Search results

The purpose of this paper is to assess online corporate communication around commitments to sustainable development goal (SDG) 12, sustainable production and consumption.

Guided by legitimacy theory, a qualitative directed content analysis was conducted on 13 companies' webpages (81 webpages, 78,947 words).

Companies broadly failed to communicate about all 11 SDG 12 targets, neglected to consistently address multiple stakeholder groups, missed opportunities to provide concrete evidence of progress and relied on a mix of substantive and symbolic legitimation strategies.

SDG 12 has been under-researched and this paper is one of the first to offer an in-depth analysis of corporate communication regarding SDG 12.

Food Loss and Waste (FLW), a result of non-sustainable consumption and production, has significant socio-environmental impacts and is addressed in the United Nation's Sustainable Development Goal (SDG) 12.3. To address current research on FLW and SDG 12.3, the authors aim to evidence the current state of knowledge on drivers and barriers to SDG 12.3 through a comprehensive literature review.

The authors employed a multi-step systematic literature review process and retrieved 171 studies addressing SDGs, with 83 explicitly addressing SDG 12.3. The analysis involved a qualitative content analysis of studies retrieved by analyzing key findings and relationships between drivers and barriers to FLW.

While academic research focuses on SDG 12.3 by stressing the necessity of FLW reduction, it fails to explain the drivers and barriers to minimizing FLW. The authors developed a conceptual framework to demonstrate how barriers and drivers can inhibit or stimulate the dynamics that will achieve SDG 12.3 through effective planning and management.

This study addressed the theoretical limitations of existing studies and clarified the critical gaps in the current literature, thereby guiding future researchers in the food supply chain (FSC) context.

The research to date focused on high-income countries, and future empirical studies should focus on consumption patterns, the associated drivers and barriers of food waste in low-income countries and its social impact.

The purpose of this study is to examine the existence of greenwashing and sustainable development goal (SDG)-washing processes by comparing ex ante (SDG Compass) and ex post (SDG Compliance) indicators and investigating whether the limitations associated with these indicators encourage companies to engage in washing processes.

The authors use a sample of 1,154 companies included in the S&P Sustainability Yearbook (formerly the RobecoSAM Yearbook). The authors test for the presence of greenwashing by comparing ex ante and ex post indicators for each SDG, whereas to test for SDG-washing, the authors compare the two ex ante and ex post approaches considering the full set of SDGs.

The results show that there is no consistency between the two types of indicators to measure the level of SDG implementation in organisations. This lack of consistency may facilitate both greenwashing and SDG-washing processes, which is due to the design and limitations of these measurement tools.

Companies may choose those indicators that paint their commitment to the SDGs in the best light, but they may also select indicators based on the SDGs they want to report on. These two options would combine greenwashing and SDG-washing.

The shift towards improved standards and regulations for measuring SDG achievement is the result of several social factors such as investor scrutiny, regulatory reform, consumer awareness and increased corporate accountability.

Few previous studies have analysed in detail the interaction between greenwashing and SDG-washing. They focus on the use of ex ante or ex post indicators separately, with samples composed of local companies, and without considering the whole set of SDGs.

This study aims to contribute to the existing literature by presenting new knowledge about sustainable development goals’ (SDGs) reporting practices through integrated reporting (IR). This paper’s ultimate goal is to dig to light companies’ main approaches to incorporating SDG disclosures into IRs.

This study puts forward both deductive content analysis and an inductive thematic analysis on a sample of worldwide leading IR adopters to assess what SDGs they disclose and how they integrate SDGs into the reports. Meaningful narratives and graphical illustrations are selected, categorised and discussed from a symbolic/substantive legitimacy perspective.

The results of this study highlighted that although a fair number of leading IR adopters addressed SDG issues, their pathways to disclosure were not uniform. In some cases, SDGs inspired substantive changes to internal management and process, communicated through an integrated approach. However, there was a persistent trend of using SDGs as camouflage and symbolic tool to enhance company’s reputation and obtain a licence to operate.

To the best of the authors’ knowledge, this was the first study that performed a deductive/inductive thematic analysis to engender insight into the most meaningful patterns followed by leading IR reporters worldwide to disclose their contributions to SDGs and address their legitimacy.

Digital technology and SDGs have gained increasing interest from the research community. This chapter aims to explore the field through a holistic review of 188 publications from 2017 to 2022. For the systematic review of 188 articles, a three-step methodology comprising of PRISMA guidelines was performed, bibliometric analysis and text analysis using VOS-Viewer and Sentiment Analysis using RStudio had been undertaken. Bibliographic coupling revealed the following clusters Digital Space (Over all SDG), Localising SDGs, Financial Systems and Growth (SDG 8), Sustainable Supply Chain (SDG 9), Education (SDG 4), Energy Management (SDG 7), Smart Cities (SDG 11 and 13), Gender, Skills, and Responsibility (SDG 5 and 12), Food Management (SDG 1, 2 and 3), Business Innovation (SDG 8 and 9) and ICT (SDG 9). Next, co-occurrence analysis highlighted the following clusters Circular Economy (SDG 8), Higher Education System (SDG 4), Digital health (SDG 3), Industry 4.0 (SDG 9) and Supply Chain Management (SDG 9). Next, text analysis traced the most relevant areas of work within the theme. Finally, sentiment analysis revealed positive sentiments of the field. The research concluded that only a few SDGs had found major focus while the others don't have any solid ground in the literature. This chapter presents a knowledge structure by mapping the most relevant SDGs in the context of digital technology and sets directions for future research.

Higher education plays an essential role in achieving the United Nations sustainable development goals (SDGs). However, there are only scattered studies on monitoring how universities promote SDGs through their curriculum. The purpose of this study is to investigate the connection of existing common core courses in a university to SDG education. In particular, this study wanted to know how common core courses can be classified by machine-learning approach according to SDGs.

In this report, the authors used machine learning techniques to tag the 166 common core courses in a university with SDGs and then analyzed the results based on visualizations. The training data set comes from the OSDG public community data set which the community had verified. Meanwhile, key descriptions of common core courses had been used for the classification. The study used the multinomial logistic regression algorithm for the classification. Descriptive analysis at course-level, theme-level and curriculum-level had been included to illustrate the proposed approach’s functions.

The results indicate that the machine-learning classification approach can significantly accelerate the SDG classification of courses. However, currently, it cannot replace human classification due to the complexity of the problem and the lack of relevant training data.

The study can achieve a more accurate model training through adopting advanced machine learning algorithms (e.g. deep learning, multioutput multiclass machine learning algorithms); developing a more effective test data set by extracting more relevant information from syllabus and learning materials; expanding the training data set of SDGs that currently have insufficient records (e.g. SDG 12); and replacing the existing training data set from OSDG by authentic education-related documents (such as course syllabus) with SDG classifications. The performance of the algorithm should also be compared to other computer-based and human-based SDG classification approaches for cross-checking the results, with a systematic evaluation framework. Furthermore, the study can be analyzed by circulating results to students and understanding how they would interpret and use the results for choosing courses for studying. Furthermore, the study mainly focused on the classification of topics that are taught in courses but cannot measure the effectiveness of adopted pedagogies, assessment strategies and competency development strategies in courses. The study can also conduct analysis based on assessment tasks and rubrics of courses to see whether the assessment tasks can help students understand and take action on SDGs.

The proposed approach explores the possibility of using machine learning for SDG classifications in scale.