Quantitative Investment Strategies

There are several common quantitative strategies, each offering unique approaches to help stakeholders navigate the market:

1. Statistical Arbitrage

This strategy aims for high returns with low volatility. It employs sophisticated algorithms to identify minuscule price discrepancies across the market, then capitalize on them at lightning speed, often generating short-term profits. However, its reliance on market efficiency makes it vulnerable to sudden market shifts, posing a risk of disrupting the calculations.

2. Factor Investing 

This strategy identifies and invests in assets based on factors like value, momentum, or quality. By analyzing these factors in quantitative databases , investors can construct portfolios designed to outperform the broader market. Overall, this method offers diversification and potentially higher returns than passive investing, but its success relies on the historical validity of these factors, which can evolve over time.

3. Risk Parity

This approach prioritizes portfolio balance above all else. Instead of allocating assets based on their market value, risk parity distributes them based on their risk contribution to achieve a desired level of overall portfolio risk, regardless of individual asset volatility. Although it is efficient in managing risks while potentially offering positive returns, it is important to note that this strategy’s complex calculations can be sensitive to unexpected market events.

4. Machine Learning & Artificial Intelligence (AI)

Quant analysts are beginning to incorporate these cutting-edge technologies into their strategies. Machine learning algorithms can act as data sifters, identifying complex patterns within massive datasets; whereas AI goes a step further, leveraging these insights to make investment decisions, essentially mimicking human-like decision-making with added adaptability. Despite the hefty development and implementation costs, its superior risk-adjusted returns and uncovering hidden patterns make this strategy a valuable asset.

Pros and Cons of Quantitative Data Analysis

Advantages of quantitative data analysis, minimum bias for reliable results.

Quantitative data analysis relies on objective, numerical data. This minimizes bias and human error, allowing stakeholders to make investment decisions without emotional intuitions that can cloud judgment. In turn, this offers reliable and consistent results for investment strategies.

Precise Calculations for Data-Driven Decisions

Quantitative analysis generates precise numerical results through statistical methods. This allows accurate comparisons between investment options and even predictions of future market behavior, helping investors make informed decisions about where to allocate their capital while managing potential risks.

Generalizability for Broader Insights 

By analyzing large datasets and identifying patterns, stakeholders can generalize the findings from quantitative analysis into broader populations, applying them to a wider range of investments for better portfolio construction and risk management

Efficiency for Extensive Research

Quantitative research is more suited to analyze large datasets efficiently, letting companies save valuable time and resources. The softwares used for quantitative analysis can automate the process of sifting through extensive financial data, facilitating quicker decision-making in the fast-paced financial environment.

Disadvantages of Quantitative Data Analysis

Limited scope .

By focusing on numerical data, quantitative analysis may provide a limited scope, as it can’t capture qualitative context such as emotions, motivations, or cultural factors. Although quantitative analysis provides a strong starting point, neglecting qualitative factors can lead to incomplete insights in the financial industry, impacting areas like customer relationship management and targeted marketing strategies.

Oversimplification 

Breaking down complex phenomena into numerical data could cause analysts to overlook the richness of the data, leading to the issue of oversimplification. Stakeholders who fail to understand the complexity of economic factors or market trends could face flawed investment decisions and missed opportunities.

Reliable Quantitative Data Solution 

In conclusion, quantitative data analysis offers a deeper insight into market trends and patterns, empowering you to make well-informed financial decisions. However, collecting comprehensive data and analyzing them can be a complex task that may divert resources from core investment activity. 

As a reliable provider, TEJ understands these concerns. Our TEJ Quantitative Investment Database offers high-quality financial and economic data for rigorous quantitative analysis. This data captures the true market conditions at specific points in time, enabling accurate backtesting of investment strategies.

Furthermore, TEJ offers diverse data sets that go beyond basic stock prices, encompassing various financial metrics, company risk attributes, and even broker trading information, all designed to empower your analysis and strategy development. Save resources and unlock the full potential of quantitative finance with TEJ’s data solutions today!

Subscribe to newsletter

• Data Analysis 107
• Market Research 48
• TQuant Lab 25
• Solution&DataSets
• Privacy Policy Statement
• Personal Data Policy
• Copyright Information
• Website licensing terms
• Information Security Policy 

• 11th Floor, No. 57, DongXing Road, Taipei 11070, Taiwan(R.O.C.)
• +886-2-8768-1088
• +886-2-8768-1336
• [email protected]

Market Research Plan

In 1970, food and drink sales of the  US restaurant industry  reached only 42.8 billion US dollars, which is way behind the 745.61 billion US dollar sales of 2015. According to the statistic posted in statista, this number should grow in the next few years. In fact, the website reported that from the 2015’s over 14 million employees of the restaurant industry, it should increase up to 16 million in 2026. However, as a result of this growth, there will be possibilities that the market will be saturated and more competitive. Thus, as a business owner, you will need to gear up and gain an edge to stand out in the market. By conducting market research for a restaurant, you can prepare your business to become more competitive and strategic, which will ensure its success.

What Do You Need to Know About Market Research?

Market research is an essential component of a business plan which aims to get information concerning the target market of a business. Through this study, you will determine the chances of a proposed service or new product to survive in the market. As part of market research, you need to develop a research plan.

What is Market Research Plan?

In general, market research plan is the foundation of a detailed research proposal . This document contains the initial thoughts about the research project that you are planning to take place logically and concisely, which is a crucial content of market research. Simply put, by obtaining a market research plan, you can thoroughly examine how your product or service will proceed in a specific domain.

2+ Market Research Plan Examples

Conducting market research will give significant benefits to your business. However, to materialize it, you may need to ensure that you build your market research plan correctly. Below is a list of the market research plan samples and templates that you can use as a guide.

1. Market Research Plan Template

• Google Docs
• Apple Pages

Size: 19 KB

2. Sample Market Research Plan Example

Size: 68 KB

3. Basic Market Research Plan Example

Size: 151 KB

4. Market Research Business Plan Example

Size: 600 KB

How to Develop a Strong Market Research Project Plan?

Now that you know how a marketing research plan should look, make a secure market research plan by following the steps below:

1. Set Goals and Objectives

What do you want to attain with your research? Your goals and objectives should answer that question. You can start by forming a general marketing goal . You will, then, make it more specific. This goal will help you focus and direct the entire research process to make the best data-driven marketing decisions. To determine the most critical issue, you may conduct qualitative research . This research methodology ensures that you address the issue that really requires an urgent solution.

2. Determine Your Target Respondents and Appropriate Distribution Method

In this step, you will identify the right people to get the information that you need to create the right decision for your marketing goals. After that, list down the best possible ways for the data gathering. For example, your target market is veterans. You may want to use more appropriate channels such as direct mails, phone, or personal interview. Once you have chosen the most appropriate data collection method, create an outline that will allow your team to get the most relevant information from your target market or audience.

4. Brainstorm for the Right Questions

In deciding the right questions for your marketing research, it is crucial to keep your study goals in mind. Only include items that are relevant to the study to come up with the best business decisions. Asking the wrong questions may lead to inadequate conclusions. Data-driven solutions mostly obtained through quantitative research questions. You can still use qualitative research questions but make it minimal to avoid making the respondents bored and held up, which can lead to survey abandonment. As much as possible, make your survey short and answerable in less than 5 minutes. Otherwise, you may want to find an alternative option in getting the desired data. Also, it would help if you will consider other factors in building the right questions. Refrain from asking sensitive, personal, and offensive questions. To do it, research your target audience.

5. Analyze the Data

Start this step by cleaning your survey data. To do it, filter out any low-quality responses. These items can affect your decision-making negatively. Basing on the set standards, remove the outlier responses. To do that, determine if the respondents answered in the desired format. If not, especially if it has become a trend, disqualify the question or conduct another data-gathering or investigation for this question. In this process, you will also find out if the answers of the participants are contributing to your research goals. At the end of this stage, you will, then, share your findings. To effectively show your results, you can use data visualization methods such as charts, graphs, and infographics.

6. Create a Data-Driven Marketing Decisions

Now that you have the necessary market research data, you can come up with a data-driven decision. Whether you are running a pharmaceutical firm or a corporal business such as Coca Cola, you can develop a new marketing campaign and other relevant business actions without unnecessary worries since you have directly reached out to your target market.

