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Ambient-assisted living (AAL) is one solution to the challenges of healthcare systems in an aging population. Using the “ecosystem adoption of practices over time” (EAPT) as a theoretical lens, this study explores and describes three elements of AAL adoption: (1) the AAL practices in which the technology is embedded (i.e. object of adoption), (2) the older adult's adoption ecosystem (i.e. subject of adoption) and (3) the change of adoption practices over time (i.e. temporality of adoption).

Qualitative interviews with three actor groups in the ecosystem: clients, relatives and home nurses.

The study identifies six categories of AAL practices. Clients, relatives and nurses interact and integrate their resources in carrying out these practices. Some of the practices have developed, or are expected to develop, over time.

The study applies a novel theoretical perspective on how AAL technology is embedded in practices performed by different actors in the adoption ecosystem. This broadens the conceptualization of what is being adopted compared to traditional adoption research.

There are three purposes in this paper: to verify the importance of bi-directional fluid-structure interaction algorithm for centrifugal impeller designs; to study the relationship between the flow inside the impeller and the vibration of the blade; study the influence of material properties on flow field and vibration of centrifugal blades.

First, a bi-directional fluid-structure coupling finite element numerical model of the supersonic semi-open centrifugal impeller is established based on the Workbench platform. Then, the calculation results of impeller polytropic efficiency and stage total pressure ratio are compared with the experimental results from the available literature. Finally, the flow field and vibrational characteristics of 17-4PH (PHB), aluminum alloy (AAL) and carbon fiber-reinforced plastic (CFP) blades are compared under different operating conditions.

The results show that the flow fields performance and blade vibration influence each other. The flow fields performance and vibration resistance of CFP blades are higher than those of 17-4PH (PHB) and aluminum alloy (AAL) blades. At the design speed, compared with the PHB blades and AAL blades, the CFP blades deformation is reduced by 34.5% and 9%, the stress is reduced by 69.6% and 20% and the impeller pressure ratio is increased by 0.8% and 0.14%, respectively.

The importance of fluid-structure interaction to the aerodynamic and structural design of centrifugal impeller is revealed, and the superiority over composite materials in the application of centrifugal impeller is verified.

Indoor air quality monitoring is extremely important in urban, industrial areas. Considering the devastating effect of declining quality of air in major part of the countries like India and China, it is highly recommended to monitor the quality of air which can help people with respiratory diseases, children and elderly people to take necessary precautions and stay safe at their homes. The purpose of this study is to detect air quality and perform predictions which could be part of smart home automation with the use of newer technology.

This study proposes an Internet-of-Things (IoT)-based air quality measurement, warning and prediction system for ambient assisted living. The proposed ambient assisted living system consists of low-cost air quality sensors and ESP32 controller with new generation embedded system architecture. It can detect Indoor Air Quality parameters like CO, PM2.5, NO2, O3, NH3, temperature, pressure, humidity, etc. The low cost sensor data are calibrated using machine learning techniques for performance improvement. The system has a novel prediction model, multiheaded convolutional neural networks-gated recurrent unit which can detect next hour pollution concentration. The model uses a transfer learning (TL) approach for prediction when the system is new and less data available for prediction. Any neighboring site data can be used to transfer knowledge for early predictions for the new system. It can have a mobile-based application which can send warning notifications to users if the Indoor Air Quality parameters exceed the specified threshold values. This is all required to take necessary measures against bad air quality.

The IoT-based system has implemented the TL framework, and the results of this study showed that the system works efficiently with performance improvement of 55.42% in RMSE scores for prediction at new target system with insufficient data.

This study demonstrates the implementation of an IoT system which uses low-cost sensors and deep learning model for predicting pollution concentration. The system is tackling the issues of the low-cost sensors for better performance. The novel approach of pretrained models and TL work very well at the new system having data insufficiency issues. This study contributes significantly with the usage of low-cost sensors, open-source advanced technology and performance improvement in prediction ability at new systems. Experimental results and findings are disclosed in this study. This will help install multiple new cost-effective monitoring stations in smart city for pollution forecasting.

This paper investigates how various strategies for combining forecasts, both simple and optimised approaches, are compared with popular individual risk models in estimating value-at-risk (VaR) and expected shortfall (ES) in emerging market at alternative risk levels.

Using the case study of the Vietnamese stock market, the author produced one-day-ahead VaR and ES forecast from seven individual risk models and ten alternative forecast combinations. Next, the author employed a battery of backtesting procedures and alternative loss functions to evaluate the global predictive accuracy of the different methods. Finally, the author investigated the relative performance over time of VaR and ES forecasts using fluctuation test.

The empirical results indicate that, although combined forecasts have reasonable predictive abilities, they are often outperformed by one individual risk model. Furthermore, the author showed that the complex combining methods with optimised weighting functions do not perform better than simple combining methods. The fluctuation test suggests that the poor performance of combined forecasts is mainly due to their inability to cope with periods of instability.

