Relative Importance of Sustainable Development Goals by Q-Sort Evaluation

Abstract

The 17 Sustainable Development Goals offer a comprehensive framework for extensive development actions. The purpose of this study is to explore the patterns of perception of the relative importance of the goals. Although the interrelations between the diversified goals provide a comprehensive approach for decision-makers, the patterns may support effective strategies in the field. The study used a voluntary online survey among 123 business students by the Q-sort ranking method. The analysis identified three characteristic patterns for the relative importance of the goals: (1) global thinkers who prioritize planet-related goals; (2) pathfinders; and (3) human-centric students who prioritize people-related goals. The preference orders suggest the goals that can best improve the acceptance of sustainability actions. Climate change and water are the most relevant calling words for environmental actions, while health, hunger, and poverty are the equivalents for social actions. Collaboration by a partnership is not considered to be among the essential items, but the position of education is encouraging. Understanding the motivations of the respondents can be used for shaping attitudes in line with the policy expectations; moreover, the factor membership can be used as a grouping factor for a broader survey. The resultant factor characteristics can be used as grouping factors for broader surveys aimed at understanding the motivations behind environmentally conscious behavior.

1. Introduction

The various goals and action areas related to environmental protection and sustainability have been continuously developed and refined in recent decades. The lengthy items defined by the reports of the United Nations Conferences aimed to serve the interests of any nation. There was an agreement on the need for global cooperation, but prioritizing the principles and actions was difficult. Subsequent models moderated the number of these items, which could lead to oversimplifications. Although the Brundtland Report [1] offered a three-component framework for corporate sustainability, including environmental, social, and economic pillars, this kind of simplification cannot describe the complexity of the problems. Global problems include both environmental and social issues interconnected with each other that require coordinated innovation and technology development action covered by, e.g., circular economy [2,3]. Snarr and Snarr [4] discussed 14 issues in 4 groups covering conflict and security, the global economy, development, and the environment. The book series entitled State of the World, published by the Word Watch Institute annually (first in 1984), shows serious problems from 2004 with a highlighted question. The United Nations names 23 global issues from Africa to youth in alphabetic order [5].

The United Nations’ 17 goals to transform our world by the United Nations, called Sustainable Development Goals (SDGs), cover the global issues and institutional system of the solution in a comprehensive manner [6]. The system of the 17 SDGs, and especially the interrelations defined between the goals, provides a compact framework covering 169 targets and 248 indicators. Any problem or action is tied to a single goal; in other words, achieving a target may be available from different starting points; development programs can strengthen each other. There are five pillars (5Ps) defined for grouping the goals, three of them (people, planet, and prosperity) can be matched to the pillars of the Brundtland Report, and two (peace and partnership) put further emphasis on asserting common interests [6]. The 5Ps model is used in the study.

Ultimately, making a judgment on which targets and actions are more or less important seems to be unnecessary. Although the problems and the related goals should not be competing with each other, the limited time and the availability of resources for solving the problems require finding effective solutions and forming preferences. This makes the analysis of the relative importance of the SDGs relevant. A goal considered more important can improve the acceptance and support of the development programs, which are available more efficiently. Therefore, learning the patterns of opinions about the goals may support targeted strategies.

The paper aims to contribute to the knowledge base of sustainability by exploring the perception of the SDGs among Hungarian business students. The purpose of the study is to explore the relative importance of the SDGs and define characteristic patterns of the opinions by Q-sort evaluation.

Q-methodology is popular in several fields for exploring behavioral patterns. It is not unknown, even in environmental issues; there are some Hungarian studies based on this methodology [7,8,9,10]. Methodologically close to the present paper, Gannon et al. [11] published a case study in East Africa for exploring development corridors, including selected goals and targets of SDG. This study has a more general approach; therefore, targets are not included. Considering the conditions and the limitations of the investigations, the analysis can be acknowledged as a holistic pilot study for preparing broader surveys or international comparisons.

