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Literature around the careers of female academics is targeted mainly toward identifying and examining career progression inhibitors, while the drivers appear largely unexplored. This paper aims to contribute to contemporary knowledge by identifying drivers to the career progression of female academics in Australia. With COVID-19 currently impacting the careers of female academics this knowledge can assist universities and human resource (HR) professionals in developing policies and practices to better facilitate female academic career progression.

Empirically this paper draws on a qualitative study of 18 male and 29 female academics, as well as nine senior university stakeholders. The authors employed semi-structured interviews and a novel methodology, Draw, Write, Reflect.

In line with attribution theory, senior stakeholders mainly identified organisational efforts, including leadership, gender equity endeavours, recruitment and promotion approaches, as well as a construct known as “relative to opportunity considerations”, as drivers of female academics’ career progression. Female academics, however, largely attributed their career progression to personal factors, such as family support, informal mentoring, and determination and persistence.

The findings have implications for universities and HR practices seeking to facilitate female academic career progression. Implementation of the drivers identified may enhance female academics’ abilities to progress their careers.

By focussing on the drivers of, rather than the barriers to, female academic careers, the research is novel in its identification of a previously unexplored mismatch between organisational attribution and individual attribution of career progression drivers thereby advancing knowledge of gender differences in academic careers.

This research aims to demonstrate the possible link between the sociodemographic profile of visitors motivated by the visit to flamenco shows and the city of Córdoba (Spain), and the preferences and sensations regarding these experiences.

The methodology used (multilayer perceptron) is based on the development of an artificial neural network.

The results show that the variables age and educational level are determining factors in the profile of the visitor. Also, as the level of income increases, so does the interest in flamenco, a fact that can be useful to determine the target audience for this type of shows.

Flamenco is an art that originated in the Andalusian region that arouses the interest of the visitor due to its music, way of singing and dance. Flamenco is a popular art that excites and awakens the senses of those who attend this dance, song and guitar show. Its recognition as Intangible Heritage of Humanity by United Nations Educational, Scientific and Cultural Organization (UNESCO) since 2010, makes it a tourist product that motivates visitors to travel to the city of Córdoba (Spain), being also one of flamenco's places of origin. Córdoba has this art in its tourist offer so that the identity of the city has two aspects: patrimonial and immaterial, among the flamenco highlighted.

From a technological determinist perspective, machine learning (ML) may significantly contribute towards sustainable development. The purpose of this study is to synthesize prior literature on the role of ML in promoting sustainability and to encourage future inquiries.

This study conducts a systematic review of 110 papers that demonstrate the utilization of ML in the context of sustainable development.

ML techniques may play a vital role in enabling sustainable development by leveraging data to uncover patterns and facilitate the prediction of various variables, thereby aiding in decision-making processes. Through the synthesis of findings from prior research, it is evident that ML may help in achieving many of the United Nations’ sustainable development goals.

This study represents one of the initial investigations that conducted a comprehensive examination of the literature concerning ML’s contribution to sustainability. The analysis revealed that the research domain is still in its early stages, indicating a need for further exploration.