supply chain sustainability a tertiary literature review

• DOI: 10.1016/J.JCLEPRO.2019.03.250
• Corpus ID: 159363923

Supply chain sustainability: A tertiary literature review

• C. Martins , M. Pato
• Published in Journal of Cleaner Production 1 July 2019
• Business, Environmental Science

134 Citations

The future of sustainable supply chains: a novel tertiary-systematic methodology, a systematic literature review of quantitative models for sustainable supply chain management., sustainable supply chain management: review of triggers, challenges and conceptual framework., how to measure sustainability in the supply chain design: an integrated proposal from an extensive and systematic literature review, an integrative conceptual framework for supply chain sustainability learning: a process-based approach, sustainability supply chain management in indonesia: a systematic review, integrating sustainability and resilience in the supply chain: a systematic literature review and a research agenda, sustainable supply chain management in the route for a circular economy: an integrative literature review, evaluation of mathematical models in sustainable supply chain management: gap analysis, sustainable supply chain management, performance measurement, and management: a review, 241 references, performance measurement of sustainable supply chains: a review and research questions, making connections: a review of supply chain management and sustainability literature, lean management, supply chain management and sustainability: a literature review, how does social sustainability feature in studies of supply chain management a review and research agenda, evolution of sustainability in supply chain management: a literature review, the constructs of sustainable supply chain management - a content analysis based on published case studies, literature reviews in supply chain management: a tertiary study, sustainable supply chain quality management: a systematic review., from a literature review to a conceptual framework for sustainable supply chain management, supply chain collaboration for sustainability: a literature review and future research agenda, related papers.

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Supply Chain Management

ISSN : 1359-8546

Article publication date: 4 August 2021

Issue publication date: 23 November 2022

In recent years, economic, environmental and social sustainability has become one of the fastest-growing research fields. The number of primary and secondary papers addressing the triple bottom line is growing significantly, and the supply chain (SC) management discipline is in the same wave. Therefore, this paper aims to propose a novel tertiary systematic methodology to explore, aggregate, categorise and analyse the findings provided by secondary studies.

Design/methodology/approach

A novel tertiary systematic literature review approach, including 94 secondary studies, is proposed and used to analyse sustainable SC literature. The papers have been analysed using a research protocol, including descriptive and content analysis criteria.

This tertiary study does not only provide an overview of the literature on the topic of sustainability in SCs but also goes further, drawing up a categorisation of main research areas and research perspectives adopted by previous researchers. The paper also presents a rank of research gaps and an updated and a prioritised agenda.

Originality/value

This paper provides a novel interpretation of the research topics addressed by the secondary studies and presents a new classification of the literature gaps and their evolution. Finally, a dynamic research compass for both academicians and practitioners is presented.

• Sustainability
• Economic sustainability
• Supply-chain management
• Green supply chains
• Triple bottom line
• Sustainable supply chain
• Sustainable supply chain management
• Tertiary study

Centobelli, P. , Cerchione, R. , Cricelli, L. , Esposito, E. and Strazzullo, S. (2022), "The future of sustainable supply chains: a novel tertiary-systematic methodology", Supply Chain Management , Vol. 27 No. 6, pp. 762-784. https://doi.org/10.1108/SCM-08-2020-0383

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The standard literature for sustainability in supply chain management includes systematic literature reviews and studies on triple bottom line sustainability.

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Martins, C.L. | Pato, M.V.

Over the last 30 years, supply chain sustainability has become one of the most dynamic and prolific decision management research fields. The volume of primary and secondary research articles continues to increase yearly with the introduction of emerging topics, new modeling and theoretical approaches, and multidisciplinary perspectives. Motivated by this body of literature, this study reviews key supply chain management concepts, sustainability perspectives, and methodological literature review features to support the systematic review of 198 surveys published between 1995 and 2018. The objective is to answer three research questions: which are the existent literature reviews on supply chain sustainability, what are their methodological features, and what are their main objectives and subject matters. Following a content analysis, the study maps and assesses the source material according to analytic categories pertaining to methodological and content features. The study shows that systematic literature reviews are becoming the standard method to conduct literature reviews on sustainable supply chain. However, frequently reviews fall short of meeting, and reporting the methodological rigor required by the systematic method. Although most reviews identified adopt a triple bottom line outlook on sustainability, social aspects continue to be underrepresented, namely in comparison with the environmental factor. This tertiary study renders a comprehensive critical survey of the current status of research on supply chain sustainability.

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Toward greener supply chains by decarbonizing city logistics: a systematic literature review and research pathways.

1. Introduction

2. research background and motivation, 2.1. research background, 2.2. research motivation, 4. bibliometric analysis and discussion, 4.1. publication years, 4.2. publication by journals, 4.3. publication by country, 4.4. keyword analysis, 5. a recap of proposed dcl solutions and qbl aspects in the extant literature.

6. Evolution of DCL Research and Concluding Remarks

Author contributions, data availability statement, conflicts of interest.

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Toktaş, D.; Ülkü, M.A.; Habib, M.A. Toward Greener Supply Chains by Decarbonizing City Logistics: A Systematic Literature Review and Research Pathways. Sustainability 2024 , 16 , 7516. https://doi.org/10.3390/su16177516

Toktaş D, Ülkü MA, Habib MA. Toward Greener Supply Chains by Decarbonizing City Logistics: A Systematic Literature Review and Research Pathways. Sustainability . 2024; 16(17):7516. https://doi.org/10.3390/su16177516

Toktaş, Doğukan, M. Ali Ülkü, and Muhammad Ahsanul Habib. 2024. "Toward Greener Supply Chains by Decarbonizing City Logistics: A Systematic Literature Review and Research Pathways" Sustainability 16, no. 17: 7516. https://doi.org/10.3390/su16177516

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Integrating the sustainable development goals into health professions’ curricula: using the nominal group technique to guide their contextualisation

• Joanna McCormack 1 ,
• Christy Noble 2 ,
• Shannon Rutherford 3 ,
• Lynda J Ross 4 &
• Andrea Bialocerkowski 5  

BMC Medical Education volume  24 , Article number:  972 ( 2024 ) Cite this article

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To embed the Sustainable Development Goals in health profession education, educators must contextualise them to their profession and geographical region. This study used the nominal group technique to contextualise the SDGs for Australian nutrition and dietetics tertiary education programs by determining the specific knowledge, skills, and values required for graduating dietitians to practise sustainably.

In 2022, 23 experts in food and sustainability attended a group session that employed the nominal group technique to discuss the Sustainable Development Goals knowledge, skills, and values Australian dietetic students should develop. After the group session, participants ranked the Sustainable Development Goals according to their perceived level of importance for student dietitians. These data were analysed using multi-methods, including a summation of the rankings, directed qualitative content analysis and reflexive thematic analysis.

The three highest-priority Sustainable Development Goals identified were (1) Zero Hunger, (2) Good Health and Well-Being, and (3) Responsible Consumption and Production, which were then considered with the qualitative findings. The main categories that were generated from the content analysis reflected the broad knowledge, skills, and values student dietitians should develop. The preliminary codes provided specific details for each of the main categories. The thematic analysis generated two additional themes: the importance of Indigenous ways of knowing, being and doing, and authentic experiential learning activities.

Conclusions

The method employed for this study provides a useful framework for health professions to contextualise the Sustainable Development Goals to their profession and geographical region. For this study, the ranking process and the qualitative data analysis enabled the Sustainable Development Goals to be reframed in a way that would be meaningful for dietetic educators and students and demonstrate the interrelatedness of the goals. The direct qualitative content analysis and reflexive thematic analysis identified the knowledge, skills, and values student dietitians should develop.

Peer Review reports

Introduction

The 2030 Agenda for Sustainable Development was accepted by all countries in the United Nations in 2015, [ 1 ] and outlines the 17 Sustainable Development Goals (SDGs) and their targets and indicators. These goals, while widely recognised as being ambitious, [ 1 , 2 ] require urgent action to achieve them. One of the goals, SDG 13 (Climate Action), highlights the need to address climate change. [ 1 ] The adverse impact of climate change on human health has been well documented [ 3 , 4 , 5 , 6 , 7 ]. Globally, the healthcare sector is responsible for 4.4% of greenhouse gas emissions and consequently is contributing to the climate crisis [ 8 ]. This bi-directional relationship between healthcare and the climate crisis supports the need for universities and their health profession educators (HPE) to develop health students’ capabilities to address both population and planetary health [ 9 , 10 , 11 ]. Planetary health is a paradigm that acknowledges the interconnectedness between human health and the environment, as well as the social, economic and political systems that support equity [ 11 , 12 ]. The SDGs support planetary health, however, health professionals and students are currently unprepared to meet the complex challenges they will face due to climate change, [ 10 , 13 ] despite health professions being acknowledged as key change agents to achieve the SDGs [ 9 , 10 , 11 ].

To date, the literature has focused on sustainable healthcare education. The SDGs have been used to validate the importance of educating the current and future health workforce on how to respond to the climate crisis and create environmentally sustainable ways to practice [ 10 , 13 , 14 , 15 , 16 , 17 , 18 ]. Reducing the impact of healthcare on the environment is critically important, however the environment directly relates to only four of the 17 SDGs [ 1 ]. While interrelated, the SDGs can be grouped into three pillars; the environment, society, and the economy, with SDG17 (Partnerships for the Goals) transcending all three pillars [ 1 ]. Given the interrelated nature of the SDGs, for health professionals to maximise their contribution to the SDGs, including Goal 13 (Climate Action), HPE must develop their students’ knowledge, skills, and values across all three pillars [ 2 , 19 ].

Education for Sustainable Development (ESD) is a well-established concept that aims to develop competencies that empower individuals to take informed, responsible actions that align with the SDGs [ 20 , 21 , 22 ]. Several frameworks exist that provide guidance specifically for HPE to integrate sustainable development into curricula [ 15 ]. The United Nations Educational, Scientific and Cultural Organization’s (UNESCO) framework for ESD details eight cross-cutting key competencies and includes learning objectives, topics and activities for each SDG for educators to adapt to their specific profession and learning context [ 2 ]. The ‘Association for Medical Education in Europe (AMEE) Consensus Statement: Planetary health and education for sustainable healthcare’ has also detailed learning activities, opportunities, and assessment approaches for HPE to integrate sustainable healthcare education into their curricula [ 10 ]. While it addresses the SDGs across the three pillars, their recommendations are not specific to particular health professions [ 10 ]. Recently, 17 frameworks that inform planetary health education for health professions were found via a rapid review of the literature, [ 10 , 23 ] including the AMEE Consensus Statement [ 10 ]. These frameworks were pitched at a high conceptual level with the majority focusing on environmental sustainability rather than the interplay between the environment, the economy and social sustainability [ 10 , 23 , 24 ]. While there are conceptual frameworks, [ 23 , 24 ] HPE still need to make sense of the SDGs and contextualise them to their profession and the geographical region in which their programs are enacted to maximise their impact [ 25 ]. Furthermore, there is a paucity of literature describing how to contextualise the SDGs for HPE.

Customising the SDGs and embedding them into different health professions’ curricula is complex and multifaceted [ 15 ]. For instance, within universities, organisational, structural and policy-level change is required in addition to upskilling HPE, who will likely need to develop their own knowledge, skills, and values to design and integrate new requirements into what is often an overcrowded curriculum [ 25 , 26 , 27 ]. One way to address overcrowding in curricula is to not consider knowledge, skills, and values as separate ‘knowledge’ entities. Instead, Billett (2015) offered an approach that focuses on the interdependence between these three types of knowledge (Fig.  1 ) [ 28 ]. For students to engage meaningfully with learning experiences, Billet (2015) suggested that first, they must demonstrate dispositional readiness, i.e., “the attitudes, values, interests, and intentions that direct and guide an individual’s conscious thinking and acting”. 28(p. 369) More simply, the students must value economic, social, and environmental sustainability first, for them to then engage with the knowledge (conceptual knowledge), develop the skills (procedural knowledge), and further develop the values required. Due to the interdependence of these three knowledges, ‘knowledge, skills and values’ will herein simply be referred to as knowledge [ 28 ].