In a market that is becoming more competitive, creating a market research plan for a new product of your business can give you an advantage and an edge over your opponents. This type of method will also save your time, effort, and money because it allows you to determine the proper actions that you can take towards the corporate goals in terms of marketing and other relevant sectors.

Text prompt

• Instructive
• Professional

Create a study plan for final exams in high school

Develop a project timeline for a middle school science fair.

• Open access
• Published: 26 August 2024

Solid health care waste management practice in Ethiopia, a convergent mixed method study

• Yeshanew Ayele Tiruneh 1 ,
• L. M. Modiba 2 &
• S. M. Zuma 2  

BMC Health Services Research volume  24 , Article number:  985 ( 2024 ) Cite this article

66 Accesses

Metrics details

Introduction

Healthcare waste is any waste generated by healthcare facilities that is considered potentially hazardous to health. Solid healthcare waste is categorized into infectious and non-infectious wastes. Infectious waste is material suspected of containing pathogens and potentially causing disease. Non-infectious waste includes wastes that have not been in contact with infectious agents, hazardous chemicals, or radioactive substances, similar to household waste, i.e. plastic, papers and leftover foods.

This study aimed to investigate solid healthcare waste management practices and develop guidelines to improve solid healthcare waste management practices in Ethiopia. The setting was all health facilities found in Hossaena town.

A mixed-method study design was used. For the qualitative phase of this study, eight FGDs were conducted from 4 government health facilities, one FGD from each private health facility (which is 37 in number), and forty-five FGDs were conducted. Four FGDs were executed with cleaners; another four were only health care providers because using homogeneous groups promotes discussion. The remaining 37 FGDs in private health facilities were mixed from health professionals and cleaners because of the number of workers in the private facilities. For the quantitative phase, all health facilities and health facility workers who have direct contact with healthcare waste management practice participated in this study. Both qualitative and quantitative study participants were taken from the health facilities found in Hossaena town.

Seventeen (3.1%) health facility workers have hand washing facilities. Three hundred ninety-two (72.6%) of the participants agree on the availability of one or more personal protective equipment (PPE) in the facility ‘‘ the reason for the absence of some of the PPEs, like boots and goggles, and the shortage of disposable gloves owes to cost inflation from time to time and sometimes absent from the market’’ . The observational finding shows that colour-coded waste bins are available in 23 (9.6%) rooms. 90% of the sharp containers were reusable, and 100% of the waste storage bins were plastic buckets that were easily cleanable. In 40 (97.56%) health facilities, infectious wastes were collected daily from the waste generation areas to the final disposal points. Two hundred seventy-one (50.2%) of the respondents were satisfied or agreed that satisfactory procedures are available in case of an accident. Only 220 (40.8%) respondents were vaccinated for the Hepatitis B virus.

Hand washing facilities, personal protective equipment and preventive vaccinations are not readily available for health workers. Solid waste segregation practices are poor and showed that solid waste management practices (SWMP) are below the acceptable level.

Peer Review reports

Healthcare waste (HCW) encompasses all types of waste generated while providing health-related services, spanning activities such as diagnosis, immunization, treatment, and research. It constitutes a diverse array of materials, each presenting potential hazards to health and the environment. Within the realm of HCW, one finds secretions and excretions from humans, cultures, and waste containing a stock of infectious agents. Discarded plastic materials contaminated with blood or other bodily fluids, pathological wastes, and discarded medical equipment are classified as healthcare waste. Sharps, including needles, scalpels, and other waste materials generated during any healthcare service provision, are also considered potentially hazardous to health [ 1 ].

Healthcare waste in solid form (HCW) is commonly divided into two primary groups: infectious and non-infectious. The existence of pathogens in concentrations identifies infectious waste or amounts significant enough to induce diseases in vulnerable hosts [ 1 ] If healthcare facility waste is free from any combination with infectious agents, nearly 85% is categorized as non-hazardous waste, exhibiting characteristics similar to conventional solid waste found in households [ 2 ]. World Health Organization (WHO) recommends that appropriate colour-coded waste receptacles be available in all medical and other waste-producing areas [ 3 ].

Solid waste produced in the course of healthcare activities carries a higher potential for infection and injury than any other type of waste. Improper disposal of sharps waste increases the risk of disease transmission among health facility workers and general populations [ 1 ]. Inadequate and inappropriate handling of healthcare waste may have serious public health consequences and a significant environmental impact. The World Health Organization (2014) guidelines also include the following guidance for hand washing and the use of alcohol-based hand rubs: Wash hands before starting work, before entering an operating theatre, before eating, after touching contaminated objects, after using a toilet, and in all cases where hands are visibly soiled [ 4 ].

Among the infectious waste category, sharps waste is the most hazardous waste because of its ability to puncture the skin and cause infection [ 3 ]. Accidents or occurrences, such as near misses, spills, container damage, improper waste segregation, and incidents involving sharps, must be reported promptly to the waste management officer or an assigned representative [ 5 ].

Africa is facing a growing waste management crisis. While the volumes of waste generated in Africa are relatively small compared to developed regions, the mismanagement of waste in Africa already impacts human and environmental health. Infectious waste management has always remained a neglected public health problem in developing countries, resulting in a high burden of environmental pollution affecting the general masses. In Ethiopia, there is no updated separate regulation specific to healthcare waste management in the country to enforce the proper management of solid HCW [ 6 ].

In Ethiopia, like other developing countries, healthcare waste segregation practice was not given attention and did not meet the minimum HCWM standards, and it is still not jumped from paper. Previous study reveals that healthcare waste generation rates are significantly higher than the World Health Organization threshold, which ranges from 29.5–53.12% [ 7 , 8 ]. In Meneilk II Hospital, the proportion of infectious waste was 53.73%, and in the southern and northern parts of Ethiopia, it was 34.3 and 53%, respectively. Generally, this figure shows a value 3 to 4 times greater than the threshold value recommended by the World Health Organization [ 7 ].

Except for sharp wastes, segregation practice was poor, and all solid wastes were collected without respecting the colour-coded waste disposal system [ 9 ]. The median waste generation rate was found to vary from 0.361- 0.669 kg/patient/day, comprising 58.69% non-hazardous and 41.31% hazardous wastes. The amount of waste generated increased as the number of patients flow increased. Public hospitals generated a high proportion of total healthcare waste (59.22%) in comparison with private hospitals (40.48) [ 10 ]. The primary SHCW treatment and disposal mechanism was incineration, open burning, burring into unprotected pits and open dumping on municipal dumping sites as well as in the hospital backyard. Carelessness, negligence of the health workers, patients and cleaners, and poor commitment of the facility leaders were among the major causes of poor HCWM practice in Ethiopia [ 9 ]. This study aimed to investigate solid healthcare waste management practices and develop guidelines to improve solid healthcare waste management practices in Ethiopia.

The setting for this study was all health facilities found in Hossaena town, which is situated 232 kms from the capital city of Ethiopia, Addis Ababa, and 165 kms from the regional municipality of Hawasa. The health facilities found in the town were one university hospital, one private surgical centre, three government health centres, 17 medium clinics, and 19 small clinics were available in the city and; health facility workers who have direct contact with generating and disposal of HCW and those who are responsible as a manager of health facilities found in Hossaena town are the study settings. All health facilities except drug stores and health facility workers who have direct contact with healthcare waste generation participated in this study.

A mixed-method study design was used. For the quantitative part of this study, all healthcare workers who have direct contact with healthcare waste management practice participated in this study, and one focus group discussion from each health facility was used. Both of the study participants were taken from the same population. All health facility workers who have a role in healthcare waste management practice were included in the quantitative part of this study. The qualitative data collection phase used open-ended interviews, focus group discussions, and visual material analysis like posters and written materials. All FGDs were conducted by the principal investigator, one moderator, and one note-taker, and it took 50 to 75 min. 4–6 participants participated in each FGD.

According to Elizabeth (2018: 5), cited by Creswell and Plano (2007: 147), the mixed method is one of the research designs with philosophical assumptions as well as methods of inquiry. As a method, it focuses on collecting, analyzing, and mixing both quantitative and qualitative data in a single study. As a methodology, it involves philosophical assumptions guiding the direction of the collection and analysis and combining qualitative and quantitative approaches in many phases of the research project. The central premise is that using qualitative and quantitative approaches together provides a better understanding of the research problems than either approach alone.

