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This research aims to examine and understand the rationales and modalities behind the use of disclosure before, during and after a corporate governance scandal involving CPA Australia (CPAA).

Data beyond CPAA's annual reports were collected, such as news articles, media releases, an independent review panel (IRP) report, and the Chief Operating Officer's letter to members. These disclosures were manually coded and analysed through the word counts and word trees in NVivo. This study also relied on Norbert Elias' conceptual tool of power games among networks of actors – figurations – to model the scandal as a power game between the old Board, the press, concerned members, the IRP and the new Board. This study analysed the data to reveal a collective and in fieri power balance that changed with the phases of the scandal.

A mix of voluntary, involuntary, requested and absent disclosures was important in triggering, managing and ending the CPAA scandal. Moreover, communication and disclosure fulfilled a constitutive role since both: mobilised actors, enabled coordination among actors, contributed to pursuing shared goals and influenced power balances. Such a constitutive role was at the heart of the ability of coalitions of figurations to challenge and restore the powerful status quo.

This research introduces to accounting studies the collective and in fieri dimensions of power from figurational theory. Moreover, the research sheds new light on using voluntary, involuntary, requested and absent disclosures before, during and after a corporate crisis.

In the field of heritage science, especially applied to buildings and artefacts made by organic hygroscopic materials, analyzing the microclimate has always been of extreme importance. In particular, in many cases, the knowledge of the outdoor/indoor microclimate may support the decision process in conservation and preservation matters of historic buildings. This knowledge is often gained by implementing long and time-consuming monitoring campaigns that allow collecting atmospheric and climatic data.

Sometimes the collected time series may be corrupted, incomplete and/or subjected to the sensors' errors because of the remoteness of the historic building location, the natural aging of the sensor or the lack of a continuous check of the data downloading process. For this reason, in this work, an innovative approach about reconstructing the indoor microclimate into heritage buildings, just knowing the outdoor one, is proposed. This methodology is based on using machine learning tools known as variational auto encoders (VAEs), that are able to reconstruct time series and/or to fill data gaps.

The proposed approach is implemented using data collected in Ringebu Stave Church, a Norwegian medieval wooden heritage building. Reconstructing a realistic time series, for the vast majority of the year period, of the natural internal climate of the Church has been successfully implemented.

The novelty of this work is discussed in the framework of the existing literature. The work explores the potentials of machine learning tools compared to traditional ones, providing a method that is able to reliably fill missing data in time series.

Climate-induced damage is a pressing problem for the preservation of cultural properties. Their physical deterioration is often the cumulative effect of different environmental hazards of variable intensity. Among these, fluctuations of temperature and relative humidity may cause nonrecoverable physical changes in building envelopes and artifacts made of hygroscopic materials, such as wood. Microclimatic fluctuations may be caused by several factors, including the presence of many visitors within the historical building. Within this framework, the current work is focused on detecting events taking place in two Norwegian stave churches, by identifying the fluctuations in temperature and relative humidity caused by the presence of people attending the public events.

The identification of such fluctuations and, so, of the presence of people within the churches has been carried out through three different methods. The first is an unsupervised clustering algorithm here termed “density peak,” the second is a supervised deep learning model based on a standard convolutional neural network (CNN) and the third is a novel ad hoc engineering feature approach “unexpected mixing ratio (UMR) peak.”

While the first two methods may have some instabilities (in terms of precision, recall and normal mutual information [NMI]), the last one shows a promising performance in the detection of microclimatic fluctuations induced by the presence of visitors.

The novelty of this work stands in using both well-established and in-house ad hoc machine learning algorithms in the field of heritage science, proving that these smart approaches could be of extreme usefulness and could lead to quick data analyses, if used properly.

This study aims to explore public coping strategies with government-imposed lockdown restrictions (i.e. forced compliance) due to a health crisis (i.e. COVID-19). This directly impacts the public's power, as they may feel alienated from their environment and from others. Consequently, this study explores the relationships between the public's power, quality of life and crisis-coping strategies. This is important to help governments understand public discourse surrounding perceived government health crisis communication, which aids effective policy development.

An online questionnaire distributed via Qualtrics received 371 responses from the South African public and structural equation modelling was used to test the hypotheses.

The results indicate the public's experience of powerlessness and resulting information-sharing, negative word-of-mouth and support-seeking as crisis coping strategies in response to government-imposed lockdown restrictions.

The public's perspective on health crisis communication used in this study sheds light on adaptive and maladaptive coping strategies that the public employs due to the alienation they feel during a health crisis with government-forced compliance. The findings add to the sparse research on crisis communication from the public perspective in a developing country context and provide insights for governments in developing health crisis communication strategies. The results give insight into developing policies related to community engagement and citizen participation during a pandemic.