Interdependence amongst conceptual, procedural and dispositional knowledge [ 28 ]

While both ambitious and broad, the SDGs must be urgently integrated into health professions curricula if these goals and their associated targets are to be met. Universities and HPE must identify how to do this in a way that is impactful and makes sense in the context of their profession and region [ 10 ]. While universities must evolve their curricula to address the SDGs, the knowledge that students are required to develop to prepare them for the future workforce may not yet be clear [ 15 ].

To identify the knowledge that health profession students should acquire, this study employed the Nominal Group Technique (NGT). The NGT is “a group process model for identifying strategic problems and developing appropriate and innovative programs to solve them”([ 29 ], (p467)). Since it was published in 1971, it has frequently been used to explore healthcare priorities [ 29 , 30 ]. The NGT was therefore identified as a data collection method that may elicit the knowledge health profession students should develop to prepare them for the future workforce.

To utilise the NGT to contextualise the SDGs, one profession in one geographical region was the focus, i.e., Nutrition and Dietetics in Australia. Specifically, we sought to identify the knowledge that should be developed in Australian nutrition and dietetics tertiary education programs in relation to the SDGs. Australia was chosen as the region, as while it is geographically diverse, the professional standards and competencies are nationwide and determined by Dietitians Australia [ 31 ]. This means that irrespective of where in Australia the program is delivered, the same professional competencies must be developed.

For educators of dietitians, the SDGs have broad applicability. As dietitians work primarily with food, and the food system is responsible for 19–29% of total global greenhouse gas emissions in addition to other adverse impacts on planetary health, [ 32 , 33 , 34 , 35 , 36 ] it is essential to understand the relationship between the food system, the SDGs, and a dietitian’s role. Much like healthcare, the impact of the climate crisis on the food system is expected to be complex and widespread [ 37 ]. The bi-directional relationship between planetary health and food production means that dietetic students should graduate with knowledge that supports both sustainable healthcare and a sustainable food system if dietitians are to positively contribute to achieving the SDGs [ 38 , 39 , 40 ]. The criticality of the food system on the health of the planet, and therefore human health, has meant that it is an emerging area of interest in the profession. Globally, dietitians have been clarifying their role in the broader food system through the development of summary resources such as role statements and standards [ 38 , 41 , 42 , 43 ].

Despite the increasing volume of literature and resources supporting a dietitian’s role in transforming the food and healthcare system to be more sustainable, [ 2 , 10 , 38 , 41 , 42 , 43 ] dietetic educators, like all HPE, are challenged with contextualising the SDGs to their profession and region, and identifying the knowledge required to develop students who demonstrate readiness to engage productively and sustainably at work. Therefore, to determine the knowledge (i.e., the knowledge, skills and values) required for students to practise through the lens of sustainable development when they enter the workforce, this study aims to contextualise the SDGs for Australian nutrition and dietetics tertiary education programs by determining the specific knowledge required for graduating dietitians to practise through the lens of sustainability.

Informed by pragmatism, [ 44 ] the goals of this research were to present a research method that was practical, effective, and useful for HPE broadly, and to provide an example in the context of Australian nutrition and dietetics tertiary education programs. To align with this methodological approach to the research, content analysis was used as the theoretical framework underpinning the study. One approach to qualitative content analysis is directed qualitative content analysis (QCA) [ 45 ]. This was used to analyse the data due to its rigour and its focus on yielding practical results [ 46 ]. In the context of this study, practical results was interpreted as applying the SDGs to concepts that may already be familiar to, or could be applied by, Australian nutrition and dietetic academics through describing those concepts through the lens of sustainability. To report on this study, the consolidated criteria for reporting qualitative studies (COREQ) was used to improve the rigour, comprehensiveness, and credibility of the qualitative component of this study [ 47 ]. This study was approved by the Griffith University Human Research Ethics Committee (GU Ref no. 2022/185) and all participants provided their consent to participate prior to collection of data.

As the data collection was conducted during May and June of 2022, the results of this study should be interpreted through the lens of the global situation at that time. The COVID-19 pandemic was impacting our daily lives, and geo-political conflict had erupted between Russia and Ukraine. Locally, in Australia, the climate crisis was impacting people across the country with devasting fires and floods. Australia’s complex food supply chains are vulnerable to geopolitical, environmental, economic and societal shocks, and consequently food shortages were experienced during this time [ 48 ]. These shocks resulted in individuals across Australia, who had never experienced food insecurity before, finding themselves in need of food relief [ 49 ].

Research team and reflexivity

Our multi-disciplinary research team included five female health care professionals, four with Ph.D. qualifications and one a Ph.D. candidate. All researchers in the team were teaching researchers and understand curriculum development within the context of Australian universities, and each of the researchers have completed post graduate training in curriculum design and development.

Two of the researchers lead programs of research in health professions education (AB and CN); SR is a public health expert and has an established research program in environmental sustainability with a focus on climate; and LR is a dietitian with an interest in the prevention of chronic disease. The lead investigator is a Ph.D. candidate (JM) who is a dietitian with an 11-year history of working in academia. She conducted each of the group discussions and, as per the recommendations of Braun and Clarke, kept a reflexive research journal to document her own position within the research [ 50 ]. As the participants included other dietitians working in the same field, the lead researcher knew six of the participants prior to commencing the study, however there were no identified conflicts of interest. Each of the participants was informed that the study formed part of a program of research for a Ph.D. Through regular discussions and peer review, the perspectives of the team were considered when analysing the group discussions which resulted in a focus on the semantics rather than the meaning of individual words and phrases for a more holistic interpretation of the interviews. The research team resolved any discrepant views through group discussions and consensus.

Study design

To address our research aim, this study used a multiple methods research design (Fig.  2 ). The nominal group technique (NGT) was employed to garner descriptions of the knowledge that should be embedded into Australian nutrition and dietetics tertiary education programs on the SDGs, and to rank their importance to dietetics curricula [ 51 ]. The NGT was employed as it is a structured group decision-making research technique that facilitates equal participation by allowing each participant to brainstorm and then communicate their ideas and knowledge without interruption through a round-robin style session. [ 51 ] Then the qualitative data were analysed primarily using directed QCA [ 46 , 52 ].

Research design for nominal group procedures and analysis

Participants

Purposive sampling was used to identify participants. To be included in the study, participants needed to be considered an expert in the field, i.e., a member of Dietitian Australia’s national Food and Environment Interest Group leadership team or had published literature on food and environment/sustainability in Australia. Thirty-three experts were identified and invited via email to participate in this study and a total of four nominal groups were conducted.

The participants were allocated to their preferred time for the NGT sessions and were sent an invitation to attend the online group session using Microsoft Teams (Microsoft Corporation, Version 1.0.1), which allowed them to participate in the study in an environment where they were comfortable. Prior to participating in the group session, the participants were provided with supporting materials to focus the conversation on the SDGs, and a link to a pre-study survey that asked the participants when, across a dietitian’s career (from student to practitioner) they felt each SDG should be learnt. Each group session was then recorded and transcribed using Microsoft Teams (Microsoft Corporation, Version 1.0.1). Transcripts were produced in Microsoft Word (Microsoft ® Word for Microsoft 365 MSO, version 2311) and checked for accuracy by the lead author. The transcripts were then used to complete the directed QCA and thematic analysis.

The classic NGT was adapted to include three round-robins, with each round-robin addressing one of the three pillars of the SDGs (social, economic, and environmental sustainability). A visual presentation was provided to the participants, including the question: “What knowledge, skills and values should be taught to Australian student dietitians?”, and the aggregated results from the pre-study survey. The presentation remained on-screen as a prompt while participants had five minutes of silence to generate their response to the question. Each participant was provided with uninterrupted time to share their ideas in response to the question. This was considered important to create an environment where everyone had an equal opportunity to contribute. At the end of the round-robin, the participants and the group facilitator had opportunity to clarify any points from the discussion. At the conclusion of the three round-robins, a note-taker provided the participants and the facilitator with a summary of the discussion and participants were asked to verify its accuracy. They were requested to prioritise the SDGs based on their perception of each SDG’s importance or relatedness to Australian nutrition and dietetics tertiary education programs. Each nominal group session was recorded and transcribed using Microsoft Teams technology, and then checked for accuracy by the lead author.

The quantitative and qualitative data were analysed in three phases: (1) summation of the nominal group ranking, [ 51 ] (2) a directed QCA on the transcripts, [ 46 ] and (3) reflexive thematic analysis on the transcripts [ 50 ]. This approach allowed the research team to organise the data in a way that would be meaningful for dietetic educators, while also reflecting on the rich qualitative themes not classified within the SDG framework.

Nominal group rankings of the SDGs

The participants’ rankings of the importance of the SDGs were summed, with lower scores indicating greater importance.

Directed qualitative content analysis

Directed QCA was used to synthesise the qualitative data gained from the NGT sessions and to re-frame the SDGs into meaningful concepts for dietitians [ 45 , 46 ]. Directed QCA has historically been used in healthcare research for data analysis of interviews, [ 53 ] to develop knowledge and understanding of a phenomenon; in this case, the SDGs [ 46 ]. The data from each nominal group session were separated into the three SDG pillars (social, economic, and environmental sustainability) and each pillar, across the four nominal groups, were merged. Deductive and inductive content analysis were used to gain an understanding of the knowledge that should be embedded in nutrition and dietetics tertiary education programs in Australia for each of the SDGs. Assarroudi et al’s (2018) 16-step process for directed QCA was used for the analyses [ 46 ]. This process involved analysing the data to identify ‘preliminary codes’, which were then logically grouped into ‘generic categories’. Using these generic categories and comparing them to the SDGs resulted in the development of new ‘main categories’ to describe the key knowledge to be embedded into Australian nutrition and dietetics tertiary education programs.

The data analysis was completed independently by the lead author (JM) and discussed with two co-authors (SR and LR) as per the recommendations by Assarroudi et al. (2018) [ 46 ]. The lead author’s reflexive research journal facilitated this discussion, which enhanced the rigour of the interpretation of data, and the appropriate coding and generation of categories. Each step of the analyses was individually documented, from analysing the raw data to the inductive abstraction of the main categories generated, providing an audit trail. Once the directed QCA was completed, the results were integrated with the SDG rankings. As the data were analysed, the rich insights shared by the participants related to the complexities of readying students for a sustainable future were recognised, resulting in a third phase of analysis.

Reflexive thematic analysis

Reflexive thematic analysis was employed using the data and the reflexive research journal maintained by the lead author (JM) throughout this study [ 50 ]. This allowed meaning to be drawn from data not directly aligned with the SDGs [ 50 ]. The lead author was guided by Braun and Clarke’s (2022) six phases of reflexive thematic analysis:1) Familiarisation with the dataset, 2) Coding, 3) Generating initial themes, 4) Developing and reviewing themes, 5) Refining, defining and naming themes, and 6) Writing up [ 50 ]. To enhance rigour of the thematic analysis, regular peer-debriefing between the lead author and the research team occurred during this phase of the analysis, including the sharing of initial observations and insights, reviewing the coding and themes generated, and ongoing review of the theme write-up.

Of the 33 experts invited, 27 consented to participate in the online group session. Of those who did not agree to participate, four were unable to attend at the times allocated due to work commitments, and two did not respond to the invitation. Four participants withdrew on the day of the study due to work commitments (2), technology issues (1) or personal reasons related to the working-from-home environment (1), resulting in 23 participants who contributed to the data collection. Of these, 3 were male and 20 were female. Each NGT session consisted of five to seven participants. Eight participants were recruited from Dietitian Australia’s Food and Environment Interest Group leadership team, twelve were identified from published literature on the topic of food and environment, and three were both in the Food and Environment Interest Group and had published on the topic of food and environment. Each of the group discussions lasted between 90 and 120 min, and no new concepts were introduced during the fourth and final group discussion.