The critical assumption of the concurrent mixed methods approach in this study is that quantitative and qualitative data provide different types of information, often detailed views of participants’ solid waste management practice qualitatively and scores on instruments quantitatively, and together, they yield results that should be the same. In this approach, the researcher collected quantitative and qualitative data almost simultaneously and analyzed them separately to cross-validate or compare whether the findings were similar or different between the qualitative and quantitative information. Concurrent approaches to the data collection process are less time-consuming than other types of mixed methods studies because both data collection processes are conducted on time and at the same visit to the field [ 11 ].

Data collection

The data collection involves collecting both quantitative and qualitative data simultaneously. The quantitative phase of this study assessed three components. Health care waste segregation practice, the availability of waste segregation equipment for HCW segregation, temporary storage facilities, transportation for final disposal, and disposal facilities data were collected using a structured questionnaire and observation of HCW generation. Recycling or re-using practice, waste treatment, the availability of the HCWM committee, and training data were collected.

Qualitative data collection

The qualitative phase of the data collection for this study was employed by using focus group discussions and semi-structured interviews about SHCWMP. Two focus group discussions (FGD) from each health facility were conducted in the government health facilities, one at the administrative level and one at the technical worker level, and one FGD was conducted for all private health facilities because of the number of available health facility workers. Each focus group has 4–6 individuals.

In this study, the qualitative and the quantitative data provide different information, and it is suitable for this study to compare and contrast the findings of the two results to obtain the best understanding of this research problem.

Quantitative data collection

The quantitative data were entered into Epi data version 3.1 to minimize the data entry mistakes and exported to the statistical package for social science SPSS window version 27.0 for analysis. A numeric value was assigned to each response in a database, cleaning the data, recoding, establishing a codebook, and visually inspecting the trends to check whether the data were typically distributed.

Data analysis

Data were analyzed quantitatively by using relevant statistical tools, such as SPSS. Descriptive statistics and the Pearson correlation test were used for the bivariate associations and analysis of variance (ANOVA) to compare the HCW generation rate between private and government health facilities and between clinics, health centres and hospitals in the town. Normality tests were performed to determine whether the sample data were drawn from a normally distributed population.

The Shapiro–Wilk normality tests were used to calculate a test statistic based on the sample data and compare it to critical values. The Shapiro–Wilk test is a statistical test used to assess whether a given sample comes from a normally distributed population. The P value greater than the significance level of 0.05 fails to reject the null hypothesis. It concludes that there is not enough evidence to suggest that the data does not follow the normal distribution. Visual inspection of a histogram, Q-Q plot, and P-P plot (probability-probability plot) was assessed.

Bivariate (correlation) analysis assessed the relationships between independent and dependent variables. Then, multiple linear regression analysis was used to establish the simple correlation matrices between different variables for investigating the strength relationships of the study variables in the analysis. In most variables, percentages and means were used to report the findings with a 95% confidence interval. Open-ended responses and focused group findings were undertaken by quantifying and coding the data to provide a thematic narrative explanation.

Appropriate and scientific care was taken to maintain the data quality before, during, and after data collection by preparing the proper data collection tools, pretesting the data collection tools, providing training for data collectors, and proper data entry practice. Data were cleaned on a daily basis during data collection practice, during data entry, and before analysis of its completeness and consistency.

Data analysis in a concurrent design consists of three phases. First, analyze the quantitative database in terms of statistical results. Second, analyze the qualitative database by coding the data and collapsing the codes into broad themes. Third comes the mixed-method data analysis. This is the analysis that consists of integrating the two databases. This integration consists of merging the results from both the qualitative and the quantitative findings.

Descriptive analysis was conducted to describe and summarise the data obtained from the samples used for this study. Reliability statistics for constructs, means and modes of each item, frequencies and percentage distributions, chi-square test of association, and correlations (Spearman rho) were used to portray the respondents’ responses.

All patient care-providing health facilities were included in this study, and the generation rate of healthcare waste and composition assessed the practice of segregation, collection, transportation, and disposal system was observed quantitatively using adopted and adapted structured questionnaires. To ensure representativeness, various levels of health facilities like hospitals, health centres, medium clinics, small clinics and surgical centres were considered from the town. All levels of health facilities are diagnosing, providing first aid services and treating patients accordingly.

The hospital and surgical centre found in the town provide advanced surgical service, inpatient service and food for the patients that other health facilities do not. The HCW generation rate was proportional to the number of patients who visited the health facilities and the type of service provided. The highest number of patients who visited the health facilities was in NEMMCSH; the service provided was diverse, and the waste generation rate was higher than that of other health facilities. About 272, 18, 15, 17, and 20 average patients visited the health facilities daily in NEMMCSH: government health centres, medium clinics, small clinics, and surgical centres. Paper and cardboard (141.65 kg), leftover food (81.71 kg), and contaminated gloves (42.96 kg) are the leading HCWs generated per day.

A total of 556 individual respondents from sampled health facilities were interviewed to complete the questionnaire. The total number of filled questionnaires was 540 (97.1) from individuals representing these 41 health facilities.

The principal investigator observed the availability of handwashing facilities near SHCW generation sites. 17(3.1%) of health facility workers had hand washing facilities near the health care waste generation and disposal site. Furthermore,10 (3.87%), 2 (2.1%), 2 (2.53%), 2 (2.1%), 1 (6.6%) of health facility workers had the facility of hand washing near the health care waste generation site in Nigist Eleni Mohamed Memorial Comprehensive Specialized Hospital (NEMMCSH), government health centres, medium clinics, small clinics, and surgical centre respectively. This finding was nearly the same as the study findings conducted in Myanmar; the availability of hand washing facilities near the solid health care waste generation was absent in all service areas [ 12 ]. The observational result was convergent with the response of facility workers’ response regarding the availabilities of hand washing facilities near to the solid health care waste generation sites.

The observational result was concurrent with the response of facility workers regarding the availability of hand-washing facilities near the solid health care waste generation sites.

The availability of personal protective equipment (PPE) was checked in this study. Three hundred ninety-two (72.6%) of the respondents agree on the facility’s availability of one or more personal protective equipment (PPE). The availability of PPEs in different levels of health facilities shows 392 (72.6%), 212 (82.2%), 56 (58.9%), 52 (65.8%), 60 (65.2%), 12 (75%) health facility workers in NEMMCSH, government health centres, medium clinics, small clinics, and surgical centres respectively agree to the presence of personal protective equipment in their department. The analysis further shows that the availability of masks for healthcare workers was above the mean in NEMMCSH and surgical centres.

Focus group participants indicated that health facilities did not volunteer to supply Personal protective equipment (PPEs) for the cleaning staff.

“We cannot purchase PPE by ourselves because of the salary paid for the cleaning staff.”

Cost inflation and the high cost of purchasing PPEs like gloves and boots are complained about by all (41) health facility owners.

“the reason for the absence of some of the PPEs like boots, goggles, and shortage of disposable gloves are owing to cost inflation from time to time and sometimes absent from the market is the reason why we do not supply PPE to our workers.”

Using essential personal protective equipment (PPEs) based on the risk (if the risk is a splash of blood or body fluid, use a mask and goggles; if the risk is on foot, use appropriate shoes) is recommended by the World Health Organization [ 13 ]. The mean availability of gloves in health facilities was 343 (63.5% (95% CI: 59.3–67.4). Private health institutions are better at providing gloves for their workers, 67.1%, 72.8%, and 62.5% in medium clinics, small clinics, and surgical centres, respectively, which is above the mean.

Research participants agree that.

‘‘ there is a shortage of gloves to give service in Nigist Eleni Mohamed Memorial Comprehensive Specialized Hospital (NEMMCSH) and government health centres .’’

Masks are the most available personal protective equipment for health facility workers compared to others. 65.4%, 55.6%, and 38% of the staff are available with gloves, plastic aprons and boots, respectively.

