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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.

The authors aim to develop a conceptual framework for longitudinal estimation of stress-related states in the wild (IW), based on the machine learning (ML) algorithms that use physiological and non-physiological bio-sensor data.

The authors propose a conceptual framework for longitudinal estimation of stress-related states consisting of four blocks: (1) identification; (2) validation; (3) measurement and (4) visualization. The authors implement each step of the proposed conceptual framework, using the example of Gaussian mixture model (GMM) and K-means algorithm. These ML algorithms are trained on the data of 18 workers from the public administration sector who wore biometric devices for about two months.

The authors confirm the convergent validity of a proposed conceptual framework IW. Empirical data analysis suggests that two-cluster models achieve five-fold cross-validation accuracy exceeding 70% in identifying stress. Coefficient of accuracy decreases for three-cluster models achieving around 45%. The authors conclude that identification models may serve to derive longitudinal stress-related measures.

Proposed conceptual framework may guide researchers in creating validated stress-related indicators. At the same time, physiological sensing of stress through identification models is limited because of subject-specific reactions to stressors.

Longitudinal indicators on stress allow estimation of long-term impact coming from external environment on stress-related states. Such stress-related indicators can become an integral part of mobile/web/computer applications supporting stress management programs.

Timely identification of excessive stress may improve individual well-being and prevent development stress-related diseases.

The study develops a novel conceptual framework for longitudinal estimation of stress-related states using physiological and non-physiological bio-sensor data, given that scientific knowledge on validated longitudinal indicators of stress is in emergent state.

As the world faces a new health crisis threatening people with the spread of Covid-19, this study aims to summarize the key information of Covid-19 related to disease characteristics, diagnosis, treatment and prevention along with the lessons learned from Thailand.

The narrative review was synthesized from various sources such as the World Health Organization; Centers for Disease Control and Prevention; Ministry of Public Health and other related news; articles in ScienceDirect, PubMed, Google Scholar; and the author's perspective regarding the lessons learned from Thailand with keywords of “Covid-19” and “Coronavirus” from January to August 2020. Google Trends was used to set common questions.

Covid-19 is the seventh family of coronaviruses that cause various symptoms related to respiratory systems. The disease can be treated through general and symptomatic treatment, by using antiviral drugs. As of July 2020, there are four potential vaccine candidates ChAdOx1 nCoV-19, mRNA-1273, Ad5-nCOV and BNT162b1. The recommendations for Covid-19 prevention are physical distancing, face masks, eye protection and hand washing. Thailand is now considered as low-risk for Covid-19 possibly because of (1) soft policy by government actions, (2) village health volunteers, (3) integration of technology and (4) fact-based communications.

This study summarized the key points about Covid-19, clarified some misunderstandings and shared strategic actions from Thailand, which can be adapted according to the different capacities and situations in other countries.

The purpose of this paper is to better understand how and why adoption and implementation of healthcare IT innovations occur. The authors examine two IT applications, computerised physician order entry (CPOE) and picture archiving and communication systems (PACS) at the meso and micro levels, within the context of the National Programme for IT in the English National Health Service (NHS).

To analyse these multi-level dynamics, the authors blend Rogers’ diffusion of innovations theory (DoIT) with Webster’s sociological critique of technological innovation in medicine and healthcare systems to illuminate a wider range of interacting factors. Qualitative data collected between 2004 and 2006 uses semi-structured, in-depth interviews with 72 stakeholders across four English NHS hospital trusts.

Overall, PACS was more successfully implemented (fully or partially in three out of four trusts) than CPOE (implemented in one trust only). Factors such as perceived benefit to users and attributes of the application – in particular speed, ease of use, reliability and flexibility and levels of readiness – were highly relevant but their influence was modulated through interaction with complex structural and relational issues.

Results reveal that combining contextual system level theories with DoIT increases understanding of real-life processes underpinning implementation of IT innovations within healthcare. They also highlight important drivers affecting success of implementation, including socio-political factors, the social body of practice and degree of “co-construction” between designers and end-users.

The originality of the study partly rests on its methodological innovativeness and its value on critical insights afforded into understanding complex IT implementation programmes.