SDG ranking

Zero Hunger (SDG 2), Good Health and Well-Being (SDG 3), and Responsible Consumption and Production (SDG 12) were considered the most important priorities for the Australian Dietetics curricula.

The data were assigned preliminary codes that were generated during the analysis. This then informed the inductive abstraction of generic categories and new main categories. The ‘preliminary codes’ described the specific knowledge or skills the food and environment experts believe should be embedded into Australian nutrition and dietetics tertiary education programs,, the ‘generic categories’ described the content areas that should be addressed in curriculum, and the new ‘main categories’ generated from the data represented the key values and topics that Australian nutrition and dietetics tertiary education programs should include. This addressed the aim of the study; to contextualise the SDGs for Australian nutrition and dietetics tertiary education programs by determining the specific knowledge required for graduating dietitians to practise through the lens of sustainability. When integrating the directed QCA results with the nominal group ranking of the SDGs, ‘zero hunger’ was merged with ‘good health and well-being’ due to their interdependence. The interrelated nature of the SDGs was emphasised when discussing SDG 12 (Responsible consumption and production). One participant commented “ I can’t emphasize enough that…almost all of that (sic. Health) is irrelevant if we don’t embed it all in a sustainable food system .” This quote clearly articulated the two sustainability concepts that are meaningful for dietitians: 1) health, and 2) a sustainable food system through responsible consumption and production. Therefore, to present these results in a meaningful way for dietetic educators, the preliminary codes, and the generic and new main categories were reframed and considered through the lens of “Good Health and Well-being” and “Responsible Consumption and Production”. The new ‘main categories’, or ‘key knowledge ’, generated from the groups are summarised below followed by quotes for each of them. The supplementary file provides further details of the codes and categories.

Good Health and Well-being, and responsible consumption and production

A strong social justice lens lies at the heart of population and planetary health.

Good health and well-being (SDG 3) and zero hunger (SDG 1) are the primary goals of dietitians in all current areas of dietetic practice. However, participants indicated that student dietitians should learn that ‘good health and well-being’ includes mental health. Health is largely influenced by the social determinants of health and other events which are often beyond the control of the individual. To address health inequities in the population, student dietitians should first value social justice, to then appreciate how hunger, poverty, poor education, poor food access, and food insecurity impacts people’s health.

Participants collectively agreed that a sustainable food system addresses many of the social determinants of health, providing affordable and healthy food while being ecologically and environmentally sustainable. Furthermore, it positively impacts on planetary health, which then influences an individual’s health and well-being. Given health and well-being is a primary goal of dietitians, every student should value a socially just and sustainable food system and understand its impact on population and planetary health.

“Dietitians [should] understand that in our food system – cheap food can contribute to poverty. … A very concrete example is … the cocoa supply chain and utilizing child slavery. … it’s one of the touchpoints that dietitians can …in the food system by encouraging ethical food choices and fair-trade products.” – Participant A5 *

Think global, act local

Footnote 1 As global citizens who value social justice, student dietitians need to think morally and ethically about food and how the current food system exploits workers, both domestically and internationally. The global food system is influenced by the global financial system, which exacerbates inequalities through the demand for low-cost food. Simultaneously, responsibly produced food needs to be affordable and accessible for everyone. Economic, social and nutrition inequalities negatively impact on the social determinants of health and student dietitians should learn how to advocate for reducing inequality both in Australia and internationally.

“I think what we’re doing is challenging people to think morally and ethically. … if you look at it globally… it just consistently shows that the people who are least responsible for the problems we’re facing are those who are going to probably suffer the most”. – Participant A6.

There is a strong relationship between the profession and global land and water systems, and dietitians have a moral obligation to protect and nurture those systems

Dietitians in all areas of practice work with food, so student dietitians should value the relationship between the profession and global land and water systems, and all that those systems provide. Our food system has a bi-directional relationship with the climate and has a significant impact on land and water systems; dietitians, as global citizens, should therefore have a moral obligation to protect and nurture these systems.

“Dietitians trained in Australia – we’ve got a moral imperative to take action. We need to take responsibility because Australia is one of the worst offenders in that global context , and so dietitians , being an important part of their food system – we need to step up and take action.” – Participant A6.

Collaboration and a ‘whole of food system approach’ is needed for the betterment of sustainability and human health

To facilitate the transition to a sustainable food system, student dietitians should understand the whole food system and be able to identify processes within the system that can be improved to enhance sustainability. Student dietitians should cultivate strong communication and advocacy skills to develop and enhance partnerships with industry, government, and non-traditional partners with the view to improving the sustainability of the food system. Locally, students should learn to critically analyse geographical areas for sustainability, to improve both population and planetary health.

A participant refers to a project which demonstrates this theme:

“They went into the fringe communities … that were really impoverished , and they worked with … professionals and … the lowest caste of individuals in these societies , which was women , and they taught them how to farm using indigenous techniques , … then they pooled their seeds at the end of their harvest season. [The project has] grown and grown and grown. And now [impacts] … 3500 families across 50 villages. They’ve opened restaurants… so that people from the city will come and eat local and indigenous produce.” – Participant A4.

A healthy and sustainable diet should be accessible to everyone, and primarily driven by a sustainable food system

A healthy and sustainability diet is beneficial for the population and the planet; however, a healthy and sustainable diet will be unique to the individual. Food provides more than macro- and micronutrients; food, and customs around food, are major contributors to both our physical and mental health. Therefore, student dietitians should appreciate that advising clients to consume a wide variety of in-season, minimally packaged foods, that is not beyond their needs, is advice that will look different for everyone. Currently, when student dietitians graduate they mostly work with consumers, [ 54 ] and therefore may inadvertently place the responsibility of sustainable food choices onto them. Students should develop their knowledge and advocacy skills to also work within the food system and support the transition to a sustainable food system.

“Dietitians need to understand what it’s going to take to create a healthy and sustainable food system.” – Participant D4.

Understanding economics and the political economy, and advocacy skills, are critical for sustainable food and healthcare systems

Understanding economics and the financial reasons that drive decision-making by businesses, organisations, and governments, allows dietitians to engage with key stakeholders and align the benefits of transforming the food system with the desired outcomes for the stakeholders. Student dietitians should develop these skills to advocate for improved infrastructure and reduce inequality in the community.

“Our current economic system is actually designed to produce excessive amounts of … food , to get people to consume excessive amounts of … food , and also produces excessive waste.” – Participant C1.

Clean water and sanitation are essential for safe food, the environment, and health

The food system is reliant on clean, safe water to produce fresh and healthy food, while individual health is also highly dependent on clean water and sanitation. Student dietitians should appreciate that outside of natural disasters, city-dwellers have ready access to clean and safe water, however this often does not occur in rural and remote communities.

“I suggest that clean water and sanitation is possibly the (the) most important because we know that people can’t survive more than about 24 hours without clean water.” – Participant B3.

As an evidence-based profession, student dietitians should understand environmental science for the betterment of the food system

As an evidenced-based profession that is driven to improving the sustainability of the food system, student dietitians should have a foundational understanding of environmental science, including climate science, environmental metrics, water scarcity metrics, the management of topsoil, Life Cycle Assessments (a method used to assess the environmental impact of food items) across the entire food chain, and planetary boundaries. This ensures they have a solid understanding of environmentally sustainable agricultural practices, which when combined with environmentally-sound production methods, can improve Australia’s ecosystems, biodiversity and food supply.

“What would be useful is just understanding what different environmental metrics are out there. So being aware that greenhouse gas emissions are not the be all and end all. That there’s water footprints , pesticide footprints etc.” – Participant B5.

Systems-thinking skills enable change-makers in the food and healthcare systems

The development of systems thinking skills should be scaffolded across the curriculum and applied to the food system to identify key touch points where dietitians can facilitate positive change. To be change-makers, dietitians across all areas of practice should be advocates and activists for climate action and the environment, including in both the food and healthcare systems. By influencing consumer demand for healthy and sustainable food, dietitians can impact production processes. Dietitians can also progress climate action by advocating for and improving energy usage and advocating for better waste management practices across the entire food system.

“(Know) what are the key touch points , like where are some of the really like big ticket items that dietitians can put our energy into understanding those.” – Participant D1.

Indigenous ways of knowing, being and doing was a strong theme that was generated from the data, however as only four indicators that underpin the SDGs specifically mention indigenous peoples, it was not reflected in the directed QCA due to the use of the SDGs as the initial framework. [ 1 ] Another theme that was generated was ‘how’ the identified knowledge should be taught to students to ensure it is meaningful and develops their values and readiness to learn and practise.

Indigenous ways of knowing, being and doing

Valuing indigenous ways of knowing, being and doing, and integrating that across the curriculum, was a theme that was generated from the analysis. The interconnectedness between humans and planetary ecosystems is fundamental to indigenous ways of ‘knowing, being and doing’, [ 48 ] and should be the lens through which student dietitians learn, with one participant saying: “we can’t train our future workforce without a fundamental knowledge and respect for that concept , and so to ignore this would be incredibly remiss of us as educators.” - Participant C2.

The interconnectedness between people, the planet, and the food system was further highlighted when discussing the Murray Darling Basin, and the recent mass death of thousands, potentially millions, of fish due to the depletion of water from irrigation. [ 55 ] This example and the disastrous effect that can occur if people dismiss indigenous knowledge on water and land management was discussed across multiple groups. Furthermore, it was identified that a culturally capable dietetic workforce was important for reducing inequality in Australia: “I think intrinsic in this [sic reducing inequality] is Aboriginal reconciliation and looking at reducing the gap here.”- Participant B1.

Authentic experiential learning

The value of authentic, experiential learning opportunities being embedded into courses across all areas of practise, was expressed by all groups. This theme arose from two different perspectives. First, participants discussed how students learn through active engagement. For example, one participant suggested: “expose students to scenario’s where clients may not have electricity in the homes or may not be able to afford it” - Participant C5. Another participant commented on how their university engages students in active learning: “we have an assignment which is focused on local governments , and we get students to choose the local government area and complete an audit of the local government area … and then they provide three recommendations for improving health equity and sustainability of the local food system.” - Participant A4.

Second, participants discussed how they, themselves, had continued to develop their knowledge through experiential, “on-the-job” learning, sometimes years after graduating from university. One participant, when referring to Life Cycle Assessments of food, said: “What is [environmentally] sustainable food? Because I know I’ve learned a lot over the last year about how it’s measured”- Participant A3. Another dietitian, when referring more broadly to the SDGs and sustainable food systems, said: “I’ve only really learned about it in the past couple of years by virtue of my policy work and personal interest. ” - Participant A5.

Globally, the need to integrate the SDGs into higher education is well accepted by universities. [ 56 ] While literature has thus far focused on embedding sustainable healthcare into curricula, [ 10 , 13 , 14 , 15 , 16 , 17 , 18 ] it has been recommended that the SDGs need to be contextualised to the health profession and geographical region [ 10 ]. Profession-specific knowledge of the SDGs is required to promote population and planetary health and are important attributes that students should develop before graduation [ 1 , 2 , 10 ]. Despite several frameworks and resources being available, the SDGs still need to be contextualised to the specific health professions and the geographical region within which they can work using their qualifications [ 2 , 10 , 38 , 41 ].

The current frameworks available to facilitate HPE embedding planetary health into curricula are high-level conceptual frameworks that mostly focus on environmental sustainability [ 23 ]. The results of this study differ from these frameworks by grouping knowledge according to values and concepts familiar to the profession rather than the three pillars of the SDGs, and by describing specific knowledge to be embedded in curricula. Given the urgency of embedding the SDGs into curricula and the importance of developing dispositional readiness (i.e., values) first to enhance knowledge development, [ 1 , 28 ] HPE may find a values-based framework useful as it applies the SDGs to concepts they may already be familiar with but describes those concepts through the lens of sustainability [ 1 , 28 ]. Therefore, the application of this method may assist other health professions develop a framework that is specific to their health profession.