The mean availability of masks, heavy-duty gloves, boots, and aprons was 71.1%, 65.4%, 38%, and 44.4% in the study health facilities. Health facility workers were asked about the availability of different personal protective equipment, and 38% of the respondents agreed with the presence of boots in the facility. Still, the qualitative observational findings of this study show that all health facility workers have no shoes or footwear during solid health care waste management practice.

SHCW segregation practice was checked by observing the availability of SHCW collection bins in each patient care room. Only 4 (1.7%) of the room’s SHCW bins are collected segregated (non-infectious wastes segregated in black bins and infectious wastes segregated in yellow bins) based on the World Health Organization standard. Colour-coded waste bins, black for non-infectious and yellow for infectious wastes, were available in 23 (9.6%) rooms. 90% of the sharp containers were reusable, and 100% of the waste storage bins were plastic buckets that were easily cleanable. Only 6.7% of the waste bins were pedal operated and adequately covered, and the rest were fully opened, or a tiny hole was prepared on the container’s cover. All of the healthcare waste disposal bins in each health facility and at all service areas were away from the arm’s reach distance of the waste generation places, and this is contrary to World Health Organization SHCWM guidelines [ 13 ]. The observation result reveals that the reason for the above result was that medication trolleys were not used during medication or while healthcare providers provided any health services to patients.

Most medical wastes are incinerated. Burning solid and regulated medical waste generated by health care creates many problems. Medical waste incinerators emit toxic air pollutants and ash residues that are the primary source of environmental dioxins. Public concerns about incinerator emissions and the creation of federal regulations for medical waste incinerators are causing many healthcare facilities to rethink their choices in medical waste treatment. Health Care Without Harm [ 14 ], states that non-incineration treatment technologies are a growing and developing field. The U.S. National Academy of Science 2000 argued that the emission of pollutants during incineration is a potential risk to human health, and living or working near an incineration facility can have social, economic, and psychological effects [ 15 ].

The incineration of solid healthcare waste technology has been accepted and adopted as an effective method in Ethiopia. Incineration of healthcare waste can produce secondary waste and pollutants if the treatment facilities are not appropriately constructed, designed, and operated. It can be one of the significant sources of toxic substances, such as polychlorinated dibenzo-dioxins/dibenzofurans (PCDD/ PCDF), polyvinyl chloride (PVC), hexachlorobenzenes and polychlorinated biphenyls, and dioxins and furans that are known as hazardous pollutants. These pollutants may have undesirable environmental impacts on human and animal health, such as liver failure and cancer [ 15 , 16 ].

All government health facilities (4 in number) used incineration to dispose of solid waste. 88.4% and 100% of the wastes are incinerated in WUNEMMCSH and government health centres. This finding contradicts the study findings in the United States of America and Malaysia, in which 49–60% and 59–60 were incinerated, respectively, and the rest were treated using other technologies [ 15 , 16 ].

World Health Organization (2014:45) highlighted those critical elements of the appropriate operation of incinerators include effective waste reduction and waste segregation, placing incinerators away from populated areas, satisfactory engineered design, construction following appropriate dimensional plans, proper operation, periodic maintenance, and staff training and management are mandatory.

Solid waste collection times should be fixed and appropriate to the quantity of waste produced in each area of the health care facility. General waste should not be collected simultaneously or in the same trolley as infectious or hazardous wastes. The collection should be done daily for most wastes, with collection timed to match the pattern of waste generation during the day [ 13 ].

SHCW segregation practices were observed for 240 rooms in 41 health facilities that provide health services in the town. In government health centres, medium clinics, small clinics, and surgical centres, SHCW segregation practice was not based on the World Health Organization standard. All types of solid waste were collected in a single container near the generation area, and there were no colour-coded SHCW storage dust bins. Still, in NEMMCSH, in most of the service areas, colour-coded waste bins are available, and the segregation practice was not based on the standard. Only 3 (10%) of the dust bins collected the appropriate wastes according to the World Health Organization standard, and the rest were mixed with infectious and non-infectious SHCW.

Table 1 below shows health facility managers were asked about healthcare waste segregation practices, and 9 (22%) of the facility leaders responded that there is an appropriate solid healthcare waste segregation practice in their health facilities. Still, during observation, only 4 (1.7%) of the rooms in two (4.87%) of the facilities, SHCW bins collected the segregated wastes (non-infectious wastes segregated at the black bin and infectious wastes segregated at yellow bin) based on the world health organization standard. The findings of this study show there is a poor segregation practice, and all kinds of solid wastes are collected together.

In 40 (97.56%) health facilities, infectious wastes were collected daily from the waste generation areas to the final disposal points. During observation in one of the study health facilities, infectious wastes were not collected daily and left for days. Utility gloves, boots, and aprons are not available for cleaning staff to collect and transport solid healthcare wastes in all study health facilities. 29.26% of the facilities’ cleaning staff have a face mask, and 36.5% of the facilities remove waste bins from the service area when 3/4 full, and the rest were not removed or replaced with new ones. There is a separate container only in 2 health facilities for infectious and non-infectious waste segregation practice, and the rest were segregated and collected using single and non-colour coded containers.

At all of the facilities in the study area, SHCW was transported from the service areas to the disposal site were transported manually by carrying the collection container and there is no trolley for transportation. This finding was contrary to the study findings conducted in India, which show segregated waste from the generation site was being transported through the chute to the carts placed at various points on the hospital premises by skilled sanitary workers [ 17 ].

Only 2 out of 41 health facilities have temporary solid waste storage points at the facility. One of the temporary storage places was clean, and the other needed to be properly cleaned and unsightly. Two (100%) of the temporary storage areas are not fenced and have no restriction to an authorized person. Temporary storage areas are available only in two health facilities that are away from the service provision areas.

Observational findings revealed that pre-treatment of SHCW before disposal was not practised at all study health facilities. 95% of the facilities have no water supply for hand washing during and after solid healthcare waste generation, collection, and disposal.

The United States Agency estimated sharp injuries from medical wastes to health professionals and sanitary service personnel for toxic substances and disease registry. Most of the injuries are caused during the recapping of hypodermic needles before disposal into sharps containers [ 13 ]. Nearly half of the respondents, 245 (51.5%), are recapping needles after providing an injection to the patient. Recapping was more practised in NEMMCSH and surgical centres, which is 57.5% and 57.5%, respectively. In government health centres, medium clinics, and surgical centres, the recapping of used needles was practised below the mean, which is 47.9%, 48, and 43.8%, respectively. This finding was reasonable compared to the study findings of Doylo et al. [ 18 ] in western Ethiopia, where 91% of the health workers are recapping needles after injection [ 18 ]. The research finding shows that there is no significant association P-value of 0.82 between the training and recapping of needles after injection.

Focus group participants ’ response for appropriate SHCWMP regarding patients ’ and visitors ’ lack of knowledge on SHCW segregation practice

“The personal responsibilities of patients and visitors on solid HCW disposal should be explained to help appropriate safe waste management practice and maintain good hygiene .” “Providing waste management training and creating awareness are the two aspects of improving SHCW segregation practice.” “Training upgrades and creates awareness on hygiene for all workers.”

Sharp waste collection practices were observed in 240 rooms in the study health facilities, and 9.2% of the rooms used disposable sharp containers.

Sixty per cent (60%), 13.3%, 8.24%, and 15.71% of the sharps containers in NEMMCSH, government health centres, medium clinics, and small clinics, respectively, were using disposable sharps containers; sharps were disposed together with the sharps container, and surgical centre was using reusable sharp collection container. All disposable sharps containers in medium and small clinics used non-puncture-resistant or simple packaging carton boxes. 60% and 13.3% of the disposable sharps containers in NEMMCSH and the government health centre use purposefully manufactured disposable safety boxes.

Needle sticks injury reporting and occurrence

A total of 70 injuries were reported to the health facility manager in the last one year, and 44 of the injuries were reported by health professionals. The rest of the injuries were reported by supportive staff. These injuries were reported from 35 health facilities, and the remaining six health facilities did not report any cases of injury related to work; see Tables 2 and 3 below.

