This historical review uncovers the institutionalisation and diffusion of the SWOTanalysis by assessing academic literature, seminar materials, proprietary research reports and interviews with experts from the virus theory perspective. We suggest that reviews of the SWOT analysis using the management fashion theory perspective are inadequate in explaining the diffusion and rejection of ideas born in practice. The virus theory perspective starts at an organizational level and reveals that predominantly practitioners were instrumental in spreading the ideas like participatory planning and distinguishing between short term and long range planning in order to resolve the planning paradox in provisional planning. Due to mutations in practice by consulting firms, the 2x2 matrix of SWOT became a cognitive artefact on its own. Theoretical roots of the original SWOT analysis stem from psychology and behavioural sciences.It is questionable if current strategy textbooks reflect these theoretical backgrounds.
Cognitive impairment is a leading cause of dysfunction in the elderly. When mild cognitive impairment (MCI) occurs in elderly, it is frequently a prodromal condition to dementia. The Montreal Cognitive Assessment (MoCA) is a commonly used tool to screen for MCI. However, this test requires a face-to-face administration and is composed of an assortment of questions whose responses are added together by the rater to provide a score whose precise meaning has been controversial. This study was designed to evaluate the performance of a computerized memory test (MemTrax), which is an adaptation of a continuous recognition task, with respect to the MoCA. Two outcome measures are generated from the MemTrax test: MemTraxspeed and MemTraxcorrect. Subjects were administered the MoCA and the MemTrax test. Based on the results of the MoCA, subjects were divided in two groups of cognitive status: normal cognition (n = 45) and MCI (n = 37). Mean MemTrax scores were significantly lower in the MCI than in the normal cognition group. All MemTrax outcome variables were positively associated with the MoCA. Two methods, computing the average MTX score and linear regression were used to estimate the cutoff values of the MemTrax test to detect MCI. These methods showed that for the outcome MemTraxspeed a score below the range of 0.87 – 91 s−1 is an indication of MCI, and for the outcome MemTraxcorrect a score below the range of 85 – 90% is an indication for MCI.
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This article describes the relation between mental health and academic performance during the start of college and how AI-enhanced chatbot interventions could prevent both study problems and mental health problems.
Receiving the first “Rijbewijs” is always an exciting moment for any teenager, but, this also comes with considerable risks. In the Netherlands, the fatality rate of young novice drivers is five times higher than that of drivers between the ages of 30 and 59 years. These risks are mainly because of age-related factors and lack of experience which manifests in inadequate higher-order skills required for hazard perception and successful interventions to react to risks on the road. Although risk assessment and driving attitude is included in the drivers’ training and examination process, the accident statistics show that it only has limited influence on the development factors such as attitudes, motivations, lifestyles, self-assessment and risk acceptance that play a significant role in post-licensing driving. This negatively impacts traffic safety. “How could novice drivers receive critical feedback on their driving behaviour and traffic safety? ” is, therefore, an important question. Due to major advancements in domains such as ICT, sensors, big data, and Artificial Intelligence (AI), in-vehicle data is being extensively used for monitoring driver behaviour, driving style identification and driver modelling. However, use of such techniques in pre-license driver training and assessment has not been extensively explored. EIDETIC aims at developing a novel approach by fusing multiple data sources such as in-vehicle sensors/data (to trace the vehicle trajectory), eye-tracking glasses (to monitor viewing behaviour) and cameras (to monitor the surroundings) for providing quantifiable and understandable feedback to novice drivers. Furthermore, this new knowledge could also support driving instructors and examiners in ensuring safe drivers. This project will also generate necessary knowledge that would serve as a foundation for facilitating the transition to the training and assessment for drivers of automated vehicles.