This study reveals the limitation of combining strategies in the one-day-ahead VaR and ES forecasts in emerging markets. A possible direction for further research is to investigate whether this finding holds for multi-day ahead forecasts. Moreover, the inferior performance of combined forecasts during periods of instability motivates further research on the combining strategies that take into account for potential structure breaks in the performance of individual risk models. A potential approach is to improve the individual risk models with macroeconomic variables using a mixed-data sampling approach.

First, the authors contribute to the literature on the forecasting combinations for VaR and ES measures. Second, the author explored a wide range of alternative risk models to forecast both VaR and ES with recent data including periods of the COVID-19 pandemic. Although forecast combination strategies have been providing several good results in several fields, the literature of forecast combination in the VaR and ES context is surprisingly limited, especially for emerging market returns. To the best of the author’s knowledge, this is the first study investigating predictive power of combining methods for VaR and ES in an emerging market.

This study aims to examine the effects of the culture for innovation, work engagement and the use of interactive performance measurement systems (PMSs) in the interorganizational accounting processes automation at a shared services center (SSC).

A single-entity survey was carried out at an SSC provider of B2B accounting services of a multinational company in the electrical sector, and structural equation modeling was applied for data analysis.

The results show that the culture for innovation, the work engagement and the use of interactive PMS positively and significantly influence the accounting process automation in the investigated SSC. Moreover, the use of interactive PMS presents a mediating effect on the relationship between culture for innovation and work engagement.

The research findings contribute by revealing that the culture of innovation, work engagement and the interactive PMS support the use of robotic process automation and artificial intelligence in the interorganizational automation of accounting processes in an SSC-type service ecosystem. This highlights the importance of the culture of innovation and the positive feeling toward work being reinforced by the organization and the role of the interactive PMS as a formal instrument to transmit the organizational objectives and provide a common vision.

The study reveals that the use of interactive PMS seems to be an important conductor of the behavior of employees toward the accounting process automation strategy at the SSC, reinforced, in this case, by the culture of innovation and work engagement.

This study, adopting a knowledge perspective, aims to explore how small- and medium-sized enterprises (SMEs) engage in service innovation by implementing design thinking.

A qualitative research methodology and a multiple case study strategy were adopted on account of the exploratory nature of this study. Three cases based in Scotland have been selected. Data were collected from staff, managers and researchers who participated in Create Cultures of Innovation (CCoI), a project sponsored by the Scottish government.

The research shows that SMEs can be enabled, through design-led interventions, to think collaboratively and leverage knowledge to achieve service innovation.

This research contributes to the service innovation literature by theorising an integrated research framework of “Knowledge exchange in SMEs service innovation with design thinking”. Further, this study enriches the extant understanding of service innovation in the traditional sector (manufacturing and hospitality) and redefines the roles and relations of the dimensions of service innovation.

This study addresses the call on tools and methods for servitisation, suggesting that changing the culture and mindsets of both the top management and the staff are critical for the success of servitisation and the implementation of design thinking. In addition, this research suggests the need to embed design thinking within the leadership prior to rolling it out to the wider public. Support from the top management should focus on both the engagement of staff and the changing of vision from product to service-focused.

This paper gets together service innovation, design thinking and knowledge exchange in the context of manufacturing SMEs offering novel insights into how to succeed in servitisation with the implementation of design thinking.

This study aims to investigate whether the extent of corporate disclosure, proxied by COVID-19-related disclosure, affects the dividend policy of listed firms.

The study uses a multinomial logistic regression model to examine the relation between corporate disclosure and the dividend policy of the 100 largest market-cap firms in Vietnam in 2021. The COVID-19 pandemic, with its unique impact on business operations, serves as the backdrop for this analysis.

The findings indicate that firms with more extensive COVID-19-related disclosure are more inclined to distribute dividends in the form of stocks or cash instead of omitting them.

This research contributes to the understanding of how corporate disclosure practices influence a firm’s financial decisions, particularly in the context of the COVID-19 pandemic. The findings hold implications for corporate financial decision-making during times of macroeconomic shock.

This study aims to assess the effect of material internal control weaknesses (MICW) on the relationship between ownership structures and future-oriented disclosure.

A total number of 197 firms were assessed in this study during 2014–2021. Two measures were used for MICW. First, the number of existing MICW was assessed in independent auditors’ reports. In Iran, the maximum number of weaknesses is 13. Second, the scoring (0 or 1) method was used as a dummy variable, 1 for a firm with MICW and otherwise 0. Moreover, the scoring (0 or 1) method was used to measure the level of future-oriented disclosure of 13 indicators.

The findings showed that institutional ownership and managerial ownership have a significant positive effect on future-oriented disclosure, whereas the MICW have a significant negative effect on future-oriented disclosure. In addition, MICW played a moderator role in the relationship between ownership structures and future-oriented disclosure. The robustness checks confirmed the results.

As the studies conducted on future-oriented disclosure and the contributing factors are limited, and also the effect of MICW on future-oriented disclosure is not explored, the present findings can show the importance of the study, and fill the gap in this field. This study offers theoretical and practical implications to drive policymakers and managers to the effectiveness of internal control and future-oriented transparency.