The frameworks currently available for practising dietitians to integrate sustainability into their work focus on concepts such as sustainable food systems and healthy and sustainable diets [ 38 , 41 , 42 , 43 , 57 ]. The results of this study focused on curricula for Australian nutrition and dietetics tertiary education programs and determined the specific knowledge required for graduating dietitians to practise through the lens of sustainability, including knowledge of sustainable food systems, healthy and sustainable diets, and sustainable healthcare. It is intended that the information presented in this framework be used by all academics, from those who may identify as ‘novice’ to those more experienced. The embedding of SDGs into Australian nutrition and dietetics tertiary education programs may be expedited by using concepts dietetic educators are already familiar with and applying SDG knowledge and competencies to those concepts.

This is the first known study that uses a method that contextualises the SDGs to a specific health profession and region and provides concrete recommendations regarding how to frame the curriculum. By experts ranking the SDGs after the NGT sessions and considering this information with the results from the directed QCA and TA, this study framed the knowledge in a way that was meaningful for the profession. Therefore, the results of this study indicate that this method was successful in contextualising the SDGs for Australian nutrition and dietetics tertiary education programs. The NG method may be useful for other health professions due to the broad participant eligibility criteria, i.e., an expert in sustainability within a profession. It is recommended that participants include experts across the three pillars of sustainability i.e., environmental, social and economic sustainability, and the results may be enhanced by including an SDG expert. The research team had a focus on recruiting experts in food and environmental sustainability which may have excluded experts across the other pillars of the SDGs. Moreover, the eligibility criteria may have excluded some experts who have not published peer-reviewed papers or engaged in Dietitian Australia’s national Interest Group. Therefore, the research team recommends a diverse sample of experts are recruited to future studies to ensure that different views are heard and represented during the NGT sessions. Furthermore, the method of analyses allows the content to be framed in a way that is most useful to the profession. For professions contemplating how to embed the SDGs into curricula, the research team recommends replicating this method to accelerate the identification of the essential knowledge required to be embedded to produce work-ready graduates.

Many HPE will need to upskill to enhance their knowledge of sustainability and their confidence to facilitate student learning to urgently embed the SDGs into curricula. [ 28 ] The results of this study indicated that the method employed may overcome this barrier by articulating the specific knowledge that educators should develop, to then be confident to facilitate student learning. To enhance HPE developing this knowledge , the concepts of readiness discussed by Billett (2015) can be equally applied to HPE as it can to students. [ 28 ]

To the best of our knowledge, this is the first study that robustly examines the SDG knowledge that should be embedded in Australian nutrition and dietetic tertiary education programs. The two SDGs that were prioritised as part of the NGT ranking process, SDG 3 (Good Health and Well-Being), and SDG 12 (Responsible Consumption and Production), may assist dietetic educators to reframe curricula in a way that is meaningful for themselves and their students, and demonstrates the interrelated nature of the SDGs. The thematic analysis generated a further two themes, including incorporating Indigenous ways of ‘knowing, being and doing’ into the curriculum, and using authentic experiential learning activities. The strong emphasis of these themes across the four groups of experts highlights the important role these themes have, or should have, in dietetics education and aligns with contemporary education practices. [ 2 ] The supplementary file provided has been designed as a framework for dietetic educators. This framework can assist dietetic educators in identifying both the alignment and the gaps between the current curricula and the relevant knowledge students should develop in relation to the SDGs.

The focus of this study was on the knowledge that should be embedded in health profession tertiary education programs in relation to the SDGs; however, this approach does have some limitations. While the thematic analysis identified the Indigenous way of ‘knowing, being and doing’ as important for curricula, it did not specifically identify the conceptual and procedural knowledge to be embedded in Australian nutrition and dietetic tertiary education programs. To encourage the respectful addition of indigenous knowledge, the research team recommends that educators work with local indigenous elders and experts. This study did not explore the barriers to implementation, i.e., a ‘perfect world’ scenario was assumed. However, barriers to embedding sustainability into curricula exist and will differ depending on the profession and geographical region [ 10 , 15 ]. It is, therefore, recommended that the barriers and enablers are identified and either addressed or utilised to support the urgent uptake of this study’s findings into curricula. When considering the limitations of the methods, it is important to consider that the four participants who consented to the study, but were unable to attend, may have had differing views than those discussed in the group sessions and presented here. Also worth noting, is the high percentage of female participants, which is reflective of the high percentage of females in the profession. While this is reflective of the profession, males may have considered the topic differently. To add further rigour and to enhance the transferability of the method to other health professions, including SDG experts as participants may have beneficial.

This study set out to employ the NGT to contextualise the SDGs to specific health professions and their region. The data collection and analysis methods used in this study have reframed the SDGs so that they are meaningful for dietetic educators in Australia. Due to the urgency needed to achieve the SDGs, the research team strongly recommends other health professions use this as a method to contextualise the SDGs to their profession and curricula. The barriers and enablers to embedding this content into curricula should be examined simultaneously, if possible, to support the urgent uptake of this content into higher education.

Data availability

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

* A, B, C, D represents the group the participant contributed to; 1–7 represents the participant within the group.

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McCormack, J., Noble, C., Rutherford, S. et al. Integrating the sustainable development goals into health professions’ curricula: using the nominal group technique to guide their contextualisation. BMC Med Educ 24 , 972 (2024). https://doi.org/10.1186/s12909-024-05968-0

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Statistical assessment of digital transformation in European Union countries under sustainable development goal 9

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The pivotal role of digital transformation (DT) in contemporary socio-economic development cannot be overstated. This crucial aspect is highlighted in the Agenda 2030, specifically in goal 9 among the 17 objectives. This article presents the results of a study assessing the level of DT in industry, innovation, and infrastructure in the 27 European Union (EU) countries in 2015 and 2020. Central to this study is the proposition of an aggregated Digital Transformation Assessment Indicator (DTAI), serving as a metric to gauge the progression of EU member states. Utilizing this indicator, the article assesses the advancement status of EU countries and orchestrates a comparative ranking of their achievements in fulfilling Sustainable Development Goal (SDG) 9 between 2015 and 2020. Moreover, a classification of countries into analogous groups based on this criterion for both periods is provided. The DTAI is prepared following the methodology of the linear ordering of objects—countries of the EU 27. The zero unitarization method (ZUM) is used as the main ordering method. To compare the results obtained, the DTAI value and classifications of countries in 2015, and 2020, are also presented using Hellwig’s pattern development method. The findings of this investigation underscore the variances existing among the EU 27 nations concerning the implementation of SDG 9. Furthermore, notable fluctuations in ranking positions are also observed. The research outcomes underscore significant challenges in DT implementation, particularly within Central, Eastern, and Southern European nations. The utilized research methodology bears substantial implications for the effective realization of the 2030 Agenda and its corresponding SDGs, both at the individual nation-state level and within the broader framework of the EU.

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1 Introduction

Sustainability is a complex concept and includes economic, environmental, and social aspects (Grzebyk et al. 2023 ). The 17 issues (objectives) for assessing the implementation of the concept of sustainable development (SD) are also complex (Pakkan et al. 2023 ). Filipiak et al. ( 2023 ) note that although SD is recognized as an essential element affecting the economy, not all European Union (EU) countries surveyed consider this factor. Therefore, states, mainly EU member states, evaluate the situation.

Both individual countries and Small and Medium Enterprises (SMEs) are looking for opportunities to gain a competitive advantage, which is why they are attempting to leverage the possibilities afforded by digital transformation (DT). DT is observed in all areas of socio-economic life in both highly developed and less developed countries. This applies in particular to the 27 EU countries. Thanks to DT, SD is possible based on modern solutions implemented in various industries, economic sectors and society.

Due to the complexity of the DT process, it isn’t easy to estimate it, especially concerning the economic and societal challenges (Kolupaieva and Tiesheva 2023 ). The literature on digital business transformation is classified into three clusters based on technological, business and social impact (Kraus et al. 2021 ). Furthermore, digitisation is presented to accelerate a sustainability transition (George and Schillebeeckx 2022 ). Consequently, the subjects of public debate are DT and sustainability (Brenner and Hartl 2021 ; Del Río Castro et al. 2021 ). Moreover, intensifying the DT to ensure sustainability is of interest to intergovernmental bodies, countries and enterprises (Ionascu et al. 2022 ).

Research has examined the significance of digital technologies in achieving economic and sustainability objectives (Nambisan et al. 2019 ; Guandalini 2022 ). This research has highlighted the crucial role of digital technologies, particularly in advanced European countries, in propelling economic growth and sustainability (Bocean and Vărzaru 2023 ). Moreover, it is worth emphazising that DT and sustainability represent paradigmatic economic, social, and ecological changes (Caputo et al. 2021 ). New digital technologies exert increasingly noticeable effects, particularly in developed European countries (Tannady et al. 2023 ). On the other hand, DT supports achieving Sustainable Development Goals (SDGs) through inclusive data collection and analysis by computational techniques to reveal patterns and trends in the environment, human behaviours and experiences that help policymakers monitor progress, establish the proper programs of development and dynamic improvement (ElMassah and Mohieldin 2020 ).

The development of the theory of SD has been undertaken by many researchers (Rosário and Dias 2022 ; Pakkan et al. 2023 ), and the perception of DT in the economy and society as one of its distinguishing features has increased in importance in recent years. The role of DT is noticed by both researchers (Vial 2019 ; Briggs 2020 ; Zaoui and Souissi 2020 ), practitioners (Sabatini, Cucculelli and Gregori, 2022), and decision-makers (Grabowska 2019 ; Nawrocki and Jonek-Kowalska 2022 ; Yang et al. 2023 ). The ‘2030 Digital Compass’ identifies targets for digitisation focusing on data, technology, and infrastructure (European Commission 2021 ).

Digital technologies play a crucial role in social and economic life and are essential to the SD of society and the economy. This is reflected in the SDGs, where goal 9 references “three important aspects of sustainable development: infrastructure, industrialization and innovation”.

The essence of the transformation has been recognized at the EU level, where a plan for realising the EU’s DT by 2030 has been proposed in the form of the document ‘Road to the Digital Decade’. The plan aims to establish a governance framework that will allow Member States to work together to achieve agreed goals. The Digital Decade policy programme, with concrete targets and objectives for 2030, guides Europe’s DT: skills, DT of businesses, secure and sustainable digital infrastructures and digitalization of public services. Similarly, the European Digital Compass indicates the digital goals that should be achieved by 2030, including an increase in digital skills, which 56% of adults currently have, with the aim of 80% (Eurostat database, https://ec.europa.eu/eurostat/data/database ). This is particularly important as many new employment opportunities are emerging through DT.

Therefore, it is essential to monitor the degree of DT, which makes it possible to observe the level of advancement in implementing solutions and identify good practices implemented in individual countries. Therefore, it is reasonable to develop a metric that would synthetically enable the measurement of DT. As we can see, there are different perspectives on DT, but from the literature review, it seems relevant to take into account the other actors and sectors influencing DT (Varakamin et al. 2017 ; Tangi et al. 2020 ). In particular, it seems essential to develop one indicator, which would measure the level of advancement of EU 27 countries in achieving goal 9, which calls for building resilient and sustainable infrastructure and promoting inclusive and sustainable industrialisation. The development of such an indicator would make it possible to compare EU countries in terms of their progress towards SDG 9.

Although you can find in the literature publications attempting to assess the state of progress of countries in individual areas of goal 9, i.e. industry (Zawada 2008 ), innovation (Andrijauskiene et al. 2023 ), and infrastructure (Koszela et al., 2020), there is no research on aggregated indicators that would assess the achievement of these three areas together under goal 9 of the SDGs.

It is worth emphasizing that the different areas of DT interact with each other, hence the need for an aggregated measure. While single measures provide some insight into the phenomenon under study, synthetic measures are superior to them. The advantage of synthetic measures over single indicators lies mainly in the ability to capture information from different areas of the phenomenon under study using a synthetic indicator. This is particularly important when measuring complex phenomena. The synthetic measure would facilitate comparison between countries in implementing SDG 9. Therefore, the paper’s main aim is to determine the level of DT of EU 27 countries as part of the implementation of SDG 9 using an aggregated measure in the years 2015–2020. The Digital Transformation Assessment Indicator (DTAI) is constructed using the selected methods of statistical multidimensional analysis, i.e. the zero unitarization method (ZUM) and Hellwig’s method. These methods make it possible to order the objects under study due to the level of phenomena that a single measure cannot measure. The selected methods synthesize information from individual indicators and assign a single aggregate measure to the phenomenon under study.