2. Literature Review

2.1. From Stockholm to Sustainable Development Goals

The large number and complexity of factors threatening the development and survival of humanity are well characterized by the history of United Nations conferences on environmental and social issues (see [12]). It is worth noting that the detailed overview of the topics and the achievements go far beyond the scope of this paper. The United Nations Conference on the Human Environment (UNCHE; Stockholm, 1972) must be highlighted. The Stockholm Declaration and Plan of Action [13] summarizes the principles for the preservation and enhancement of the human environment and includes recommendations for international environmental action. The document named 26 principles and 109 recommendations in 4 groups for action (

Table 1. Stockholm and Rio recommendation fields [13,14].

Sections of Recommendations Stockholm 1972	Sections of Agenda 21 Rio de Janeiro 1992
Planning and management of human settlements for environmental quality	Social and economic dimensions
Environmental aspects of natural resources management	Conservation and management of resources for development
Identification and control of pollutants of broad international significance	Strengthening the role of major groups
Development and environment	Means of implementation

). Transforming these recommendations into operative actions was an enormous challenge with a remarkable need for resources. Nonetheless, the main success of the conference was the systematic listing of the problems and the establishment of the institutional background for managing the progress.

A recognition of the Stockholm conference was the necessity for managing overlaps in the recommendations. Twenty years later, the ‘Earth Summit’ in Rio de Janeiro aimed to define how different social, economic, and environmental factors are interdependent and evolve together and to develop a new agenda for international actions. The Rio Declaration [14] had 27 principles on new and equitable partnerships and development through cooperation among States, social sectors, and individuals. Agenda 21 [15] presented 38 topics (excluding preambles) in 4 sections (Table 1). The sections and the problems highlighted in the related documents in Stockholm and Rio de Janeiro are similar, but refinement and compression can be observed.

After another 20 years, the Rio+20 United Nations Conference on Sustainable Development was organized in Rio de Janeiro. In line with the Millennium Developments Goals, document A/RES/70/1 entitled Transforming Our World: The 2030 Agenda for Sustainable Development [6] presented the 17 SDGs (Figure 1) with 169 integrated and indivisible targets associated. The empirical analysis is based on the 17 goals.

The concept of sustainability has been extended from the first idea of a strategy against ecological disasters resulting from overusing natural resources. Nowadays, it has first-rate economic and social complexity [17].

Beyond policy development, the SDGs offer an excellent framework for research. Efforts assigned to any goals can contribute to the dynamic enhancement of the knowledge base by the international share of the local results, or weighted evaluation of the performance allows the building of new indicator sets and strategy evaluation [18]. An updated understanding of sustainability is included, with an emphasis on the coordinating role of Goal 17 regarding partnership for the goals. Since the SDGs and a large number of targets are interconnected with each other, it is worth presenting a grouping for easier visibility. The United Nations defined the 5Ps of the goals as follows [6]. This categorization is used to support the students’ evaluations and the characteristic patterns, but other attempts to find a practical set of goals are available [19].

People:

• Goal 1: End poverty in all its forms everywhere;
• Goal 2: End hunger, achieve food security and improved nutrition, and promote sustainable agriculture;
• Goal 3: Ensure healthy lives and promote well-being for all at all ages;
• Goal 4: Ensure inclusive and equitable quality education and promote lifelong learning opportunities for all;
• Goal 5: Achieve gender equality and empower all women and girls.

Planet:

• Goal 6: Ensure availability and sustainable management of water and sanitation for all;
• Goal 12: Ensure sustainable consumption and production patterns;
• Goal 13: Take urgent action to combat climate change and its impacts;
• Goal 14: Conserve and sustainably use the oceans, seas, and marine resources for sustainable development;
• Goal 15: Protect, restore, and promote sustainable use of terrestrial ecosystems, sustainably manage forests, combat desertification, and halt and reverse land degradation and halt biodiversity loss.

Prosperity:

• Goal 7: Ensure access to affordable, reliable, sustainable, and modern energy for all;
• Goal 8: Promote sustained, inclusive, and sustainable economic growth, full and productive employment, and decent work for all;
• Goal 9: Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation;
• Goal 10: Reduce inequality within and among countries;
• Goal 11: Make cities and human settlements inclusive, safe, resilient, and sustainable.

Peace:

• Goal 16: Promote peaceful and inclusive societies for sustainable development, provide access to justice for all and build effective, accountable, and inclusive institutions at all levels.

