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1 – 5 of 5Patrick T.I. Lam and Kelvin S.H. Mok
This study aims to identify the challenges facing innovative startups in the construction environment, recommending possible self-help measures and society support.
Abstract
Purpose
This study aims to identify the challenges facing innovative startups in the construction environment, recommending possible self-help measures and society support.
Design/methodology/approach
A comprehensive literature survey informed a questionnaire survey on built environment startups in Hong Kong, followed by a statistical analysis and supplemented by written views of respondents. Validation by experts confirms the survey results.
Findings
Triangulated findings highlight the problems of conservative policies, investors’ preference on short payback periods, price competition, high operation cost and a lack of promotion channels. The firm’s size and its age differentiate its networking and fund-raising capabilities.
Research limitations/implications
While the survey samples cover the spread of startups in Hong Kong’s construction/real estate industries well, the number is still limited because the city is relatively compact. The barriers and solutions may be particularly relevant to the built environment there, but also worth noting elsewhere.
Practical implications
Built environment startups are emerging and their path of development is obscured by industry barriers. While the findings reflect the current situation in Hong Kong, which is a metropolitan city with a vibrant construction market, government policies may present a varying factor in different economies. Conservatism in the construction industry may also be a hindrance, but gradual signs of improvements are seen.
Originality/value
The recommendations provided may help mitigate the problems of startup growth. They also provide insights into the construction “startup eco-system” worth the attention of policy makers and project managers, who may make better use of the innovative technology and services of built environment startups if the difficulties are alleviated.
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Bochra Nourhene Saguem, Marwa Gharmoul, Amel Braham, Selma Ben Nasr, Sang Qin and Patrick Corrigan
This study aims to examine the reliability and validity of the Arabic version of the attribution questionnaire (AQ).
Abstract
Purpose
This study aims to examine the reliability and validity of the Arabic version of the attribution questionnaire (AQ).
Design/methodology/approach
The AQ is designed to assess attitudes, affects and behavioral intentions related to a hypothetical person diagnosed with schizophrenia. The original English version was translated into Literary Arabic. A total of 310 students registered in different universities, with medical and paramedical establishments excluded completed the Arabic version of AQ. Reliability was tested using Cronbach’s alpha coefficients. Structural equation modeling was used to test hypothesized paths. Correlations among exogenous (e.g. responsibility) and endogenous (e.g. help) variables in the path were examined. Fit indicators were then examined for equations that were identified.
Findings
The results revealed that the Arabic version of AQ showed acceptable psychometric properties in the assessment of stigma in the Tunisian population. All factors of this Arabic version showed Cronbach’s alpha values equal to or greater than 0.72. Structural equation models for the responsibility and dangerousness models were mostly supported. The Arabic version of AQ is valid and reliable for the assessment of stigma in Tunisian and Arabic-speaking populations.
Practical implications
The Arabic version of AQ may be used to promote research on stigma toward people with mental illness in larger and more representative Tunisian and Arabic-speaking populations, which will help to further address the complex and multifaceted phenomenon of stigma toward people with mental illness.
Originality/value
This is the first validated stigma measure in the Tunisian socio-cultural context.
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Russell Woodfield, Katie Dhingra, Daniel Boduszek and Agata Debowska
The purpose of this paper is to investigate the moderating role of psychopathy facets on the relationship between traumatic exposure and posttraumatic stress disorder (PTSD…
Abstract
Purpose
The purpose of this paper is to investigate the moderating role of psychopathy facets on the relationship between traumatic exposure and posttraumatic stress disorder (PTSD) symptomology.
Design/methodology/approach
Participants were male prisoners incarcerated in the UK.
Findings
The analysis revealed differential associations between the two facets of psychopathy, with potentially traumatic events and symptoms of PTSD. Specifically, neither primary psychopathy nor trauma exposure were significantly related to PTSD, while secondary psychopathy was positively and significantly related with PTSD symptoms. Furthermore, the effect of trauma exposure on PTSD was found to depend on the level of secondary psychopathy. More specifically, trauma exposure was strongly and positively associated with PTSD symptoms for low levels of secondary psychopathy and negatively associated with PTSD symptomology for individuals with high levels of secondary psychopathy.
Originality/value
The findings clarify linkages among psychopathy facets, trauma, and PTSD, and extend the understanding of the presentation of PTSD in male prisoners.
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Lois James, Stephen James and Ian Hesketh
To evaluate the effectiveness of a fatigue-management training and sleep health promotion intervention in a sample of officers from UK Home Office Police Forces.
Abstract
Purpose
To evaluate the effectiveness of a fatigue-management training and sleep health promotion intervention in a sample of officers from UK Home Office Police Forces.
Design/methodology/approach
Using a pre- and post-design we exposed 50 officers from selected UK police forces to a fatigue-management training intervention. Pre- and post-intervention data collection included wrist actigraphy, a physiological and objective measure of sleep quantity and quality, as well as surveys including the Pittsburg Sleep Quality Index (PSQI), the World Health Organization Quality of Life (WHOQOL) instrument, the Epworth Sleepiness Scale (ESS), the Perceived Stress Scale (PSS) and the PTSD Checklist (PCL-5).
Findings
We found the training significantly increased sleep quantity by 25 min per 24-h period, from 6.9 h to 7.3 h (f = 9.2; df = 519; p = 0.003), and improved sleep quality scores from 84% before the intervention, to 87% after the training (f = 10.6; df = 519; p = 0.001).
Research limitations/implications
Continued research is necessary to guide nationwide implementation of fatigue-management and sleep health promotion programs.
Practical implications
Our findings show that a fatigue-management training resulted in a significant and meaningful increase in sleep among police officers.
Originality/value
This is the first piece of research to emerge from a full population survey (response rate 16.6%) of the UK police service exploring issues of sleep and fatigue.
Magesh S., Niveditha V.R., Rajakumar P.S., Radha RamMohan S. and Natrayan L.
The current and on-going coronavirus (COVID-19) has disrupted many human lives all over the world and seems very difficult to confront this global crisis as the infection is…
Abstract
Purpose
The current and on-going coronavirus (COVID-19) has disrupted many human lives all over the world and seems very difficult to confront this global crisis as the infection is transmitted by physical contact. As no vaccine or medical treatment made available till date, the only solution is to detect the COVID-19 cases, block the transmission, isolate the infected and protect the susceptible population. In this scenario, the pervasive computing becomes essential, as it is environment-centric and data acquisition via smart devices provides better way for analysing diseases with various parameters.
Design/methodology/approach
For data collection, Infrared Thermometer, Hikvision’s Thermographic Camera and Acoustic device are deployed. Data-imputation is carried out by principal component analysis. A mathematical model susceptible, infected and recovered (SIR) is implemented for classifying COVID-19 cases. The recurrent neural network (RNN) with long-term short memory is enacted to predict the COVID-19 disease.
Findings
Machine learning models are very efficient in predicting diseases. In the proposed research work, besides contribution of smart devices, Artificial Intelligence detector is deployed to reduce false alarms. A mathematical model SIR is integrated with machine learning techniques for better classification. Implementation of RNN with Long Short Term Memory (LSTM) model furnishes better prediction holding the previous history.
Originality/value
The proposed research collected COVID −19 data using three types of sensors for temperature sensing and detecting the respiratory rate. After pre-processing, 300 instances are taken for experimental results considering the demographic features: Sex, Patient Age, Temperature, Finding and Clinical Trials. Classification is performed using SIR mode and finally predicted 188 confirmed cases using RNN with LSTM model.
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