This approach provides an opportunity for international comparisons in terms of the level of implementation of DT under SDG 9. It also enables the classification of countries into groups similar in terms of DT and the identification of countries that are the best/weakest in this respect.

Despite many studies on the DT and implementation of SDG 9 (Nambisan et al. 2019 ; Guandalini 2022 ; Rosário and Dias 2022 ; Pakkan et al. 2023 ), there is still a research gap in the lack of an indicator to assess the 9 influencing areas of DT highlighted in SDG 9 (Varakamin et al. 2017 ; Luken et al. 2022 ; Kynčlová et al. 2020 ). Our new approach allows an in-depth analysis of different aspects of DT, which have been combined into one aggregated measure. This has made it possible to create a comprehensive measure containing information from each DT area, facilitating a better assessment of individual countries. Such research allows for preparing practical activities in different aspects of DT to accelerate it. Given the objective of the article, the following results are expected:

Identification of the EU countries demonstrating the most outstanding leadership in achieving SDG 9 among the 27 member countries.

Identification of the EU countries’ weakest performance in achieving SDG 9 among the 27 member countries.

Identification of the EU countries that have demonstrated the most significant progress in achieving SGD 9 in 2020, compared to 2015.

Identification of the countries with the most significant declines in their progress towards achieving SDG 9 in 2020, compared to 2015.

Development of a ranking of the EU 27 countries in terms of their achievement of SDG 9 in 2015 and 2020.

The article is structured as follows. An introduction is followed by a literature review on the implementation of SDGs, with a focus on goal 9 in EU countries. The section on data and method presents the diagnostic variables and their characteristics, as well as the basis for further statistical analysis. The results and discussion section presents the research findings and corresponding discussions. The paper concludes with a section on the limitations of the study.

2 Literature review

The literature review indicates different definitions of DT depending on the perspective adopted by researchers. It is defined as “doing things in a new, digital way and closely connected with the digital revolution” (Olczyk and Kuc-Czarnecka 2022 ). In another perspective, it is described as “a new development model that calls for redefining relationships between companies, their stakeholders, and clients and reviewing previous approaches to offering services and products as companies undergo multidimensional transformation” (Zaoui and Souissi 2020 ).

G. Vial ( 2019 ) identifies as many as 23 unique definitions of DT presented in various academic papers. This enabled him to create his definition of DT, which he calls “a process that aims to improve an entity by triggering significant changes to its properties through combinations of information, computing, communication, and connectivity technologies” (Vial 2019 ). Thus, we can see that DT is analyzed from multiple perspectives and its definitions concern various aspects of socio-economic life. For this study, we have chosen the definition of DT formulated by G. Vial, which appears to be the most accurate. This definition emphasizes the transformation of information from messages of the age of algorithms to automated processes that convert existing information into practical knowledge (Hilbert 2020 ).

The literature review provides information on different aspects of DT that affect SD. The impact of DT on EU countries based on the digitalization of society and economy is caused by digital skills and technological development (Malkowska et al., 2021). Other research shows that the impact is highlighted by both e-government and big data (El-Massah and Mohieldin, 2020). DT and enterprise innovation all positively impact the SD of enterprises (Su and Wu 2024 ).

Balbi ( 2023 ) notes that digitalization significantly impacts communication, representing a revolution and a radical change for those who experience it. Digitisation has shifted from 19 and 20th-century mechanical and analogue electronic expertise to digital electronics, indicating that DT is advancing through innovation. Furthermore, DT fosters business actions that support the transition to sustainability (Chatzistamoulou 2023 ). These changes are visible in the industry. To implement digital technology, an infrastructure is required to enable information sharing (Tangi et al. 2020 ; Manny et al. 2021 ). Due to the large data sets and the need to synchronize tasks in many areas, DT is a highly demanding activity.

The EU member states aim to make Europe the digital leader by 2030 (European Commission 2021 ). They are implementing a digital policy that empowers citizens and businesses by setting standards and tasks to achieve this. The European Digital Compass outlines the digital goals that must be achieved by 2030. The European Commission has been monitoring the progress of individual countries on digitalization since 2014 through the Digital Economy and Society Index (DESI) (Digital Economy and Society Index, 2021). In addition, from 2023 onwards, the Digital Decade policy programme measures digital skills, digital infrastructure, and digitalization of business and public services to assess the implementation of the actions taken.

The practical implementation of SDGs requires the development of new technologies, digitalization, and intelligent automated production processes. The success of the implementation of goal 9—“Industry, innovation and infrastructure”—depends to a large extent on the DT that is carried out (Ringel et al. 2016 ). Depending on the level of development of information and communication technologies (ICT), the economies of individual countries effectively develop their innovation potential (Wu et al. 2018 ; Khajuria et al. 2022 ; Rosário and Dias 2022 ; Yang et al. 2023 ). Furthermore, digital technology has long been identified as a crucial driver of economic growth and development, as well as a critical determinant of sustainable urban development (Graziano 2021 ; Sabatini, Cucculelli and Gregori, 2022; Yang et al. 2023 ). Digital technologies such as artificial intelligence, cloud computing, and the Internet of Things (IoT) can support sustainable production by improving organizational efficiency, planning processes, experimenting with new business models, and creating innovative ecosystems. (Wu et al. 2018 ; Bonamigo and Frech 2020 ). Moreover, the contemporary innovation-intensive economy requires companies to have the capacity to repeat the development of potentially radical innovations at every stage of their existence to create sustainable long-term value (Cornell et al. 2020 ).

However, the extent of ICT usage varies across countries and is influenced by a range of cultural, economic, technological, and social factors (Ringel et al. 2016 ; Asongu et al. 2018 ; Sabatini, Cucculelli and Gregori, 2022; Yang et al. 2023 ). Moreover, the global innovation landscape is changing during the pandemic, as well as recovery and geopolitical upheaval, as observed in the Global Innovation Index (GII) Report (WIPO 2023 ). Studies (Androniceanu et al., 2019; Bilozubenko et al., 2020; Chakravorty, Chaturvedi, 2017; Milosevic et al., 2018) show differences in countries’ digital progress. Moreover, it is observed that the level of digitalization in European countries is differentiated (Kolupaieva and Tiesheva 2023 ). The research shows that localization allows governments to effectively tailor SD strategies locally, which can be boosted with DT (ElMassah and Mohieldin 2020 ). It is observed that the progress and impact of DT might be different than in the global context. Research shows that countries with a high level of DT have also adopted sustainability principles and recorded high economic growth rates per capita (Bocean and Vărzaru 2023 ).

There are many studies in the literature on this topic regarding measurement methods, selection of indicators, and factors influencing the implementation of SDG 9 and the progress of its implementation (Varakamin et al. 2017 ). For example, Varakamin et al. ( 2017 ) use the AHP method to evaluate SDG 9 indicators in Thailand, which in turn is used to measure the progress and performance of SDG 9 in 20 Sub-Saharan African (SSA) countries, while Luken et al. ( 2022 ) use two indicators, SDG 9 progress and SDG 9 performance (Luken et al. 2022 ).

Kynčlová et al. ( 2020 ) base their criteria selection on the global framework of indicators for the Agenda 2030 goals adopted by the United Nations General Assembly (Kynčlová et al. 2020 ). The results of their analysis show that industrialised economies outperform other countries, with the top five in the 2016 ranking being Ireland, Germany, the Republic of Korea, Switzerland and Japan (Kynčlová et al. 2020 ). Ulbych (2020) takes the same indicators into account and compares the degree to which goal 9 is met in the Czech Republic and Poland countries. Both economies are transit countries with a high share of manufacturing in GDP, so a developing competitive and sustainable industrial base is crucial. It is also essential to modernise production processes and increase the share of value added by medium- and high-tech industries in total value added.

Other indicators and a different methodology to assess the level of EU countries in building stable infrastructure, promoting sustainable industrialisation and supporting innovation are adopted by Brodny and Tutak ( 2023 ). The basis of the proprietary methodology they have developed is a multi-criteria decision-making approach. They use TOPSIS, WASPAS, and EDAS methods to determine the index, entropy and CRITIC methods to determine the weight of the indicators adopted. In addition, the use of Spearman’s and Kendall’s nonparametric tau tests allows the analysis of the relationship between the SDG 9 indicator and the fundamental economic, environmental and energy parameters, as well as the digitalization of the countries studied (Brodny and Tutak 2023 ).

Therefore, it can be seen that many researchers have attempted to assess the level of achievement of SDG 9 using different methods and indicators (Nambisan et al. 2019 ; Guandalini 2022 ; Rosário and Dias 2022 ; Pakkan et al. 2023 ). However, a critical literature review has shown that the presented methods and indicators measure DT imperfectly. Therefore, efforts have been made to develop a new measure. The literature review provides evidence that the areas of innovation (Gajdzik et al. 2024 ; Li et al. 2024 ; Xiao et al. 2024 ), industry (Chatzistamoulou,  2023 ; Sołtysik- Piorunkiewicz and Zdonek  2021 ), and infrastructure (Hodson et al. 2024 ; Li et al. 2024 ) are relevant to DT. Therefore, areas within these sectors that are relevant for the measurement of DT have been identified.

2.1 The importance of innovation in the DT

The implementation of DT takes place through undertaken innovation activities. Essential aspects for assessing the level of innovation are, e.g., Gross Domestic Expenditure on R&D by sector (GERD), R&D personnel by sector and patent applications to the European Patent Office by country of residence of the applicant/inventor. Their role in the development of DT is outlined below.

2.1.1 Gross domestic expenditure on R&D by sector (GERD)

Information technology (IT) plays a significant role in shaping gross national research and development expenditure. IT infrastructure, such as high-performance computing, data analysis and simulation tools, helps scientists conduct complex experiments, analyze vast amounts of data, and accelerate the pace of research (Sołtysik-Piorunkiewicz and Zdonek 2021 ; Nuseir et al. 2022 ). By facilitating efficient and effective R&D processes, IT contributes to the growth of R&D (Wu et al. 2018 ). Additionally, IT enables collaboration between scientists, both locally and globally. R&D activities generate large amounts of data that require advanced management and analysis. IT infrastructure, including databases, data mining algorithms, and machine learning, enables scientists to extract insights from data, identify trends, and make informed decisions (Tannady et al. 2023 ). Effective data management and analysis contribute to more efficient and productive R&D processes, leading to increased GERD. Advanced communication and collaboration technologies such as videoconferencing, cloud platforms, and virtual research environments facilitate knowledge sharing, collaborative projects, and interdisciplinary research (Michna and Kmieciak 2020 ; Yaqub and Al-Sabban 2023 ). IT-enabled collaboration increases GERD by leveraging diverse expertise and pooling resources for R&D initiatives (Bonamigo and Frech 2020 ; Michna and Kmieciak 2020 ; Nawrocki and Jonek-Kowalska 2022 ).

Information technology facilitates the transfer of research results into commercial applications. IT infrastructures such as technology transfer platforms, intellectual property management systems and online marketplaces connect researchers with potential industrial partners and investors. By supporting technology transfer and commercialisation, IT encourages private sector investment in R&D, thereby increasing GERD (Sabatini et al. 2022 ; Yaqub and Al-Sabban 2023 ).

2.1.2 R&D personnel by sector

IT across various sectors of the economy (businesses, government institutions, academia, and non-profit organizations) enables more efficient management of data and information related to R&D. Customer Relationship Management (CRM) systems can aid in analysing and tracking customer needs and provide feedback for the research process. Moreover, analytical tools and artificial intelligence support data analysis, business processes, and better decision-making (Roba and Maric 2023 ). An example is the application of Big Data Analytics in the Business Enterprise Sector (BES), which facilitates identifying trends, customer preferences, and new development opportunities (Taleb et al. 2020 ; Sołtysik-Piorunkiewicz and Zdonek 2021 ; Nuseir et al. 2022 ).