Accidents or incidents, including near misses, spillages, damaged containers, inappropriate segregation, and any incidents involving sharps, should be reported to the waste-management officer. Accidental contamination must be notified using a standard-format document. The cause of the accident or incident should be investigated by the waste-management officer (in case of waste) or another responsible officer, who should also take action to prevent a recurrence [ 13 ]. Two hundred seventy-one (50.2% (CI: 45.7–54.6) of the respondents agree that satisfactory procedures are available in case of an accident, while the remaining 269 (49.8%( CI: 45.4–54.3) of respondents do not agree on the availability of satisfactory procedures in case of an accident, see Table  4 below. The availability of satisfactory procedures in case of an accident is above the mean in medium clinics, which is 60.8%. 132(24.4%) of the staff are pricked by needle stick injury while providing health services. Nearly half of the respondents, 269 (49.8%), who have been exposed to needle stick injury do not get satisfactory procedures after being pricked by a needle, and those who have not been stung by a needle stick injury for the last year. 204 (37.8%) disagree with the presence of satisfactory procedures in the case of a needle stick injury. In NEMMCSH, 30.2% of the research participants were pricked by needle stick injury within one year of period, and 48.8% of those who were stung by needle stick injuries did not agree upon the presence of satisfactory procedures in case of needle stick injuries in the study hospital. 17.9% and 49.5%, 24.1% and 60.8%, 7.6% and 50% of the respondents are pricked by needle sticks, and they disagree on the availability of satisfactory procedures in case of accidents, respectively, in government health centres, medium clinics, small clinics, and surgical centre respectively.

One hundred seventy-seven (32.7% (CI:29.1–37) respondents were exposed to needle stick injury while working in the current health facilities. One hundred three (58.1%) and 26 (32.9%) needle stick injuries were reported from WUNEMMCSH and medium clinics, which is above the mean. One hundred thirty-two(24.7% (95%CI:20.7–28.1) of the respondents are exposed to needle stick injury within one year of the period. Seventy-eight(30.2%), 17 (17.9%), 19 (24.1%), 15 (16.3%), 3 (18.8%) of the staff are injured by needle sticks from NEMMCSH, government health centres, medium clinics, small clinics, and surgical centre staffs respectively within one year of service.

The mean availabilities of satisfactory procedures in case of accidents were 321 (59.4% (CI:55.4–63.7). Out of this, 13.7% of the staff is injured by needle sticks within one year before the survey. Except in NEMMCSH, the mean availabilities of satisfactory procedures were above the mean, which is 50%, 60%, 77.2%, 66.3%, and 81.3% in NEMMCSH, government health centres, medium clinics, small clinics, and surgical centres respectively.

Table 5 below shows that Hepatitis B, COVID-19, and tetanus toxoid vaccinations are the responses of the research participants to an open-ended question on which vaccine they took. The finding shows that 220 (40.8%) of the respondents were vaccinated to prevent themselves from health facility-acquired infection. One hundred fifty-six (70.9%) of the respondents are vaccinated to avoid themselves from Hep B infection. Fifty-nine (26%0.8) of the respondents were vaccinated to protect themselves from two diseases that are Hep B and COVID-19.

Appropriate health care waste management practice was assessed by using 12 questions: availability of colour-coded waste bins, foot-operated dust bins, elbow or foot-operated hand washing basin, personal protective equipment, training, role and responsibility of the worker, the presence of satisfactory procedures in case of an accident, incinerator, vaccination, guideline, onsite treatment, and the availability of poster. The mean of appropriate healthcare waste management practice was 55.58%. The mean of solid health care waste management practice based on the level of health facilities was summed and divided into 12 variables to get each health facility’s level of waste management practice. 64.9%, 45.58%, 49%, 46.9%, and 51.8% are the mean appropriate health care waste management practices in NEMMCSH, government health centres, medium clinics, small clinics, and surgical centres, respectively. In NEMMCSH, the practice of solid healthcare waste management shows above the mean, and the rest was below the mean of solid healthcare waste management practice.

Healthcare waste treatment and disposal practice

Solid waste treatment before disposal was not practised at all study health facilities. There is an incineration practice at all of the study health facilities, and the World Health Organization 2014 recommended three types of incineration practice for solid health care waste management: dual-chamber starved-air incinerators, multiple chamber incinerators, and rotary kilns incinerators. Single-chamber, drum, and brick incinerators do not meet the best available technique requirements of the Stockholm Convention guidelines [ 13 ]. The findings of this study show that none of the incinerators found in the study health facilities meet the minimum standards of solid healthcare waste incineration practice, and they need an air inlet to facilitate combustion. Eleven (26.82%) of the health facilities have an ash pit to dispose of burned SHCW; the majority, 30 (73.17%), dispose of the incinerated ash and burned needles in the municipal waste disposal site. In one out of 11 health facilities with an ash pit, one of the incinerators was built on the ash pit, and the incinerated ashes were disposed of in the ash pit directly. Pre-treatment of SHCW before disposal was not practised at all health facilities; see Table  6 below.

All government health facilities use incineration to dispose of solid waste. 88.4% and 100% of the solid wastes are incinerated in WUNEMMCS Hospital and government health centres, respectively. This finding was not similar to the other studies because other technologies like autoclave microwave and incineration were used for 59–60% of the waste [ 15 ]. Forty-one (100%) of the study facilities were using incinerators, and only 5 (12.19%) of the incinerators were constructed by using brick and more or less promising than others for incinerating the generated solid wastes without considering the emitting gases into the atmosphere and the residue chemicals and minerals in the ashes.

Research participants’ understanding of the environmental friendliness of health care waste management practice was assessed, and the result shows that more than half, 312(57%) of the research participants do not agree on the environmental friendliness of the waste disposal practices in the health facilities. The most disagreement regarding environmental friendliness was observed in NEMMCSH; 100 (38.8%) of the participants only agreed the practice was environmentally friendly of the service. Forty-four (46.3%), 37 (46.8%), 40 (43.5%), and 7 (43.8%) of the participants agree on the environmental friendliness of healthcare waste management practice in government health centres, medium clinics, small clinics, and surgical centres, respectively.

One hundred twenty-five (48.4%) and 39(42.4%) staff are trained in solid health care waste management practice in NEMMCSH and small clinic staff, respectively; this result shows above the mean. Twenty-seven (28.4%), 30 (38%), and 4 (25%) of the staff are trained in health care waste management practice in Government health centres, medium clinics, and surgical centres, respectively. The training has been significantly associated with needle stick injury, and the more trained staff are, the less exposed to needle stick injury. One hundred ninety-six (36.4%) of the participants answered yes to the question about the availability of trainers in the institution. 43.8% of the NEMMCSH staff agreed on the availability of trainers on solid health care waste management, which is above the mean, and 26.3%, 31.6%, 31.5%, and 25% for the government health centres, medium clinics, small clinics, and surgical centre respectively, which is below the mean.

Trained health professionals are more compliant with SHCWM standards, and the self-reported study findings of this study show that 41.7% (95%CI:37.7–46) of the research participants are trained in health care waste management practice. This finding was higher compared to the study findings of Sahiledengle in 2019 in the southeast of Ethiopia, shows 13.0% of healthcare workers received training related to HCWM in the past one year preceding the study period and significantly lower when compared to the study findings in Egypt which is 71% of the study participants were trained on SHCWM [ 8 , 19 , 20 ].

Three out of four government health facility leaders, 17 (45.94%) of private health facility leaders/owners of the clinic and 141 FGD participants complain about the absence of some PPEs like boots and aprons to protect themselves from infectious agents.

‘ ‘Masks, disposable gloves, and changing gowns are a critical shortage at all health facilities.’’

Cleaners in private health facilities are more exposed to infectious agents because of the absence of personal protective equipment. Except for the cleaning staff working in the private surgical centre, all cleaning staff 40 (97.56) of the health facilities complain about the absence of changing gowns and the fact that there are no boots in the facilities.

Cost inflation and the high cost of purchasing PPEs like gloves and boots are complained by all of (41) the health facility owners and the reason for the absence of some of the PPEs like boots, goggles, and shortage of disposable gloves. Sometimes, absence from the market is the reason why we do not supply PPE to our workers.

Thirty-four (82.92%) of the facility leaders are forwarded, and there is a high expense and even unavailability of some of the PPEs, which are the reasons for not providing PPEs for the workers.

