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Power reserves such as money, information, support, knowledge, and time are essential in handling conflicts. These power reserves are, as a rule, limited in that they are insufficient in handling existing multi‐conflicting relationships. This paper presents an episodic model for the rational planning of power reserves that can aid in effective power use. How much and to what extent this power should be used depends on the following: (1) determining the significance of the various activity fields (i.e., arenas) in which conflict arises; (2) calculating the decision maker's conflict intensity with the other party(ies) in the attainment of each goal; (3) identifying the facilitating or inhibitory factors in power use; and (4) estimating the likely consequences of using power to resolve any conflict.

The aim of this study is to critically examine the transparency and reporting practice of Environmental, Social, and Governance (ESG) controversies within the pharmaceutical and textile industry. Based on the four core dimensions of transparency, we explore which reporting medium is most frequently chosen for the disclosure of negative ESG contributions, the nature and information content of the disclosed incidents and how voluntary adherence to sustainability reporting standards and independent assurances affect the reporting.

We use conceptual content analysis and employ a counter-accounting approach to analyse the disclosure of 190 ESG controversies in 104 corporate reports from the pharmaceutical and textile industries, covering a three-year period from 2018–2020.

The very large majority of controversies are reported only once in the legal proceedings section of the annual report, but not again in the sustainability report, where it would be necessary to provide a balanced picture. Moreover, companies tend to disclose only those controversies that are either associated with high media attention or are expected to be related to litigation, resulting in 26 per cent of controversies not being disclosed at all. The overall quality of disclosure is unsatisfactory and in need of improvement, but comparably higher in the pharmaceutical industry than in the textile industry. Interestingly, neither the application of sustainability reporting standards nor independent assurance seems to positively impact the disclosure behaviour.

Our paper provides new insights into the shortcomings of current ESG controversy disclosures by revealing patterns of selective reporting practices and the strategic framing of issues. In addition, it contributes to the debates on corporate cherry-picking in the adoption of sustainability reporting guidelines and on the effectiveness of external assurance of sustainability reports. Based on the findings, it offers important implications for practitioners, in particular management, policy makers, rating agencies and assurance providers.

The paper aims to provide empirical evidence on whether the identification of the teacher as a role model is truly a key factor in improving students' entrepreneurial competencies and intention.

Data were obtained from a survey of 387 university students enrolled in an entrepreneurship course, who were asked to respond to the same questionnaire at the beginning and at the end of the semester. To test the proposed hypotheses, comparison of means tests for independent samples were carried out.

The results obtained confirm that students with a role model teacher perceive that entrepreneurial competencies and perceived behavioural control increase largely during the course, whilst students with a non-role model teacher perceive that their attitude towards intention decrease.

The “Who should teach?” question has been disregarded in previous entrepreneurship education (EE) literature. The current paper is a first step towards a better comprehension of the teacher of entrepreneurship as a role model.

This study aims to estimate the overall SARS-CoV-2 seroprevalence and evaluate the accuracy of an antibody rapid test compared to a reference serological assay during a COVID-19 outbreak in a prison complex housing over 13,000 prisoners in Brasília.

The authors obtained a randomized, stratified representative sample of each prison unit and conducted a repeated serosurvey among prisoners between June and July 2020, using a lateral-flow immunochromatographic assay (LFIA). Samples were also retested using a chemiluminescence enzyme immunoassay (CLIA) to compare SARS-CoV-2 seroprevalence and 21-days incidence, as well as to estimate the overall infection fatality rate (IFR) and determine the diagnostic accuracy of the LFIA test.

This study identified 485 eligible individuals and enrolled 460 participants. Baseline and 21-days follow-up seroprevalence were estimated at 52.0% (95% CI 44.9–59.0) and 56.7% (95% CI 48.2–65.3) with LFIA; and 80.7% (95% CI 74.1–87.3) and 81.1% (95% CI 74.4–87.8) with CLIA, with an overall IFR of 0.02%. There were 78.2% (95% CI 66.7–89.7) symptomatic individuals among the positive cases. Sensitivity and specificity of LFIA were estimated at 43.4% and 83.3% for IgM; 46.5% and 91.5% for IgG; and 59.1% and 77.3% for combined tests.

