A considerable amount of literature has been published on Corporate Reputation, Branding and Brand Image. These studies are extensive and focus particularly on questionnaires and statistical analysis. Although extensive research has been carried out, no single study was found which attempted to predict corporate reputation performance based on data collected from media sources. To perform this task, a biLSTM Neural Network extended with attention mechanism was utilized. The advantages of this architecture are that it obtains excellent performance for NLP tasks. The state-of-the-art designed model achieves highly competitive results, F1 scores around 72%, accuracy of 92% and loss around 20%.
When corporate social responsibility (CSR) as a sensemaking process is assessed from a corporate governance perspective, this implies that stakeholders do not only influence companies by promoting and enforcing regulations and other corporate guidelines. They also influence companies by promoting regulation on influence pathways, by demanding that companies develop formal mechanisms that allow companies and stakeholders to discuss and in some cases agree on changes to principles and policies. This perspective suggests that regulation is an outcome of power relations and is, as such, a reflection of certain mental models. As such, mental models reveal the political bias in corporate governance perspectives. For this reason, CSR research needs to be clear about the underlying assumptions about corporate governance, and corporate governance research needs to disclose which mental models of CSR influence the outcomes. Taking a governance perspective on the development of mental models of CSR helps to understand the interaction between CSR and processes of sensemaking at the institutional, organizational and individual levels.
Current symptom detection methods for energy diagnosis in heating, ventilation and air conditioning (HVAC) systems are not standardised and not consistent with HVAC process and instrumentation diagrams (P&IDs) as used by engineers to design and operate these systems, leading to a very limited application of energy performance diagnosis systems in practice. This paper proposes detection methods to overcome these issues, based on the 4S3F (four types of symptom and three types of faults) framework. A set of generic symptoms divided into three categories (balance, energy performance and operational state symptoms) is discussed and related performance indicators are developed, using efficiencies, seasonal performance factors, capacities, and control and design-based operational indicators. The symptom detection method was applied successfully to the HVAC system of the building of The Hague University of Applied Sciences. Detection results on an annual, monthly and daily basis are discussed and compared. Link to the formail publication via its DOI https://doi.org/10.1016/j.autcon.2020.103344