Partnership:

• Goal 17: Strengthen the means of implementation and revitalize the Global Partnership for Sustainable Development.

2.2. Q-Methodology

Due to the complexity of the sustainability problems mirrored in the interrelations of the SDGs, the study requires a unique methodology. A Likert-scaling or similar way [20] can lead to distortions due to social expectations [21] and a left-skewed distribution of the responses. According to Askay et al. [22], the standardized and relatively objective assessments by a questionnaire may not be satisfying in assessing personal reasons because the respondents are unlikely to be able to quantify the complex interactions among the reasons. The direct ranking of the 17 items is difficult, while a pairwise comparison requests a questionnaire that is too extensive, i.e., n × (n − 1)/2 = 136 items for evaluation, and the number of responses with a clear preference order may be limited. Guilford’s method [23] based on pairwise comparison is not outdated, but the interval scale evaluation and the missing comparison opportunity between samples limit the generalization of the results. The Q-methodology offers a solution for ranking a large number of statements and exploring typical patterns of opinions. It was developed by a physicist and psychologist, William Stephenson, in the 1930s [24]. Its popularity goes beyond psychology and medical sciences in general; it can be applied to analyzing customer behavior for marketing or management purposes.

Q methodology combines qualitative and quantitative aspects of the investigation. The respondents express their views by sorting a set of statements from most agree to most disagree, and patterns of views can be drawn.

Watts and Stenner [25] compare by-variable factor analysis and the R-methodology with by-person factor analysis and Q-methodology as invert techniques. It must be noted that R and Q approaches require different styles of surveys, so the same dataset cannot be directly translated between the procedures. Similar purposes can be served by two approaches: finding the groups on views or opinions on a topic. A usual means in R-methodology involves using factor analysis of the question for dimension reduction and then running a cluster analysis for establishing the groups. This process is based on the distances of the responses, while Q-methodology uses the correlation matrix and groups the statements. A critical issue is data availability.

The data collection method makes the relative opinion of a respondent about every statement concerning all other statements explicit, presenting a holistic order with integrated trade-offs [26]. Data recording can be managed manually or online. The traditional way uses pre-printed cards with the statements and a blank pattern for the evaluation. The respondents are asked to arrange the cards. After that, the researcher records the orders by respondents and prepares the data matrix. Software solutions are available that guide the respondents through the process step by step and automatically provide the data for analysis. Not incidentally, data processing time can be significantly reduced by online data recording, and data capture errors can be avoided. The main steps of the analytical process in Q methodology can be summarized as follows (detailed descriptions are available in [25,27,28]):

• Preparing the initial data matrix of the evaluations;
• Calculating correlations;
• Selecting the number of factors based on the eigenvalues and the scree plot;
• Calculating rotated factors loadings;
• Determining factor weights and scores;
• Analysis of distinguishing statements;
• Presenting patterns of opinions by the final factors.

A supplementary questionnaire can be included for additional data about the respondent’s characteristics.

3. Research Design

3.1. Research Goal

Although the SDGs are designed to support each other to a large extent, i.e., the fulfillment of one goal simultaneously contributes to and dictates the fulfillment of other goals, understanding the patterns of preferences in the field can contribute to developing improved strategies. If the action programs use the keywords of the preferred sustainability goals, higher acceptance and support can be assumed. Due to the interrelations of the goals, generally, better results are expected.

Differences in the perception of the relevance of the SDGs may be found by profession, national culture, living conditions, or other influencing factors. A particular emphasis on business students is justified by the fact that they are prepared to make business decisions in the near future. Based on the above, the goal of the study is to explore the patterns of business students’ perceptions.

The research question can be formulated as there is a dominant preference order among the business students and competing patterns are characteristic of the categorization of the SDGs.

3.2. Research Method

The survey uses a ready-made Q-sample based on the content of the SDGs. The “cards” that had to be arranged contained the descriptions of the SDGs. The Q-pattern of the evaluation was designed as in Figure 2. Normal distribution of the evaluations is assured by the forced sort of the participants [29]. The question for evaluation in the questionnaire asked the respondents to sort the items by their importance in their opinion.

