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This paper aims to introduce International Digital Collaborative Autoethnographical Psychobiography (IDCAP).

This paper describes how IDCAP was developed to answer research questions about what it takes and what it means to recover from mental illness. During its development, IDCAP combined the diverse and intersectional experiences, knowledge and interests of an Anglo-Swedish research team with what could be found in different publications concerning the experiences and the mental illnesses of the musicians Syd Barrett, Peter Green and Brian Wilson.

IDCAP combines features of autoethnography and psychobiography to offer a novel qualitative research method.

Whilst IDCAP was created to focus on recovery from mental illness and musicians, it can be applied to other areas of research. It shares the same limitations as autoethnography and psychobiography, although some of the features of IDCAP may go some way to mitigate against these.

IDCAP is a novel research method that is offered to other researchers to develop and enhance further through application.

IDCAP is a collaborative research method that encourages the involvement of a wide range of researchers from different countries and cultures. It can be used to give voice to marginalised groups and to counter discrimination and prejudice. Recovery from mental illness is a topic of great personal and social value.

IDCAP is a novel research method that, to the best of the authors’ knowledge, has not been explicitly used before.

This paper explores organization-level racialized work strategies for maintaining racialized organizations (Ray, 2019). It focuses on intentional actions to maintain dominant racial norms, demonstrating how work strategies are informed by dominant racial structures that maintain racial inequities.

We compiled a chronological case study (Yin, 2012) based on 168 news media articles and various organizational documents to examine responses to athlete protests at the University of Texas at Austin following the death of George Floyd. Gioia et al.’s (2013) method uncovered how dominant racial norms inform organizational behaviors.

The paper challenges institutional theory neutrality and identifies several racialized work strategies that organizations employ to maintain racialized norms and practices. The findings provide a framework for organizations to interrogate their strategies and their role in reproducing dominant racial norms and inequities.

In 2020, the Black Lives Matter (BLM) movement was reinvigorated within sporting and corporate domains. However, many organizations engaged in performativity, sparking criticism about meaningful change in organizational contexts. Our case study examines how one organization responded to athlete activists’ BLM-fueled demands, revealing specific racialized work strategies that maintain structures of racism. As organizations worldwide disrupt and discuss oppressive structures such as racism, we demonstrate how organizational leadership, while aware of policies and practices of racism, may choose not to act and actively maintain such structures.

This article aims to investigate the status of and the trends in the intertwining of crowdfunding and innovation literature by identifying, evaluating and synthesizing the findings from previous research. This paper provides a bibliometric meta-analysis of the already substantial and growing literature on innovation and crowdfunding research.

Using a bibliometric approach, this research scrutinizes all articles that include terms related to “crowdfunding” and “innovation” (in their title, abstract or keywords) in Elsevier’s Scopus database. VosViewer and Bibliometrix package in R have been used to analyse 150 articles.

The results suggest that there are three main research clusters in the innovation and crowdfunding literature. The first cluster highlights the role of crowdfunding in fostering radical and incremental innovation. The second cluster focuses on the concept of openness and its effect on innovation in crowdfunding campaigns, while the third cluster explains the role of platforms’ innovation in crowdfunding success.

Taking a holistic perspective, this contribution advances new knowledge on the intertwining of crowdfunding and innovation research fields. It implies that crowdfunding is facilitating the flow of knowledge between different stakeholders, including project initiators and crowd investors, among others, as they all benefit from open innovation platforms.

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.