‘‘Medical equipment and consumables importers and whole sellers are selective for importing health supplies, and because of a small number of importers in the country and specifically, in the locality, we can’t get materials used for health care waste management practice even disposable gloves. ’’

One of the facility leaders from a private clinic forwarded that before the advent of COVID-19 -19) personal protective equipment was more or less chip-and-get without difficulty. Still, after the advent of the first Japanese COVID-19 patient in Ethiopia, people outside the health facilities collect PPEs like gloves and masks and storing privately in their homes.

‘‘PPEs were getting expensive and unavailable in the market. Incinerator construction materials cost inflation, and the ownership of the facility building are other problems for private health facilities to construct standard incinerators.’’

For all of the focus group discussion participants except in NEMMCSH and two private health facilities, covered and foot-operated dust bins were absent or in a critical shortage compared to the needed ones.

‘‘ Waste bins are open and not colour-coded. The practice attracts flies and other insects. Empty waste bins are replaced without cleaning and disinfecting by using chlorine solution.’’ “HCW containers are not colour-coded, but we are trying to label infectious and non-infectious in Amharic languages.”

Another issue raised during focus group discussions is incineration is not the final disposal method. It needs additional disposal sites, lacks technology, is costly to construct a brick incinerator, lacks knowledge for health facility workers, shortage of man powers /cleaners, absence of environmental health professionals in health centres and all private clinics, and continues exposure to the staff for needle stick injury, foully smell, human scavengers, unsightly, fire hazard, and lack of water supply in the town are the major teams that FGD participants raise and forwarded the above issue as a problem to improve SHCWMP.

Focus group participants, during the discussion, raised issues that could be more comfortable managing SHCWs properly in their institution. Two of the 37 private health facilities are working in their own compound, and the remaining 35 are rented; because of this, they have difficulty constructing incinerators and ash removal pits and are not confident about investing in SHCWM systems. Staff negligence and involuntary abiding by the rules of the facilities were raised by four of the government health facilities, and it was difficult to punish those who violated the healthcare waste management rules because the health facility leaders were not giving appropriate attention to the problem.

Focus group participants forwarded recommendations on which interventions can improve the management of SHCW, and recommendations are summarised as follows:

“PPE should be available in quality and quantity for all health facility workers who have direct contact with SHCW.” “Scientific-based waste management technologies should be availed for health facilities.” “Continuous induction HCW management training should be provided to the workers. Law enforcement should be strengthened.” “Communal HCW management sites should be availed, especially for private health facilities.” “HCWM committee should be strengthened.” “Non-infectious wastes should be collected communally and transported to the municipal SHCW disposal places.” “Leaders should be knowledgeable on the SHCWM system and supervise the practice continuously.” “Patient and client should be oriented daily about HCW segregation practice.” “Regulatory bodies should supervise the health facilities before commencing and periodically between services .”

The above are the themes that FGD participants discussed and forwarded for the future improvements of SHAWMP in the study areas.

Lack of water supply in the town

Other issues raised during FGDs were health facilities’ lack of water supply. World Health Organization (2014: 89) highlights that water supply for the appropriate waste management system should be mandatory at any time in all health service delivery points.

Thirty-nine (95.12%) of the health facilities complain about the absence of water supply to improve HCW management practices and infection prevention and control practices in the facilities.

“We get water once per week, and most of the time, the water is available at night, and if we are not fetching as scheduled, we can’t get water the whole week”.

In this research, only those who have direct contact have participated in this study, and 434 (80.4%) of the respondents agree they have roles and responsibilities for appropriate solid health care waste management practice. The rest, 19.6%, do not agree with their commitment to manage health care wastes properly, even though they are responsible. Health facility workers in NEMMCSH and medium clinics know their responsibilities better than others, and their results show above the mean. 84.5%, 74.5%, 81%, 73.9% and 75% in NEMMCSH, Government health centres, medium clinics, small clinics, and surgical centres, respectively.

Establishing a policy and a legal framework, training personnel, and raising public awareness are essential elements of successful healthcare waste management. A policy can be viewed as a blueprint that drives decision-making at a political level and should mobilize government effort and resources to create the conditions to make changes in healthcare facilities. Three hundred and seventy-four (69.3%) of the respondents agree with the presence of any solid healthcare waste management policy in Ethiopia. The more knowledge above the mean (72.9%) on the presence of the policy is reported from NEMMCSH.

Self-reported level of knowledge on what to do in case of an accident revealed that 438 (81.1% CI: 77.6–84.3%) of the respondents knew what to do in case of an accident. Government health centre staff and medium clinic staff’s knowledge about what to do in case of an accident was above the mean (88.4% and 82.3%), respectively, and the rest were below the mean. The action performed after an occupational accident revealed that 56 (35.7%) of the respondents did nothing after any exposure to an accident. Out of 56 respondents who have done nothing after exposure, 47 (83.92%) of the respondents answered yes to their knowledge about what to do in case of an accident. Out of 157 respondents who have been exposed to occupational accidents, only 59 (37.6%) of the respondents performed the appropriate measures, 18 (11.5%), 9 (5.7%), 26 (16.6%), 6 (3.8%) of the respondents are taking prophylaxis, linked to the incident officer, consult the available doctors near to the department, and test the status of the patient (source of infection) respectively and the rest were not performing the scientific measures, that is only practising one of the following practices washing the affected part, squeezing the affected part to remove blood, cleaning the affected part with alcohol.

Health facility workers’ understanding of solid health care waste management practices was assessed by asking whether the current SHCWM practice needs improvement. Four hundred forty-nine (83.1%) health facility workers are unsatisfied with the current solid waste management practice at the different health facility levels, and they recommend changing it to a scientific one. 82.6%, 87.4%, 89.9%, 75%, and 81.3% of the respondents are uncomfortable or need to improve solid health care waste management practices in NEMMCSH, government health centres, medium clinics, small clinics, and surgical centres, respectively.

Lack of safety box, lack of colour-coded waste bins, lack of training, and no problems are the responses to the question problems encountered in managing SHCWMP. Two Hundred and Fifty (46.92%) and 232 (42.96%) of the respondents recommend the availability of safety boxes and training, respectively.

Four or 9.8% of the facilities have infection prevention and control (IPC) teams in the study health facilities. This finding differed from the study in Pakistan, where thirty per cent (30%) of the study hospitals had HCWM or infection control teams [ 21 ]. This study’s findings were similar to those conducted in Pakistan by Khan et al. [ 21 ], which confirmed that the teams were almost absent at the secondary and primary healthcare levels [ 20 ].

The availability of health care waste management policy report reveals that 69.3% (95% CI: 65.4–73) of the staff are aware of the presence of solid health care waste management policy in the institution. Availability of health care waste management policy was 188 (72.9%), 66 (69.5%), 53 (677.1%), 57 (62%), 10 (62.5%) in NEMMCSH, Government health centres, medium clinics, small clinics, and surgical centre respectively. Healthcare waste management policy availability was above the mean in NEMMCSH and government health centres; see Table  6 below.

Open-ended responses on the SHCWM practice of health facility workers were collected using the prepared interview guide, and the responses were analyzed using thematic analysis. All the answered questions were tallied on the paper and exported to Excel software for thematic analysis.

The study participants recommend.

“appropriate segregation practice at the point of generation” "health facility must avail all the necessary supplies that used for SHCWMP, punishment for those violating the rule of SHCWMP",

“waste management technologies should be included in solid waste management guidelines, and enforcement should be strengthened.”

The availability of written national or adopted/adapted SHCWM policies was observed at all study health facilities. Twenty eight (11.66%) of the rooms have either a poster or a written document of the national policy document. However, all staff working in the observed rooms have yet to see the inside content of the policy. The presence of the policy alone cannot bring change to SHCWMP. This finding shows that the presence of policy in the institution was reasonable compared to the study findings in Menelik II hospital in Addis Ababa, showing that HCWM regulations and any applicable facility-based policy and strategy were not found [ 22 ]. The findings of this study were less compared to the study findings in Pakistan; 41% of the health facilities had the policy document or internal rules for the HCWM [ 21 ].

Focus group participants have forwarded recommendations on which interventions can improve the management of SHCW, and recommendations are summarised as follows.