Government agencies, on the other hand, can utilize advanced IT tools for data collection, analysis, and management, enabling evidence-based decision-making. Examples of such tools and initiatives include e-health systems, smart cities, e-administration, and digital services for citizens (Corte et al. 2017 ; Graziano 2021 ; Jonek-Kowalska 2022 ).

In higher education, introducing modern e-learning tools and platforms enables remote research, access to scientific resources, and collaboration and knowledge exchange among researchers from different institutions. Furthermore, IT technologies support scientific research, data analysis, simulations, and modelling, accelerating research processes and enabling innovative discoveries.

To a limited extent, even in the Private Non-Profit Organizations (PNP) sector, IT technologies play a significant role as they support the work of these organizations. IT tools and systems aid in data management, project monitoring, result analysis, and reporting (Mayer and Fischer 2023 ). Examples include CRM systems, which help non-profit organizations build and maintain long-term relationships with donors and track donations and outcomes. IT technologies enable non-profit organizations to acquire knowledge and access scientific resources through e-learning tools and collaboration platforms (Berardi and Rea 2010 ; Miković et al. 2020 ).

The impact of ICT on the size of R&D personnel in various sectors can be complex. On one hand, IT technologies may contribute to increasing the efficiency and productivity of research processes, leading to an increased demand for R&D personnel. On the other hand, advancements in automation, machine learning, and artificial intelligence may affect the automation of specific tasks, potentially reducing the demand for R&D personnel in some areas.

2.1.3 Patent applications to the European Patent Office by applicant’s/inventor’s country of residence

Patents are essential indicators of innovation and technological progress as they represent the creation and protection of new inventions and technologies (Higham et al. 2021 ). Technological innovation is crucial for developing sustainable infrastructure, promoting industrialisation and supporting innovation in the context of SDG 9. Patents represent progress in various areas, including renewable energy, clean technologies, waste management, transport systems, and digital solutions. These areas are crucial components of the SDGs (Javeed et al. 2022 ; Liu et al. 2023 ).

Additionally, patent-protected inventions can create new market opportunities and industries, leading to job creation and economic development. Patents also provide inventors and companies with intellectual property rights, enabling them to commercialise their inventions and attract investment for further R&D. This financial support and economic growth contribute to the successful implementation of SDG 9.

2.2 The importance of infrastructure in the DT

Infrastructure is another crucial aspect to consider when measuring the impact of DT. Developed and modern infrastructure can improve residents’ living standards and contribute to countries’ economic development.

2.2.1 Share of buses and trains in inland passenger transport

Digital technology has a significant impact on enhancing sustainable transportation in cities. This includes the involvement of buses and trains in urban passenger transport and the participation of railways and inland waterway transport in inland freight transport. Primarily, digital technology increases efficiency in these areas by enabling better planning, scheduling, and management of bus and rail services (Corte et al. 2017 ; Jararweh et al. 2020 ; Graziano 2021 ).

Real-time data analysis and optimization algorithms can be utilized to optimize routes, reduce congestion, and improve overall performance (Molinillo et al. 2019 ; Walentek 2021 ). This leads to better utilization of existing resources and encourages the use of public transportation. Additionally, digital solutions such as mobile applications, online ticketing systems, and passenger information systems provide convenience and enhance overall passenger experiences. Passengers can access real-time information about schedules, delays, and seat availability, making public transportation more accessible and user-friendly (Anthopoulos 2015 ; Li et al. 2017 ; Hysa et al. 2021 ; Yadav et al. 2021 ).

Digital platforms and applications facilitate the implementation of demand-responsive services, where public transport routes and schedules can be dynamically adjusted based on passenger demand. This helps provide more flexible and personalized transport options, encouraging people to choose public transportation over private vehicles, which is crucial for SD and environmental protection.

2.2.2 Share rail and inland waterways in inland freight transport

Digital technology enables the integration of different supply chain elements, including rail and inland waterways. Through data analytics, sensors and automation, logistics processes can be optimized, leading to improved efficiency, reduced costs and better use of resources (Tannady et al. 2023 ). Additionally, digital tools like GPS tracking, RFID tags and IoT devices can track and monitor cargo shipments over time (Lin et al. 2023 ; Wang 2023 ). This improves cargo visibility throughout the transport process, enabling better security, reduced theft and better supply chain management.

Digital platforms facilitate the seamless integration of different modes of transport, such as rail and inland waterways, with other parts of the logistics network.

2.3 The importance of industry in the DT

The other indicators selected to measure DT do not only belong to the industrial sector but also significantly impact the economic level of companies and the implementation of SDGs. Thanks to them, observing the effects of the implemented DT activities is possible. Among them are industrial air emission intensity, tertiary educational attainment by gender, gross value added in the environmental goods and services sector, and high-speed internet coverage by type of area.

2.3.1 Air emission intensity from industry

The intensity of air emissions from industry is closely linked to the development of digital technology. Digital technology enables the implementation of advanced monitoring and control systems in industry. Through sensors, data analysis, and automation, industries can continuously monitor their operations and identify sources of air emissions (Chang 2015 ; Pirc et al. 2016 ; Corte et al. 2017 ). Real-time data collection and analysis help detect inefficiencies, optimize processes, and minimize pollution emissions.

Digital control systems can also regulate and adjust production processes to ensure compliance with environmental standards, reducing the intensity of air emissions. By integrating digital systems with industrial machinery and devices, companies can optimize energy consumption, reduce waste, and limit emissions. For example, smart grids can more effectively balance energy supply and demand, minimizing fossil fuel-based energy sources and reducing air emissions (Myeong and Shahzad 2021 ).

By leveraging digital technology, the industry can improve its environmental efficiency, reduce air emissions intensity, and contribute to the achievement of SDG 9. The development and adoption of digital solutions increase monitoring and control capabilities, optimize energy efficiency, promote industrial automation, support sustainable supply chains, and facilitate collaboration for SD.

2.3.2 Tertiary educational attainment by sex

Tertiary education is vital in human capital development, equipping individuals with the knowledge, skills, and experience to address complex environmental challenges. Higher education institutions create a skilled workforce capable of driving innovation, designing sustainable infrastructure, and implementing technological solutions that support SDG 9 (Podgórska and Zdonek 2022 ).

Furthermore, research conducted at universities and research institutes can lead to breakthroughs in renewable energy, clean technologies, waste management, and transportation efficiency, thereby supporting SDG 9. Higher education fosters an environment that promotes critical thinking, creativity, and problem-solving skills necessary for driving innovation and finding sustainable solutions (Michna and Kmieciak 2020 ). Higher education fosters a learning environment that cultivates a deep understanding of the principles of SD, enabling graduates to make practical contributions to the implementation of SDG 9 across various sectors (Michna and Kaźmierczak 2020 ; Miković et al. 2020 ).

2.3.3 Gross value added in the environmental goods and services sector

Digital technology enables innovation and productivity improvement in the environmental goods and services sector. Companies can optimize their operations, streamline processes, and develop innovative solutions by integrating digital tools such as data analytics, the IoT, and automation. These advancements contribute to increased productivity, cost reduction, and overall performance improvement, ultimately leading to higher gross value added in the sector (Gössling 2021 ; Rosário and Dias 2022 ; Yang et al. 2023 ).

Moreover, digital technology facilitates developing and implementing smart solutions and services in the environmental protection sector. For example, digital systems enable better monitoring and control of energy production, predictive maintenance, and network optimization in renewable energy generation. In waste management, digital platforms can streamline waste collection, recycling processes, and resource recovery (Asongu et al. 2018 ; Jararweh et al. 2020 ; Ulbrych 2020 ; Luken et al. 2022 ). These smart solutions and services contribute to SD and generate economic value, thereby increasing gross value added in the environmental goods and services sector.

Digital technology opens up new markets and business opportunities in the environmental protection sector. Adopting digital tools enables the creation of innovative products, services, and business models that address environmental challenges. This market expansion and growing demand for sustainable solutions lead to sector growth and increased gross value added (Grabowska 2019 ; Sabatini et al. 2022 ; Liu al., 2023).

2.3.4 High-speed internet coverage, by type of area

High-speed internet access accelerates DT across various sectors, including infrastructure, manufacturing, transportation, and energy. It enables the integration of digital technologies such as the IoT, cloud computing, and data analytics, which optimize operations, improve efficiency, and promote sustainable practices (Bratulescu et al. 2023 ).

DT, supported by fast internet, enhances industrial productivity, resource management, and environmental sustainability. Fast internet also enables effective e-governance and delivery of digital public services. It streamlines administrative processes, citizen engagement, and access to government information. These digital advances contribute to the efficiency and effectiveness of public administration in the context of SDG 9. Access to fast internet fosters innovation and entrepreneurship by providing a platform for collaboration, knowledge sharing, and entrepreneurial activities (Wang 2023 ; Wang et al. 2023 ). Fast internet access facilitates the development of digital startups, promotes technological innovation, and enables individuals to create and scale sustainable businesses. This contributes to economic growth, job creation, and the overall success of SDG 9.

Within the EU, several countries have highly developed and extensive fast internet infrastructure. Countries such as Denmark (Saunavaara et al. 2022 ), Sweden, Finland (Helms Jørgensen et al. 2019 ), the Netherlands (Tangi et al. 2020 ), and Luxembourg (Hunady et al. 2022 ) prioritize investments in digital technologies and have robust networks supporting high-speed internet connections.

In summary, we can observe that DT encompasses many dimensions of SD, with these dimensions intersecting each other. DT is a multi-faceted and multi-threaded phenomenon, so measuring it requires a multidimensional approach. Previous literature research has indicated that no single indicator encompasses this complex phenomenon. The multidimensional approach used in this article allowed for considering 9 areas of DT. Each area contributed theoretically to constructing the proposed measure (Fig.  1 ).

Source own elaboration

The theoretical framework for constructing the synthetic measure DTAI.

Due to the identified research gap, a literature review of the individual aspects comprising SDG 9 innovation, industry, and infrastructure was conducted. This allowed for constructing a theoretical multidimensional model preceding the construction of an empirical model.

3 Data and Method

We conducted a comparative analysis of EU 27 member states based on their level of advancement of SDG 9 using variables identified by Eurostat (Eurostat database, https://ec.europa.eu/eurostat/data/database ). Table 1 presents the variables with the names and symbols of indicators relevant to the EU and UN (United Nations).

Among the examined variables are eight stimulants (X1–X5, X7–X9) and one destimulant (X6). Stimulants are variables whose growing values positively affect the studied phenomenon. Destimulants are variables whose growing values have a negative impact (Pociecha and Zając 1989 ).

The assessment used data from the launch of the seventeen SDGs from 2015 to 2020, which was the time when the most recent comparable statistical data on the issue under consideration was available. We performed the initial evaluation of the SDG 9 accomplishment using a set of descriptive measures (mean, maximum and minimum, CV and CA) for individual indicators (variables). Individual indicators allowed us to describe the countries’ industry, innovation and infrastructure situation in detail. However, the conclusions based on this evaluation presented only a particular image of the SDG 9 implementation in the different EU member countries. That was why a multi-feature evaluation of the EU member countries was performed in the following stage of the studies, taking all indicators.

The essence of multidimensional comparative analysis (MCA) is the determination of a synthetic (aggregate) measure, which is a function of multiple variables (Grzebyk and Stec 2023 ). Among the numerous methods in this study, we selected the ZUM (Kukuła 2000 ) and Hellwig’s development pattern method (Hellwig 1968 ). Hellwig’s method for linear ordering of objects initiated the development of MCA in Polish subject literature. This method was also promoted in the world literature in 1972 through the UNESCO research project on the human resources indicators for less developed countries (Hellwig, 1972). Hellwig’s method is based on distance from the pattern object, also used in the well-known and popular TOPSIS method (Jefmański et al. 2021 ). However, MUZ consists of comparing multiple objects using selected criteria. Different quantities can express these criteria. The purpose of the ZUM is to normalize the criteria under consideration.