The authors found high seroprevalence of anti-SARS-CoV-2 antibodies within the prison complex. The occurrence of asymptomatic infection highlights the importance of periodic mass testing in addition to case-finding of symptomatic individuals; however, the field performance of LFIA tests should be validated. This study recommends that vaccination strategies consider the inclusion of prisoners and prison staff in priority groups.

Aiming at the shortcomings of EEG signals generated by brain's sensorimotor region activated tasks, such as poor performance, low efficiency and weak robustness, this paper proposes an EEG signals classification method based on multi-dimensional fusion features.

First, the improved Morlet wavelet is used to extract the spectrum feature maps from EEG signals. Then, the spatial-frequency features are extracted from the PSD maps by using the three-dimensional convolutional neural networks (3DCNNs) model. Finally, the spatial-frequency features are incorporated to the bidirectional gated recurrent units (Bi-GRUs) models to extract the spatial-frequency-sequential multi-dimensional fusion features for recognition of brain's sensorimotor region activated task.

In the comparative experiments, the data sets of motor imagery (MI)/action observation (AO)/action execution (AE) tasks are selected to test the classification performance and robustness of the proposed algorithm. In addition, the impact of extracted features on the sensorimotor region and the impact on the classification processing are also analyzed by visualization during experiments.

The experimental results show that the proposed algorithm extracts the corresponding brain activation features for different action related tasks, so as to achieve more stable classification performance in dealing with AO/MI/AE tasks, and has the best robustness on EEG signals of different subjects.

The aim of this paper is to identify the business barriers that influence cost of implementing unmanned aerial system (UAS) and its suitability for a decentralised system. UAS, or drone, plays a role of data provider to architectural, engineering and construction professionals within a decentralised system. However, the expectations in the execution and test of the effectiveness of the UAS is often not met. The reasons for these fails are not well elaborated in the literature. Hence, the study investigates the barriers and cost analysis of UAS that can be used for a decentralised case, in which the UAS data are useful for multiple stakeholders, and provide illustration of the interactions within this approach.

This paper is part of a longitudinal project by using a qualitative method of interviewing 24 participants involved in the process of application of drones in the country of the Dominican Republic. The open-ended semi-structured interviews were composed for questions regarding the application of UAS, barriers and business implications. The data gathered were transcribed and used thematic analysis for its interpretation. Later, conclusions of the barriers of UAS implementation in the organisation were analysed and a cost model was developed to identify a viable scenario.

The paper provides empirical insights about the barriers and economic considerations faced in the implementation process of UAS. In this research, 16 barriers in the implementation process at the management level, 8 types of cases of business relationships and 13 business models were identified. Furthermore, recommendations were made about being the accountability of the dimensions and recurrent visits to the projects handled by the portfolio of the organisations.

Blockchain system is supported by UAS data and its tests require skills and resources that were outside of the scope of the main research intend regarding UAS implementation in construction. Furthermore, as these technologies are still under development, the assessment of the decentralised system, smart contract and swarm technology was addressed conceptually and further research are encouraged in this field.

The paper includes barriers to consider before implementation, business implications, project examples and cost structure developed. Furthermore, the findings are fit theoretically into the context of a decentralised system. It was understood and contemplated that monitoring in open and outdoor spaces is the suitable approach for UAS implementations for decentralised system. The trend of decentralised autonomous organisations for transparency and efficiency of human tasks provides the foundations of human–robot interactions as well as the role of tokenisation of assets into the cyberspace. Therefore, the paper brings managers and technicians the implications for the future-proofing the implementation of UAS.

This paper provides an overview of the implications of cost and the suitable scenarios for return of investment in the UAS implementation in the current stage of the technology development. In addition, the paper makes reference to decentralised systems, smart contracts and swarm technology as options in which reality capture technologies are essential for construction projects.