‘‘Supplies should be available in quality and quantity for all health facility workers with direct contact with SHCW. Scientific-based waste management technologies should be available for health facilities. Continues and induction health care waste management training should be provided to the workers. Law enforcement should be strengthened. Community healthcare waste management sites should be available, especially for private health facilities. HCWM committee should be strengthened. Non-infectious wastes should be collected communally and transported to the municipal SHCW disposal places. Leaders should be knowledgeable about the SHCWM system and supervise the practice continuously. Patients and clients should be oriented daily about health care waste segregation practices. Regulatory bodies should supervise the health facilities before commencing and periodically in between the service are the themes those FGD participants discussed and forward for the future improvements of SHCWMP in the study areas.’’

The availability of PPEs in different levels of health facilities shows 392 (72.6%), 212 (82.2%), 56 (58.9%), 52 (65.8%), 60 (65.2%), 12 (75%) health facility workers in NEMMCSH, government health centres, medium clinics, small clinics, and surgical centres respectively agree to the presence of personal protective equipment in their department. The availability of PPEs in this study was nearly two-fold when compared to the study findings in Myanmar, where 37.6% of the staff have PPEs [ 12 ].

The mean availability of masks, heavy-duty gloves, boots, and aprons was 71.1%, 65.4%, 38%, and 44.4% in the study health facilities. This finding shows masks are less available in the study health facilities compared to other studies. The availability of utility gloves, boots, and plastic aprons is good in this study compared to the study conducted by Banstola, D in Pokhara Sub-Metropolitan City [ 23 ].

The findings of this study show there is a poor segregation practice, and all kinds of solid wastes were collected together. This finding was similar to the study findings conducted in Addis Ababa, Ethiopia, by Debere et al. [ 24 ] and contrary to the study findings conducted in Nepal and India, which shows 50% and 65–75% of the surveyed health facilities were practising proper waste segregation systems at the point of generation without mixing general wastes with hazardous wastes respectively [ 9 , 17 ].

Ninety percent of private health facilities collect and transport SHCW generated in every service area and transport it to the disposal place by the collection container (no separate container to collect and transport the waste to the final disposal site). This finding was similar to the study findings of Debre Markos’s town [ 25 ]. At all of the facilities in the study area, SHCW was transported from the service areas to the disposal site manually by carrying the collection container, and there was no trolley for transportation. This finding was contrary to the study findings conducted in India, which show segregated waste from the generation site was being transported through the chute to the carts placed at various points on the hospital premises by skilled sanitary workers [ 17 ].

Observational findings revealed that pre-treatment of SHCW before disposal was not practised at all study health facilities. This study was contrary to the findings of Pullishery et al. [ 26 ], conducted in Mangalore, India, which depicted pre-treatment of the waste in 46% of the hospitals [ 26 ]. 95% of the facilities have no water supply for handwashing during and after solid healthcare waste generation, collection, and disposal. This finding was contrary to the study findings in Pakistan hospitals, which show all health facilities have an adequate water supply near the health care waste management sites [ 27 ].

Questionnaire data collection tools show that 129 (23.8%) of the staff needle stick injuries have occurred on health facility workers within one year of the period before the data collection. This finding was slightly smaller than the study findings of Deress et al. [ 25 ] in Debre Markos town, North East Ethiopia, where 30.9% of the workers had been exposed to needle stick injury one year prior to the study [ 25 ]. Reported and registered needle stick injuries in health facilities are less reported, and only 70 (54.2%) of the injuries are reported to the health facilities. This finding shows an underestimation of the risk and the problem, which was supported by the study conducted in Menilik II hospitals in Addis Ababa [ 22 ]. 50%, 33.4%, 48%, 52%, and 62.5% of needle stick injuries were not reported in NEMMCSH, Government health centres, medium clinics, small clinics, and surgical centres, respectively, to the health facility manager.

Nearly 1/3 (177 or 32.7%) of the staff are exposed to needle stick injuries. Needle stick injuries in health facilities are less reported, and only 73 (41.24%) of the injuries are reported to the health facilities within 12 months of the data collection. This finding is slightly higher than the study finding of Deress et al. [ 25 ] in Debere Markos, Ethiopia, in which 23.3% of the study participants had encountered needle stick/sharps injuries preceding 12 months of the data collection period [ 25 ].

Seventy-three injuries were reported to the health facility manager in the last one year, 44 of the injuries were reported by health professionals, and the rest were reported by supportive staff. These injuries were reported from 35(85.3%) health facilities; the remaining six have no report. These study findings were better than the findings of Khan et al. [ 21 ], in which one-third of the facilities had a reporting system for an incident, and almost the same percentage of the facilities had post-exposure procedures in both public and private sectors [ 21 ].

Within one year of the study period, 129 (23.88%) needle stick injuries occurred. However, needle stick injuries in health facilities are less reported, and only 70 (39.5%) of the injuries are reported to the health facilities. These findings were reasonable compared to the study findings of the southwest region of Cameroon, in which 50.9% (110/216) of all participants had at least one occupational exposure [ 28 , 29 ]. This result report shows a very high exposure to needle stick injury compared to the study findings in Brazil, which shows 6.1% of the research participants were injured [ 27 ].

The finding shows that 220 (40.8%) of the respondents were vaccinated to prevent themselves from health facility-acquired infection. One Hundred Fifty-six (70.9%) of the respondents are vaccinated in order to avoid themselves from Hep B infection. Fifty-nine (26%0.8) of the respondents were vaccinated to protect themselves from two diseases that are Hep B and COVID-19. This finding was nearly the same as the study findings of Deress et al. [ 7 ],in Ethiopia, 30.7% were vaccinated, and very low compared to the study findings of Qadir et al. [ 30 ] in Pakistan and Saha & Bhattacharjya India which is 66.67% and 66.17% respectively [ 25 , 30 , 31 ].

The incineration of solid healthcare waste technology has been accepted and adopted as an effective method in Ethiopia. These pollutants may have undesirable environmental impacts on human and animal health, such as liver failure and cancer [ 15 , 16 ]. All government health facilities use incineration to dispose of solid waste. 88.4% and 100% of the wastes are incinerated in WUNEMMCSH and government health centres, respectively. This finding contradicts the study findings in the United States of America and Malaysia, which are 49–60% and 59–60 are incinerated, respectively, and the rest are treated using other technologies [ 15 , 16 ].

All study health facilities used a brick or barrel type of incinerator. The incinerators found in the study health facilities need to meet the minimum standards of solid health care waste incineration practice. These findings were similar to the study findings of Nepal and Pakistan [ 32 ]. The health care waste treatment system in health facilities was found to be very unsystematic and unscientific, which cannot guarantee that there is no risk to the environment and public health, as well as safety for personnel involved in health care waste treatment. Most incinerators are not properly operated and maintained, resulting in poor performance.

All government health facilities use incineration to dispose of solid waste. All the generated sharp wastes are incinerated using brick or barrel incinerators, as shown in Fig.  1 above. This finding was consistent with the findings of Veilla and Samwel [ 33 ], who depicted that sharp waste generation is the same as sharps waste incinerated [ 33 ]. All brick incinerators were constructed without appropriate air inlets to facilitate combustion except in NEMMCSH, which is built at a 4-m height. These findings were similar to the findings of Tadese and Kumie at Addis Ababa [ 34 ].

Barrel and brick incinerators used in private clinic

Strengths and limitations

This is a mixed-method study; both qualitative and quantitative study design, data collection and analysis techniques were used to understand the problem better. The setting for this study was one town, which is found in the southern part of the country. It only represents some of the country’s health facilities, and it is difficult to generalize the findings to other hospitals and health centres. Another limitation of this study was that private drug stores and private pharmacies were not incorporated.

Conclusions

In the study, health facilities’ foot-operated solid waste dust bins are not available for healthcare workers and patients to dispose of the generated wastes. Health facility managers in government and private health institutions should pay more attention to the availability of colour-coded dust bins. Most containers are opened, and insects and rodents can access them anytime. Some of them are even closed (not foot-operated), leading to contamination of hands when trying to open them.

Healthcare waste management training is mandatory for appropriate healthcare waste disposal. Healthcare-associated exposure should be appropriately managed, and infection prevention and control training should be provided to all staff working in the health facilities.

Availability of data and materials

The authors declare that data for this work are available upon request to the first author.