The results are based on a voluntary online questionnaire following the structure of Easy-htmlq version 2.0.3, and data processing was performed with the free Ken-Q Analysis Desktop Edition (KADE) software. Other statistical analyses used IBM SPSS.

The factors were defined by principal component analysis with Varimax rotation for maximizing the sum of the variances of the squared correlations between variables and factors. Supplementary questions included gender, level of study, work experience, and the fact that the student was in the first semester or not.

The processes of data collection and analysis with an emphasis on services used and solutions is summarized in Figure 3.

3.3. Sample Characteristics

The application of Q-methodology can be justified by Brown [30], who emphasized that only a limited number of distinct viewpoints exist on any topic, so Q-samples containing a wide range of existing opinions on the topic will reveal these perspectives. The methodology does not require a representative sample, and due to the data collection design, it assures normal distribution of the responses. As a consequence, large samples are not required. Data collection is limited to Hungary. The sample consists of the responses from one university to exclude other influencing factors. There are 123 responses from business students available for analysis.

Table 2. Sample composition.

Grouping Factors		Number	% Within the Sample
Gender	Female	80	65%
	Male	43	35%
Study level	Bachelor	109	88.6%
	Master	14	11.4%
Semester	First semester	62	50.4%
	Not the first semester	61	49.6%
Work experience	No work experience	66	53.7%
	With Work experience	40	32.5%
	Only internship	17	13.8%

summarizes the sample characteristics.

4. Results

4.1. Demarcation of Factors

The principal component analysis offered eight factors with an eigenvalue greater than 1 based on the 123 responses. The variance explained is 26% for the first factor, the total variance explained is 39% for two factors, and 47% for three factors. The scree plot (Figure 4) suggests using three factors. It is worth noting that the total variance explained is a maximum of 77% with eight factors, but the differences between the factors are less interpretable.

Factor 1 includes 39% of the respondents, and 81.3% of them belong significantly to this factor at p = 0.05 level (flagged member); factor 2 consists of 26% of the respondents, with 56.3% of flagged members; and factor 3 consists of 35% of the respondents, with 62.8% flagged. The factor score correlations are low and moderate. Factor characteristics are presented in

Table 3. Factor characteristics.

	Factor 1	Factor 2	Factor 3
Eigenvalues	32.05736	15.87014	9.758227
% explained variance	26	13	8
Total % explained variance	26	39	47
% explained variance after VARIMAX rotation	21	12	14
Number of members (% of total respondents)	48 (39%)	32 (26%)	43 (35%)
The ratio of flagged within the factor	81.3%	56.3%	62.8%
Correlation with Factor 1	1	0.434	0.413
Correlation with Factor 2	0.434	1	0.111
Correlation with Factor 3	0.413	0.111	1

.

4.2. Factors Scores

The z-score (

Table 4. Factor scores.

Statement	Factor 1		Factor 2		Factor 3
	z-Score	Rank	z-Score	Rank	z-Score	Rank
No poverty	0.18	6	−0.87	13	1.34	2
Zero hunger	0.88	5	0.32	10	1.3	3
Good health and well-being	−0.02	9	0.58	6	1.83	1
Quality education	−0.44	10	0.72	4	0.63	5
Gender equality	0.01	8	−2.5	17	0.06	8
Clean water and sanitation	1.12	3	1.47	1	1.02	4
Affordable and clean energy	−0.59	13	0.46	7	−0.71	14
Decent work and economic growth	−0.53	11	−0.1	12	−0.35	10
Industry, innovation and infrastructure	−1.35	16	0.43	8	−1.15	16
Reduced inequalities	−0.93	15	−1.27	16	0.26	6
Sustainable cities and communities	−0.69	14	0.23	11	−0.49	11
Responsible consumption and production	0.17	7	0.42	9	−0.54	13
Climate action	2.17	1	0.77	2	0.14	7
Life below water	1.15	2	0.75	3	−0.51	12
Life on land	1.02	4	0.62	5	−0.76	15
Peace, justice and strong institutions	−0.58	12	−1.13	15	0.02	9
Partnership for the global	−1.58	17	−0.9	14	−2.08	17

) is a weighted average of the values that the Q-sorts most closely related to the factor given to a statement, and it is continuous [26]. It is calculated as a mathematical expression of the distance between a particular absolute score and the mean average score of the measured sample [25].