The MCAs find their application in empirical studies of complex phenomena. Inherently connected with the concept of complex phenomenon are the concepts of diagnostic variables X and synthetic (aggregate) variables Q . In addition, mention should be made of the output set W of describing variables, the set Y of describing variables reduced in the selection process, and the set Z of transformed (normalized) variables.

There are many requirements for diagnostic variables in the subject literature, and here are the most important ones (Kukuła 2020): (a) diagnostic variables must play a significant role in describing the phenomenon under study, (b) diagnostic variables must be available, (c) diagnostic variables, if possible, should be measured on strength scales (interval or quotient), (d) selected variables should be weakly correlated among themselves so that they do not duplicate the information carried by other variables, (e) variables from set X should be strongly correlated with variables from set Y , i.e. reduced variables.

Diagnostic variables tend to have different units and ranges of variation, making it impossible to compare them directly. Thus, wishing to bring the diagnostic features to comparability, they must transform their values. The transformation of diagnostic variables that brings their values to a state of comparability is called normalization. Among the normalization procedures, four groups of methods can be distinguished: rank, standardization, unitarization, and quotient transformation (Kukuła 2000 ).

The next step in constructing the synthetic variable is to weigh the normalized diagnostic features. The weighting of variables generates numerous discussions and disputes regarding the need to differentiate their importance and the methods used. In most empirical work in which a synthetic variable is constructed, an assumption is made about the equal weights of all selected diagnostic variables (e.g. Grzebyk and Stec 2023 ; Fura et al. 2017 ; Kukuła and Bogocz 2014 ; Skica et al. 2020 ). In any case, equal weighting does not mean "no weights" but implicitly implies equal weights (OECD 2008 ). This is predominantly the result of the lack of information about the circumstances affecting diagnostic features’ differential importance and role (Kukuła 2000 ).

The last thing to do when constructing a synthetic variable is to use the right formula to aggregate the normalized features. Aggregation formulas can be divided into “with a pattern” and “without a pattern”. In the non-pattern aggregation, there is an averaging, adding, or multiplication of diagnostic variables normalized, taking into account the weighting system. In pattern aggregation, there is an aggregate comparison of the normalized features of a given object with those of the pattern object. This comparison is made using one of the distance measures (Kukuła 2000 ).

In this paper, to build the synthetic measure, we used the ZUM, which belongs to non-pattern methods, and Hellwig’s method, which represents the group of pattern methods. The methodologies used in the methods mentioned above are presented below.

The level of a complex phenomenon is considered r objects: O 1 , O 2 ,…, O r . N diagnostic variables describe each of the objects. The gathered information about diagnostic variables form a two-dimensional matrix of the following form:

where \(x_{ij}\) represents the value of the variable \(X_{j}\) in the object \(W_{i}\) . Each object is characterized by a vector of diagnostic variables:

Diagnostic variables selected to measure complex phenomena should effectively discriminate the classified objects, so we determine their variability using the coefficient of variation at the first stage of their selection. From the set of potential diagnostic features, we remove those whose coefficient of variation is determined from the equation:

where \(v_{j}\) —coefficient of variation of the \(j\) -the variable, \(S_{j}\) —standard deviation of the \(j\) -th variable and \(\left| {\overline{x}_{j} } \right|\) —the absolute value of the mean of the \(j\) -th variable, does not exceed 10% (Sobczyk 1983 ).

Zero unitarization method (ZUM).

According to the ZUM, there is a constant reference point, which is the range of the normalized variable:

Normalization of the feature \(X_{j} ,\) which is a stimulant \((X_{j} \in S)\) is performed as follows:

where \(z_{ij} \in \left[ {0,1} \right]\) . Furthermore:

Normalization of feature \(X_{j, }\) which is a destimulant \((X_{j} \in D)\) is performed as follows:

where \(z_{ij} \in \left[ {0,1} \right]\) . Also in this case the normalized variables are from the range [0,1]. In addition:

It is worth noting that the diagnostic variables, both stimulants and destimulants, are subjected to a linear transformation according to the ZUM.

Normalized diagnostic variables form the following matrix:

Thus, the matrix ( X ) with dimensions ( \(r \times n\) ) crosses through the matrix Z with the same dimensions. Each object is described by the vector of normalized features:

To obtain an assessment characterizing a given object all normalized variables should be summed up for each object:

The assessment of the variable which characterizes the i -th object is called a synthetic variable \(Q_{i}\) :

The synthetic variable obtained through the formula ( 12 ) assumes values in the range [0,1].

The closer the measure’s value is to 1, the better the situation of the assessed object in terms of the examined phenomenon.

Hellwig’s pattern development method.

The values of the variables \(X_{j}\) are standardized in the studied set of objects according to the following formula:

\(z_{ij}\) —the normalized value of the j -th variable for the i -th object,

\(x_{ij}\) —the value of the j -th variable for the i -th object,

\(\overline{x}_{j}\) —mean of the j -th variable,

\(S_{j}\) —standard deviation of the j -th variable.

The coordinates of the development pattern are established using the following relations:

The following formula:

is used to calculate Euclidean distances of objects from the pattern, obtaining a sequence of distance values \(\left( {D_{10} , D_{20} , \ldots , D_{n0} } \right)\) .

From the above Euclidean distances, the arithmetic mean is determined according to the formula:

and the standard deviation of these distances is calculated from the formula:

Then the measure of development is defined by the formula:

The measure \(Q_{i}\) generally takes values in the interval [0, 1]. If there are objects that are significant outliers, then the normalized values of Hellwig’s aggregate measure may go beyond this interval. The closer the values of the measure \(Q_{i}\) are to unity, the closer the evaluated object is to the pattern, i.e. it ranks higher in the ranking of objects.

To check for the robustness of the DTAI, we correlated it with GII. This index was chosen because of the availability of data and the fact that it provides a comprehensive assessment of a country’s innovation performance.

According to the presented methods, the next step of the statistical analysis is classifying objects into similar groups. Classifying objects takes place as follows:

where: \(\overline{Q}_{i}\) is the mean value of the synthetic measure, \(S_{i}\) is the standard deviation of the synthetic measure.

At the final stage of the analysis to distinguish homogeneous groups, we also used one of the multivariate analysis methods, i.e., cluster analysis. It allowed the combining of multidimensional objects into groups (clusters) that meet the condition of internal homogeneity and external heterogeneity. In the cluster analysis, we used the Euclidean distance to measure the distance between objects. Ward’s technique was used to calculate the distance between clusters (Ward 1963 ). To represent the results of hierarchical grouping, we applied a binary tree called a dendrogram, which presents the agglomeration process.

Ward’s method differs from the two previously employed methods. The previously used methods belong to the group of linear ordering methods, while Ward’s method is one of the agglomerative clustering methods. We use linear ordering methods to determine the hierarchy of objects, that is, to order them from the object standing highest in the hierarchy to the object standing lowest in it. Ward’s method, on the other hand, allows us to determine the similarity of objects without establishing their hierarchy.

4 Results and Discussion

4.1 selection of variables.

The first step in selecting diagnostic variables (X1–X9) was to assess their variability (Table  2 ). For this purpose, the coefficient of variation (CV) was used.

The variability of the variables under study took on values ranging from 0.21 for variable X7 to 1.27 for variable X3 in 2015. High variability was characterized by the diagnostic variables for 2020. The lowest value of the coefficient of variation was recorded for variable X7 (0.11) and the highest for variable X3—1.16. Following the criterion of sufficient variability, no variable was removed from the diagnostic variables (Table  2 ).

In the second stage, the variables’ correlation level was assessed (Table  3 ).

A strong correlation was observed for the variables X1, X2 and X2, X3. This was valid for both 2015 and 2020 (Table  3 ). Despite the high correlation of the mentioned variables due to their substantive importance for the analyzed phenomenon, we decided not to remove them. Therefore, in the second stage of variable selection, as in the first stage, none were removed from the diagnostic variables. Thus, the adopted variable selection procedure led to the adopting variables X1–X9 as the final set of diagnostic variables.

4.2 Descriptive statistics

We present the variables and their descriptive statistics (Table  4 ). Variable X1, a stimulant, recorded Sweden’s highest value in 2015 and 2020. This indicates that in both periods, gross domestic expenditure on R&D by sector was highest among the EU 27 countries in this country. The lowest level of this variable in 2015 was observed in Cyprus, and in 2020, it was in Romania. Member states exhibited average variability in this variable, with most countries achieving values below the mean. This was valid for both 2015 and 2020. R&D personnel by sector (X2) was highest in Denmark in 2015 and 2020. These results suggest that economically, more developed countries allocate more funds to R&D than countries that cannot afford increased expenditure (Brodny and Tutak 2023 ). The lowest values of this variable were recorded in Cyprus (2015) and Romania (2020). Member states of the EU exhibited relatively average variability in this variable. The variable’s values were distributed below (2015) and above the mean (2020). Variable X3—patent applications attained the highest value in Luxembourg in 2015 and 2020, while the lowest was recorded in Romania in both years. EU countries showed a high degree of variation in the number of patents filed in 2015 and 2020. The coefficient of variation values were comparable in both time periods.

The number of patents filed in most EU countries was significantly below the mean. It is commonly believed that the number of patents is a measure of innovativeness; however, as highlighted by Higham et al. ( 2021 ), quality rather than quantity may be more critical (Higham et al. 2021 ). The highest level of variable X4—share of buses and trains in inland passenger transport was in Hungary in 2015 and 2020. This variable recorded its lowest value in Portugal in 2015 and Lithuania in 2020. The studied EU countries exhibited relatively low diversity in this variable, as indicated by a coefficient of variation of 23% in 2015 and 27% in 2020. Most EU countries recorded values of variable X4 slightly below the mean.

Share of rail and inland waterways in inland freight transport—X5 achieved its highest values in 2015 in Latvia and Lithuania in 2020. This variable recorded its lowest values in both comparable time periods in Ireland. EU countries showed high variability in terms of variable X5 in both 2015 and 2020. Most EU countries recorded values of this variable below the mean. This indicates the need for further increase in funding for digital technologies in these areas as it will significantly improve the management of bus and rail services (Corte et al. 2017 ; Graziano 2021 ). Algorithms for real-time data analyzis and optimization can be utilized to optimize routes and reduce congestion (Molinillo et al. 2019 ; Walentek 2021 ).

Unlike the other variables, the following variable had a destimulating nature, meaning that the lowest possible values were expected from the perspective of the phenomenon under study. The highest values of this variable (X6)—air emission intensity from industry was recorded in Portugal in 2015 and 2020, while the lowest was in Denmark. The diversity of EU countries was high in both comparable time periods, although it decreased in 2020 compared to 2015. Most EU countries achieved values of variable X6 below the mean in 2015 and 2020. Another variable analyzed was variable X7—tertiary educational attainment by sex. The highest value was recorded in Lithuania in 2015, and the lowest in Italy. In 2020, Luxembourg and Romania had the highest and lowest values, respectively. The values of this variable were the least differentiated among all the variables studied in both time periods. In 2015, most EU countries were below the mean, while in 2020, the values of variable X7 hovered around the mean.

In the case of variable X8, Finland, in both years, had the highest values. The lowest values of this variable were observed for Ireland (in 2015) and Hungary (in 2020). Analyzing the area of higher education, it can be assumed that digital technologies also significantly contribute to the success of SDG 9, developing human capital, stimulating R&D, influencing policy development, building potential, and facilitating cooperation. Countries with solid higher education systems like Luxembourg are better prepared to meet SDG challenges.

The last of the analyzed variables was variable X9—High-speed internet coverage, by type of area. It reached its highest value in 2015 in Latvia and its lowest in Cyprus. In 2020, the highest and lowest values were recorded for Malta and Greece, respectively. Variable X9 exhibited average variability, and most countries recorded values below the mean. This result may be surprising as highly developed countries such as Denmark, Finland, or Luxembourg have robust networks supporting high-speed internet connections and prioritize investments in digital technologies (Helms Jørgensen et al. 2019 ; Saunavaara et al. 2022 ).