Chartier, Y et al. Safe management of wastes from health-care activities. 2nd ed. WHO; 2014.

Tesfahun E, et al. Developing models for the prediction of hospital healthcare waste generation rate. Waste Manag Res. 2014;34(1):75–80.

Manzoor J, Sharma M. Impact of Biomedical Waste on Environment and Human Health. Environmental Claims Journal. 2019;31(4):311–34.

Article   Google Scholar  

Yves C, Jorge E, Ute P, Annette P, et al. Safe management of wastes from health-care activities. WHO 2nd ed. 2014.

OSHA. Occupational Safety and Health Administration, Guidelines for Healthcare Waste Management. 2023.

Godfrey L, Ahmed M, et al. Solid waste management in Africa: governance failure or development opportunity?. Intech open. 2019.

Deress T, Jemal M, Girma M, Adane K. Knowledge, attitude, and practice of waste handlers about medical waste management in Debre Markos town healthcare facilities, northwest Ethiopia. BMC Res Notes. 2019;12(1):146.

Article   PubMed   PubMed Central   Google Scholar  

Sahiledengle B. Self-reported healthcare waste segregation practice and its correlate among healthcare workers in hospitals of Southeast Ethiopia. BMC Health Serv Res. 2019;19(1):591.

Debalkie D, Kume A. Healthcare Waste Management: The Current Issue in Menellik II Referral Hospital, Ethiopia. Curr World Environ. 2017;12(1):42–52.

Debere MK, Gelaye KA, Alamdo AG, Trifa ZM. Assessment of the health care waste generation rates and its management system in hospitals of Addis Ababa, Ethiopia, 2011. BMC Public Health. 2013;13(28).

Creswell JW. Research design qualitative, quantitative, & mixed method approach. 4th ed. SAGE Publications, Inc.; 2014.

Win EM, Saw YM, Oo KL, Than TM, Cho SM, Kariya T, et al. Healthcare waste management at primary health centres in Mon State, Myanmar: the comparisons between hospital and non-hospital type primary health centres. Nagoya J Med Sci. 2019;81(1):81–91.

PubMed   PubMed Central   Google Scholar  

WHO. Safe management of wastes from health-care activities. 2nd ed. editor Chartier, Y et al. 2014. 

Richard B, Ben A, Kristian S. Health care without harm climate-smart health care series green paper number one. 2019.

Khadem Ghasemi M, Mohd YR. Advantages and Disadvantages of Healthcare Waste Treatment and Disposal Alternatives: Malaysian Scenario. Pol J Environ Stud. 2016;25(1):17–25.

Mohseni-Bandpei A, Majlesi M, Rafiee M, Nojavan S, Nowrouz P, Zolfagharpour H. Polycyclic aromatic hydrocarbons (PAHs) formation during the fast pyrolysis of hazardous health-care waste. Chemosphere. 2019;227:277–88.

Article   PubMed   CAS   Google Scholar  

Pandey A, Ahuja S, Madan M, Asthana AK. Bio-Medical Waste Managment in a Tertiary Care Hospital: An Overview. J Clin Diagn Res. 2016;10(11):DC01-DC3.

Doylo T, Alemayehu T, Baraki N. Knowledge and Practice of Health Workers about Healthcare Waste Management in Public Health Facilities in Eastern Ethiopia. J Community Health. 2019;44(2):284–91.

Article   PubMed   Google Scholar  

Hosny G, Samir S, Sharkawy R. An intervention significantly improve medical waste handling and management: A consequence of raising knowledge and practical skills of health care workers. Int J Health Sci.2018;12(4).

Khan EA, Sabeeh SM, Chaudhry MA, Yaqoob A, Kumar R. et al. Health care waste management in Pakistan: A situational analisis and way forward. Pak J Public Health. 2016;6(3).

Khan BA, Cheng L, Khan AA, Ahmed H. Healthcare waste management in Asian developing countries: A mini review. Waste Manag Res. 2019;37(9):863–75.

Debalkie D, Kumie A. Healthcare Waste Management: The Current Issue in Menellik II Referral Hospital. Ethiopia Current World Environment. 2017;12(1):42–52.

Banstola D, Banstola R, Nepal D, Baral P. Management of hospital solid wastes: A study in Pokhara sub metropolitan city. J Institute Med. 2017;31(1):68–74.

Debere MK, Gelaye KA, Alamdo AG, Trifa, ZM. Assessment of the HCW generation rates and its management system in hospitals of Addis Ababa, Ethiopia. BMC Public Health. 2014;13(28):1–9.

Deress T, Hassen F, Adane K, Tsegaye A. Assessment of Knowledge, Attitude, and Practice about Biomedical Waste Management and Associated Factors among the Healthcare Professionals at Debre Markos Town Healthcare Facilities. Northwest Ethiopia J Environ Public Health. 2018;2018:7672981.

PubMed   Google Scholar  

Pullishery F, Panchmal GS, Siddique S, Abraham A. Awareness, knowledge, and practices on bio-medical waste management among health care professionals in Mangalore- A cross sectional study. Integr Med. 2016;3(1):29–35.

Google Scholar  

Ream PS, Tipple AF, Salgado TA, Souza AC, Souza SM, Galdino-Junior H, et al. Hospital housekeepers: Victims of ineffective hospital waste management. Arch Environ Occup Health. 2016;71(5):273–80.

Ngwa CH, Ngoh EA, Cumber SN. Assessment of the knowledge, attitude and practice of health care workers in Fako division on post exposure prophylaxis to blood borne viruses: a hospital based cross-sectional study. Pan Afr Med J. 2018;31.

Health care waste managemnt in pakistan. a situation analysis and way forward. Pakistan Journal of Public Health. 2016;6(3):35–45.

Qadir DM, Murad DR, Faraz DN. Hospital Waste Management; Tertiary Care Hospitals. The Professional Medical Journal. 2016;23(07):802–6.

Saha A, Bhattacharjya H. Health-Care Waste Management in Public Sector of Tripura, North-East India: An Observational Study. Indian J Community Med. 2019;44(4):368–72.

Pullishery F, Panchmal G, Siddique S, Abraham A. Awareness, knowledge, and practices on bio-medical waste management among health care professionals in Mangalore- A cross sectional study. Integr Med. 2016;3(1):29–35.

Veilla EM, Samwel VM. Assessment of sharps waste management practices in a referral hospital. Afr J Environ Sci Technol. 2016;10(3):86–95.

Tadesse ML, Kumie A. Healthcare waste generation and management practice in government health centers of Addis Ababa. Ethiopia BMC Public Health. 2014;14:1221.

Download references

Acknowledgements

The authors are grateful to the health facility leaders and ethical committees of the hospitals for their permission. The authors acknowledge the cooperation of the health facility workers who participated in this study.

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Author information

Authors and affiliations.

Wachemo University College of Medicine and public health, Hossana, Ethiopia

Yeshanew Ayele Tiruneh

Department of Public Health, University of South Africa, College of Human Science, Pretoria, South Africa

L. M. Modiba & S. M. Zuma

You can also search for this author in PubMed   Google Scholar

Contributions

Dr. Yeshanew Ayele Tiruneh is a researcher of this study; the principal investigator does all the proposal preparation, methodology, data collection, result and discussion, and manuscript writing. Professor LM Modiba and Dr. SM Zuma are supervisors for this study. They participated in the topic selection and modification to the final manuscript preparation by commenting on and correcting the study. Finally, the three authors read and approved the final version of the manuscript and agreed to submit the manuscript for publication.

Corresponding author

Correspondence to Yeshanew Ayele Tiruneh .

Ethics declarations

Ethics approval and consent to participate.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note.

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

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/ .

Reprints and permissions

About this article

Cite this article.

Tiruneh, Y.A., Modiba, L.M. & Zuma, S.M. Solid health care waste management practice in Ethiopia, a convergent mixed method study. BMC Health Serv Res 24 , 985 (2024). https://doi.org/10.1186/s12913-024-11444-8

Download citation

Received : 05 March 2023

Accepted : 14 August 2024

Published : 26 August 2024

DOI : https://doi.org/10.1186/s12913-024-11444-8

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

• Health care waste
• Waste management
• Private health facilities

BMC Health Services Research

ISSN: 1472-6963

• General enquiries: [email protected]