4.3. Distinguishing Statements

The distinguishing statements are displayed at a maximum of p = 0.05 threshold value. These statements show the items of the evaluation that draw the idiographic patterns.

Table 5. Distinguishing statements by factors.

Factor	Threshold	Q Sort Value	State. No.	Statement
Factor 1	p < 0.0001	1	1	No poverty
	p < 0.0001	0	3	Good health and well-being
	p < 0.0001	0	4	Quality education
	p < 0.0001	3	13	Climate action
	p < 0.0001	−1	16	Peace, justice, and strong institutions
	p < 0.0001	−3	17	Partnership for the global
	p < 0.001	1	2	Zero hunger
	p < 0.005	2	14	Life below water
	p < 0.005	1	15	Life on land
	p < 0.05	−2	10	Reduced inequalities
	p < 0.1	−1	11	Sustainable cities and communities
	p < 0.1	0	12	Responsible consumption and production
	p < 0.15	0	8	Decent work and economic growth
	p < 0.15	−2	9	Industry, innovation, and infrastructure
Factor 2	p < 0.0001	−1	1	No poverty
	p < 0.0001	0	2	Zero hunger
	p < 0.0001	1	3	Good health and well-being
	p < 0.0001	−3	5	Gender equality
	p < 0.0001	0	7	Affordable and clean energy
	p < 0.0001	0	9	Industry, innovation, and infrastructure
	p < 0.0001	0	11	Sustainable cities and communities
	p < 0.0001	2	13	Climate action
	p < 0.0001	−2	16	Peace, justice, and strong institutions
	p < 0.0001	−1	17	Partnership for the global
	p < 0.005	2	14	Life below water
	p < 0.005	1	15	Life on land
	p < 0.05	3	6	Clean water and sanitation
	p < 0.05	−2	10	Reduced inequalities
	p < 0.1	−1	8	Decent work and economic growth
	p < 0.1	0	12	Responsible consumption and production
Factor 3	p < 0.0001	2	1	No poverty
	p < 0.0001	3	3	Good health and well-being
	p < 0.0001	1	10	Reduced inequalities
	p < 0.0001	-1	12	Responsible consumption and production
	p < 0.0001	0	13	Climate action
	p < 0.0001	−1	14	Life below water
	p < 0.0001	−2	15	Life on land
	p < 0.0001	0	16	Peace, justice, and strong institutions
	p < 0.0001	−3	17	Partnership for the global
	p < 0.001	2	2	Zero hunger
	p < 0.1	0	11	Sustainable cities and communities
	p < 0.15	0	8	Decent work and economic growth
	p < 0.15	−2	9	Industry, innovation, and infrastructure

summarizes the distinguishing statements’ positions ordered by the threshold values. In the case of Factor 2, 14 of 17 statements are listed; in the cases of Factor 1 and Factor 3, 10 of 17 items are listed. Besides the low and moderate correlations between the factors, this analysis suggests characteristic opinion patterns.

4.4. Patterns of Opinions in Factors

The results of the survey can be presented by factor visualizations (Figure 5, Figure 6 and Figure 7). The characteristic patterns of the three factors are shown by the icons of the SDGs on the left side of the figures, while the right side replaces the icons with the 5Ps of grouping the SDGs [6]. Due to the smaller number of evaluation factors, that solution allows a simplified but transparent understanding of the opinions. Appendix A (Figure A1, Figure A2 and Figure A3) presents the output results of the analysis and marks the items for significant differences.

5. Discussion

The goal of the study is to explore characteristic patterns; therefore, a detailed set of hypotheses was not formulated. The research question was about the existence of competing patterns that was supported by the results. There is no exclusive way of thinking about SDGs. The three factors describe three different patterns for the relative importance of the SDGs confirmed by the correlations between the factors. During the analysis, several patterns were tested since the eigenvalues were convincing up to eight factors. However, the additional factors could only collect some non-significant items of the selected solution. Based on the characteristics, the factors can be described as:

• Global thinker students are in Factor 1, who are sensitive to global issues;
• Pathfinder students are in Factor 2, who do not have a clear preference order by the 5Ps of the SDGs;
• Human-centric students are in Factor 3, who prioritize the well-being of people.