4.3 Ranking of EU 27 countries

The ranking of EU countries was based on the values of DTAI calculated for the EU 27 countries for 2015–2020. DTAI values for the years 2015–2020 were obtained using the ZUM. For the extreme years 2015 and 2020, the values of the measure were also calculated using Hellwig’s method (Table  5 ).

In addition, using the correlation coefficient, the convergence of the results obtained by the two linear ordering methods was assessed (Table  5 ). This convergence, determined by the correlation coefficient, was 0.8918 for 2015 and was even higher at 0.9725 for 2020. The synthetic measure values obtained for 2015 and 2020 were also positively correlated with the GII values. The highest correlation with GII was for ZUM results 2015 (0.8263).

Based on the DTAI values calculated by the ZUM, the ranking of countries for 2015–2020 was presented (Table  6 ).

The best results in terms of achieving SDG 9 throughout the entire study period of 2015–2020 were observed in Sweden (Table  6 ). Comparing these results to a study by Kynčlová et al. ( 2020 ), it can be noted that Sweden is the leading country in rankings regarding achieving goal 9. However, in the study by Brodny and Tutak ( 2023 ), another Scandinavian country, Denmark, was the leader in the ranking, while Sweden was seventh.

The second position throughout the period 2015–2019 was noted in Denmark. In 2020, Denmark was third in the ranking. High in the ranking was another Scandinavian country, Finland (3rd position in 2015–2017, 4th position in 2018–2019 and second position in 2020). Highly developed countries such as Luxembourg, Austria, and the Netherlands occupied subsequent ranking positions during the study period. These results indicate that economically developed countries perform better in achieving SDGs. This may be because economically developed countries have significant R&D investments (Kynčlová et al. 2020 ; Pakkan et al. 2023 ), which translates into innovation, infrastructure development, and industrial growth.

Among the new member states, Slovenia ranked high. Among the countries of the former Eastern Bloc, Lithuania ranked the highest. Latvia, Slovakia, and the Czech Republic held lower-ranking positions. The lowest-ranking positions were held by the least-developed countries in the EU, Romania, and Bulgaria (Bocean and Vărzaru 2023 ). Poland, a more developed country, held positions comparable to those mentioned countries during the study period. Southern European countries such as Greece, Croatia, and Portugal performed poorly during the study period.

The most remarkable progress (in 2020 compared to 2015) in achieving goal 9 was noted in Malta (+ 9 positions) and Ireland (+ 7 positions). The most significant decrease in ranking positions (in 2020 compared to 2015) was observed in Latvia and Hungary (-4 positions). This result differs from the ranking proposed by Brodny and Tutak ( 2023 ), where Cyprus showed the most significant progress (+ 8), and the Czech Republic showed the most significant decline (-6). Bulgaria, Croatia, Greece, the Netherlands, Poland, Portugal, and Sweden did not change their ranking positions in 2020 compared to 2015. The remaining countries experienced slight changes in ranking positions (in 2020 compared to 2015).

4.4 Classification of EU 27 countries

Based on the rankings of EU countries presented in Table  6 , classifications of countries were prepared in 2015 and 2020 regarding the implementation of SDG 9. EU countries were divided into four relatively similar groups to achieve this goal using the ZUM and Hellwig’s method (Table  7 ).

Below, we present the characteristics of the distinguished groups first, according to the ZUM:

Group 1: The first group consisted of countries with a high level of achievement of goal 9 (high level). In 2015, this group comprised 6 highly developed Western countries: Sweden, Denmark, Finland, Luxembourg, Austria and the Netherlands. In 2020, the composition of group one did not differ. These results differ from Bocean and Vărzaru ( 2023 ) and the Digital Economy and Society Index (DESI, 2021) due to the inclusion of France in this group.

Group 2: The second group consisted of countries with a medium–high level of achievement of SDG 9. In 2015, this group comprised 8 countries, which decreased to 6 countries in 2020. This result is different from the ranking by Kynčlová et al. ( 2020 ), where Ireland was the leader. In 2015, both old and new member states were included in this group, while in 2020, it consisted of two new member states (Lithuania and Slovenia).

Group 3: The third group consisted of countries with a medium–low level of achievement of goal 9. This group consisted of both old and new member countries. In 2015, this group comprised 8 countries, which increased to 11 countries in 2020.

Group 4: The fourth group consisted of countries with the lowest level of achievement of goal 9 (low level). In 2015, there were 5 countries in this group, including Southern and Central-Eastern countries such as Bulgaria. This result is comparable to Kynčlová et al. ( 2020 ) and Brodny and Tutak ( 2023 ). In 2020, Malta and Bulgaria moved to the third group, and Cyprus joined the fourth group.

The classification of the EU 27 countries obtained using Hellwig’s method gave similar results. The composition of group one obtained by Hellwig’s method was similar to the classification obtained by the ZUM, with the difference that in 2015, Luxembourg was classified into group two. The second group obtained by Hellwig’s method included, apart from Luxembourg, highly economically developed countries such as Germany, France, and Belgium, as well as new member countries and Spain (for 2015). In 2020, the number of countries in group two decreased from 10 to 4. Group two in 2020 consisted of the highly developed Belgium, France, Germany and Slovenia. Apart from the island Malta, the third group obtained by the mentioned method consisted of new member countries (2015). In 2020, the composition of the third group expanded to include five more countries. The third group was joined by Ireland, Spain and Italy, among others. In group four in 2015 were 5 countries, including highly developed Ireland. In 2020, Ireland moved to the beginning of group three, and Croatia moved to the end. In 2020, Romania dropped to group four.

Figures  2 a and 2b provide classifications of the EU-27 countries according to the ZUM for 2015 and 2020, respectively. Figures  3 a and 3b show classifications of the EU 27 countries according to Hellwig’s method for 2015 and 2020, respectively.

a The classification of the EU 27 countries in 2015, according to the ZUM. b The classification of the EU 27 countries in 2020, according to the ZUM.

a The classification of the EU 27 countries in 2015, according to Hellwig’s method. b The classification of the EU 27 countries in 2020 according to Hellwig’s method.

As mentioned in the data and method section, the research was complemented by cluster analysis using Ward’s algorithm, where the Euclidean distance between their centroids and data scale normalization (bringing the values of each variable into the range [0, 1]) were used (Fig.  4 a and Fig.  4 b).

Source authors’ design using R version 4.3.0

a Cluster analysis results for 2015. b Cluster analysis results for 2020.

The results of the analysis are countries divided into three clusters, A, B, and C, contrasting the previous methods, where countries were divided into four groups. A comparison of the outcomes for 2015 and 2020 is presented in Table  8 .

Cluster A: Countries in Cluster A exhibited a high level of achievement concerning SDG 9. In 2015, this cluster comprised all countries previously classified as group 1 and some from group 2. Generally, these were highly developed Western countries, consistent with previous analyses (Table  7 ). The composition of Cluster A remained unchanged in 2020. These findings align with the results of Bocean and Vărzaru ( 2023 ) and the DESI.

Cluster B: Countries in Cluster B showed a medium–low level of achievement regarding goal 9. According to previous analyses (Table  7 ), these countries belonged to group 3. However, by 2020, all countries in Cluster B had declined to Cluster C.

Cluster C: Cluster C included countries with a low level of implementation of goal 9. In prior analyses (Table  7 ), these countries were part of groups 3 and 4. The composition of Cluster C in 2020 remained consistent, with no recorded improvements.

5 Conclusions

The implementation of SD necessitates action across social, economic, and environmental domains, with activities under the 17 SDGs requiring monitoring to assess progress. Due to its complexity, DT significantly influences SD but demands a multidimensional approach for accurate measurement. Addressing a research gap, this article presents a theoretical framework for constructing the aggregated measure to evaluate DT statistically.

The paper’s main aim was to determine the level of DT of EU 27 countries as part of the implementation of SDG 9 in the years 2015–2020. The aim was achieved by constructing the aggregated DTAI, which enables a comprehensive DT assessment. DTAI was built using two methods of linear ordering of objects: the ZUM and Hellwig’s method. Both methods yielded comparable results, as indicated by the high values of the correlation coefficient. In addition, the high positive correlation with GII indicated that the measures obtained are robust and adequately designed. Based on the DTAI values, we created rankings of countries for achieving goal 9, appointing leaders and losers in SDG 9 adoption. Sweden was the most outstanding leader in achieving SDG 9 among the 27 EU member countries throughout the entire study period of 2015–2020. At the same time, the weakest performance in achieving SDG 9 among the 27 member countries was observed in Portugal. Malta and Ireland made the most significant progress towards achieving SDG 9 in 2020 compared to 2015. The most significant decrease in ranking positions (in 2020 compared to 2015) was observed in Latvia and Hungary.

In addition to ranking countries, our research allowed us to classify countries into relatively similar groups to achieve goal 9. This classification was made using the ZUM, Hellwig’s method, and cluster analysis with Ward’s technique. The first two methods yielded relatively similar classification results. The countries with the highest level of implementation of goal 9 included the Scandinavian countries and highly developed Austria and the Netherlands (in 2015 and 2020). The countries with the lowest levels of SGD 9 implementation were mainly Romania, Greece, Malta, Cyprus and Portugal. Using cluster analysis, we obtained three groups of countries that were relatively similar in achieving goal 9. However, this method did not allow us to rank the countries, only to classify them.

Using aggregated measures and statistical analysis, the rankings and classifications of countries conducted revealed challenges in accomplishing goal 9, mainly in Central, Eastern, and Southern European countries, with Scandinavian and Western European nations leading.

Based on the presented results, theoretical and managerial implications were possibly formulated. The paper extends the domains of SD and DT theory. Innovative in the article is the proposal of a new measurement method, which, through a synthetic indicator encompassing information from multiple variables, allows for a relatively simple assessment of DT over time and space. Among the managerial implications, the proposed multi-criteria evaluations enable a comparison between EU countries using only one synthetic measure. Managers can identify best practices in specific countries and take corrective actions accordingly. Moreover, the research procedure has profound implications for the effective implementation of the 2030 Agenda by individual countries and the EU. Additionally, the proposed indicator can be used to measure the advancement of DT in non-EU countries.

From a practical perspective, the results underscore the need for EU policymakers to devise targeted strategies to address the DT gaps identified among member states. The DTAI can be a diagnostic tool for policymakers to monitor progress and identify areas needing intervention. Countries lagging in DT implementation, particularly in Central, Eastern, and Southern Europe, can benefit from tailored support programs, digital infrastructure, and education investment. It will ultimately contribute to ensuring the SD of the countries that make up the EU. Furthermore, the insights gained from this study can inform the allocation of EU funding and resources to maximize impact, ensuring that all member states can achieve the objectives of SDG 9. The practical application of this research extends to designing effective policies that promote inclusive DT, fostering innovation, and improving infrastructure across the EU.

Limitations include data availability constraints, linear methodology, and the focus on EU countries, suggesting avenues for future research, such as qualitative studies on determinants influencing DT and classification group identification. The proposed approach offers insights crucial for policymakers and decision-makers in driving SD agendas.

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This study was supported by the Ministry of Science and Higher Education (MNiSW) in Poland. The authors have no relevant financial or non-financial interests to disclose.

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Barbara Fura

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Conceptualization—Barbara Fura, Aneta Karasek, and Beata Hysa; literature review—Aneta Karasek, and Beata Hysa; methodology—Barbara Fura, Aneta Karasek, and Beata Hysa; formal analysis—Barbara Fura; writing—Barbara Fura, Aneta Karasek, and Beata Hysa; discussion and conclusions—Beata Hysa, and Aneta Karasek. The authors have read and agreed to the published version of the manuscript.

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Fura, B., Karasek, A. & Hysa, B. Statistical assessment of digital transformation in European Union countries under sustainable development goal 9. Qual Quant (2024). https://doi.org/10.1007/s11135-024-01972-0

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