According to the environmental (planet), social (people), and economic (prosperity) pillars of sustainability defined by the Brundtland Report [1], Factor 1 considers planet-centered SDGs the most important, followed by people. Prosperity is evaluated among the less important goals. Climate action and water-related goals are at the top of the list. It is worth noting that these issues may be less relevant in the case of Hungary. However, local problems and differences in water quality may occur [31,32,33]; the lack of clear water is not typical, and water quality and access to water are provided [31]. Moreover, aquatic life is underrepresented in the diet [34,35,36], and water transportation [37,38] is not remarkable. The pattern of Factor 1 undervalues the economic aspects.

At the same time, Factor 2 shows a mixed pattern. Compared to Factor 1, the same three SDGs are at the top of the list, just in a different order (Figure 5). Among people-related goals, gender equality is ranked as the least important item. Factor 3 emphasizes people over the planet. The relative order of importance of planet, people, and prosperity has changed to people, prosperity, and planet. Health, poverty, and hunger are considered the most essential areas of sustainability by these factors.

Beyond the results above, the partnership for the goals has a relatively low position in the ranking order of each factor. Factor 1 and Factor 3 ranked it last, and 64.7% of the SDGs were evaluated as more important than the partnership for the goals in Factor 2. Similarly, peace is not considered among the relatively important part of the rankings in any of the factors. A better relative position is to be found in education. The role of education in improving the individual and corporate approach to sustainability is a popular research topic; Foster [39] points out the complexity of the relationship. Quality education is ranked in the middle field in each factor, and it is not a significant distinguishing statement.

Cross-tabulation was used to check the differences by the grouping factors. The gender shows significant differences (Pearson Chi-Square = 6.293, d_f = 2, sig. = 0.043). Female students are underrepresented in Factor 2 (Figure 8). Former results confirmed that females are more sensitive to environmental and social problems [40,41,42,43], even in Hungary [44], which is reflected in the result.

6. Conclusions

The world has gone on a long journey from the principles defined in Stockholm in 1972 to the Sustainable Development Goals for the 21st century. A reduced number of goals and interrelations between them offers a comprehensive framework for individuals, corporations, and national-level policymakers. A policy implication of the study is the need for a flexible approach in formulating actions for broader social acceptance. The results suggest that climate change and water are the most relevant calling words for environmental actions, while health, hunger, and poverty are for social actions. Partnership and peace are not presented among the most preferred items in any factor. Although cooperation is emphasized by coordinating other SDGs, it is an additional element compared to the triangle of environment, society, and economy, and its role is not acknowledged by the respondents. In addition, the position of quality education compared to partnership suggests an opportunity to develop targeted sustainability education programs.

The factor compositions and the large proportion of distinguishing statements give confirmation to the research question about the competing patterns of opinions. There is no general agreement on the relative perception of global problems. Covering the SDGs with the 5Ps of the United Nations [6], the differences can be made visible. Beyond global thinkers who prioritize planet-related problems as more important and human-centric ones who prioritize satisfying basic human needs, there is a pathfinder group. Regardless of the factor classification, the economic prosperity goals or gender equality are not included in the relatively important goals among the respondents.

A novel contribution of the paper is the method selection. A methodological implication of the study is the applicability of Q-methodology in the field of sustainability. This leads to a practical implication that further investigations can make use of in two ways: first, understanding the motivations of the respondents can be used for shaping attitudes in line with the policy expectations; second, the factor membership can be used as a grouping factor for a broader survey.

There are some notable limitations of the study. Despite the remarkable benefits of the Q-sort method and its robustness in sample composition, the results are presented as a pilot study; although a representative or a large sample is not required, the reliability of the results can be improved by extensive systematic sampling in the data collection. The data collection was limited to higher education and, within that, Hungarian business students. It cannot be stated with absolute certainty that the patterns described cover the full range of relevant opinions in society; other professions and age ranges must be checked before the conclusions can be generalized. The author considers the results as a pilot. In expanding the research, the next steps are to check whether the results can be generalized within the country and to make international comparisons.